JPWO2019162496A5 - - Google Patents

Description

The present application relates to a calibration method for calibrating a camera for detecting analytes in samples. The invention further relates to a detection method for detecting an analyte in a sample and a computer program with program means for carrying out a calibration method or a detection method. Further, the present invention provides a calibration system for calibrating a camera for the purpose of detecting an analyte in a sample by using the camera, for detecting the analyte in the sample by using at least one test element. and systems for detecting an analyte in a sample. Methods, computer programs and systems according to the present invention can be used in medical diagnostics to qualitatively or quantitatively detect one or more analytes in one or more bodily fluids. Other fields of application of the invention are also possible.

In many cases, medical diagnostics require the detection of one or more analytes in a sample of bodily fluid, such as blood, interstitial fluid, urine, saliva, or other type of bodily fluid. Examples of analytes to be detected are glucose, triglycerides, lactate, cholesterol or other types of analytes typically present in these bodily fluids. Generally, devices and methods known to those skilled in the art for detecting an analyte involve performing one or more detectable detection reactions, such as optically detectable reactions, in the presence of the analyte to be detected. Use a test element containing one or more test chemicals that can be For these test chemicals see, for example, J. Phys. Hoenes et al. : The Technology Behind Glucose Meters: Test Strips, Diabetes Technology & Therapeutics, Volume 10, Supplement 1, 2008, S-10 to S-26. Depending on the concentration and/or presence of the analyte, appropriate treatment is selected if necessary.

Typically, one or more optically detectable changes in the test chemical are monitored, thus deriving from these changes the concentration of at least one analyte to be detected. Various types of detectors are known in the art for detecting changes in at least one optical property of the test field. Thus, different types of light sources as well as different types of detectors are known for illuminating the test field. In addition to using custom detectors developed specifically for the purpose of optically detecting changes in test chemicals contained in their corresponding test elements, recent developments have led to widely available devices such as smartphones. I'm aiming to use

As an example, Li Shen et al. ：「Ｐｏｉｎｔ－ｏｆ－ｃａｒｅ ｃｏｌｏｒｉｍｅｔｒｉｃ ｄｅｔｅｃｔｉｏｎ ｗｉｔｈ ａ ｓｍａｒｔｐｈｏｎｅ」，Ｌａｂ Ｃｈｉｐ，２０１２，１２，４２４０～４２４３，「Ｔｈｅ Ｒｏｙａｌ Ｓｏｃｉｅｔｙ ｏｆ Ｃｈｅｍｉｓｔｒｙ」および対応するＥｌｅｃｔｒｏｎｉｃ Ｓｕｐｐｌｅｍｅｎｔａｒｙ Ｍａｔｅｒｉａｌ（ＥＳＩ） ｆｏｒ Ｌａｂ ｏｎ ａ Ｃｈｉｐ，「Ｔｈｅ Ｒｏｙａｌ Society of Chemistry” proposes using smart phones to perform colorimetric detection. The color of the colorimetric diagnostic assay is quantified using a smart phone that allows for highly accurate measurements within a wide range of ambient conditions. Instead of directly using the red, green and blue (RGB) intensities of color images taken by smartphones, the chromaticity values are used to construct a calibration curve of analyte concentrations. Furthermore, to make this approach adaptable under different lighting conditions, a calibration technique was developed to compensate for measurement errors due to ambient light variability.

In general, when a smart phone or other consumer electronics product with a camera is used for the purpose of evaluating analytical measurements, individual calibration of the camera is required. Thus, Ali K. Yetisen et al. : "A smartphone algorithm with inter-phone repeatability for the analysis of colorimetric tests", Sensors and Actuators B 196 (2014) 156-160 describes a method in which a user inputs information into a smartphone and performs various calibrations using the camera of the smartphone. We propose a smartphone algorithm with interphone reproducibility for the analysis of colorimetric tests that requires calibration inputs by capturing images of points. Calibration must be performed under predefined conditions. A colorimetric measurement can then be performed using exactly the same conditions as when performing the calibration. The app calculates final measurements by comparing target data values against the calibration curve. This is achieved by an interpolation algorithm analogous to the nearest neighbor problem in computational geometry.

Another calibration approach is proposed in WO2014/037820A2, which discloses systems and methods for colorimetric test strip analysis and disease control. The system can include an accessory operably coupled to a mobile device, where the mobile acquires and/or analyzes images of the colorimetric test strip. The light box attachment can be removably attached to the mobile device or adapted to remain attached to the mobile device, in which case the light is removed from the camera's field of view for general photography purposes. It is possible to remove box accessories. In other embodiments, images containing known calibration colors and reagent areas are obtained without a lightbox for comparison with previous calibration images for purposes of modeling changes in ambient lighting conditions and determining color correction functions. be done. A correction may be applied to the detected reagent area color for matching between the detected reagent area color and the reference color on the reference chart.

Despite the advantages involved in calibrating and using cameras, such as those included in consumer electronics, for the purpose of detecting analytes in samples, several technical challenges remain. . Generally, the color representation within a camera system is adapted, for example by internal post-processing of the raw data captured by the camera, to provide an image that is optimized in terms of human color perception. However, such post-processing due to human color perception may not be ideal when aiming to accurately determine analyte concentration in a sample. In addition, non-linear factors such as the individual technical and optical properties of the vast number of cameras on the market or different lighting conditions may influence the determination of analyte concentration and therefore need to be taken into account.

Therefore, for calibrating a camera for detecting an analyte in a sample, for example, a mobile device, such as a consumer electronics product mobile device, in particular a smart phone or a tablet computer, dedicated to analytical measurements. It would be desirable to provide methods, computer programs and systems that address the above technical challenges for calibrating cameras included in non-general purpose mobile devices. Specifically, a method, computer program and system shall be proposed which are widely applicable to available cameras and suitable for increasing measurement accuracy and user convenience.

This problem is addressed by a method, computer program and system with the features of the independent claims. Advantageous embodiments, which can be realized singly or in any combination, are recited in the dependent claims.

As used below, "have", "comprise or include" or any grammatical variations thereof are used non-exclusively. These terms therefore cover both the situation in which there are no further characteristics within the entities described in this context, other than the characteristics introduced by these terms, and the situation in which one or more further characteristics are present. can mean As an example, the expressions "A has B" and "A comprises B and A includes B" both mean that there are no other elements in A besides B. non-existent (i.e., A consists exclusively of B), and B plus one or more further elements, such as element C, elements C and D, and even another element, is entity A It can mean the situation that exists within.

Furthermore, the phrase "at least one," "one or more," or similar phrases indicating that a feature or element can be present one or more times is typically used when introducing each feature or element. It should be pointed out that it is used only once. In the following, in most cases when referring to each feature or element, the expressions "at least one" or "one or more" mean that the respective feature or element can be present one or more times. Despite the fact, it is not repeated.

In addition, the terms "preferably", "more preferably", "particularly", "more particularly", "specifically", "more specifically" or similar terms used below Terms are used in conjunction with any feature without restricting alternative possibilities. As such, the features introduced by these terms are optional features and are not intended to limit the scope of the claims in any way. The present invention may also be implemented using alternative features, as will be recognized by those skilled in the art. Similarly, the features introduced by "in an embodiment of the invention" or similar language are not limitations with respect to alternative embodiments of the invention, without any limitation as to the scope of the invention, and without any limitation as to the scope of the invention. It is intended to be an optional feature, without any limitation as to the possible combination of features introduced with optional or non-optional features.

In a first aspect of the invention, a calibration method is disclosed for calibrating a camera for the purpose of detecting an analyte in a sample. The method specifically includes the following steps, which can be performed in a given order. Moreover, different orders are possible as well. Moreover, it is possible to perform two or more method steps wholly or partially concurrently. Furthermore, it is possible to perform one or more or even all method steps once or repeatedly. The method may include additional method steps not listed herein. Generally, calibration methods include the following steps:
a. providing a set of color coordinate systems, the set of color coordinate systems including a plurality of different color coordinate systems configured to describe the color of an object;
b. providing a test sample set having a known concentration of the analyte;
c. applying a test sample to a set of test elements, each test element comprising at least one test chemical configured to undergo an optically detectable detection reaction with an analyte; having four test fields, thereby creating at least one colored test field for each of the test samples;
d. acquiring an image of the colored test field by using a camera;
e. generating color coordinates for the image of the colored test field by using the color coordinate system of the color coordinate system set, thereby creating a color coordinate set for the test sample and the color coordinate system; ;
f. providing a coding function set, the coding function set comprising a plurality of coding functions for converting color coordinates of the test field to corresponding concentrations of analytes in the sample;
g. By using the coding function set, step e. converting the set of color coordinates generated in to a set of measured densities;
h. Comparing the known densities of the test samples of the test sample set with the measured density set, and the best match of the color coordinate system set where the measured density set best matches the known densities the best match of the color coordinate system and the coding function set the coding function the step of determining

The term "calibration method for calibrating a camera for detecting an analyte in a sample" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art. and should not be limited to any special or customized meaning. The term can specifically, without limitation, refer to the process of determining the relationship between information provided by a camera and the presence or absence or concentration of one or more specific analytes in a sample. Typically, therefore, the camera produces information, eg electronic information, one or more images, eg one or more images of at least one test field, as described in more detail below. Generally, there is some relationship between the information, or part of it, and the presence or absence or concentration of the analyte in the sample, such as when a camera takes one or more images of a test field wetted by the sample. obtain. The process of determining this relationship can be called a calibration method. As a result of the calibration method, this relationship may be expressed in at least one equation, such as at least one equation defining the relationship, at least one table, such as at least one lookup table, or at least one calibration curve, for example, for defining the relationship. It can be defined by defining one or more of a graph.

The term "camera" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should not be limited to a specific or customized meaning. do not have. The term specifically refers, without limitation, to a device configured for recording spatially resolved optical data, such as one or more images. A camera may specifically include one or more camera chips that are imaging devices such as one or more CCD and/or CMOS chips. A camera may generally include a one- or two-dimensional array of image sensors, such as pixels. As an example, the camera may include at least 10 pixels in at least one dimension, such as at least 10 pixels in each dimension. However, it should be pointed out that other cameras are possible as well. The invention is intended to be particularly applicable to cameras such as those commonly used in mobile applications such as laptops, tablets or in particular mobile phones, eg smart phones. Thus, in particular, the camera may be part of a mobile device that includes at least one camera plus at least one data processing device, such as one or more data processors. However, other cameras are possible. A camera may include, in addition to at least one camera chip or imaging chip, further elements, such as one or more optical elements, such as one or more lenses. As an example, the camera may be a fixed focus camera having at least one lens that is fixedly adjusted with respect to the camera. Alternatively, however, the camera can include one or more variable lenses that can be adjusted automatically or manually.

As used herein, the term "detecting an analyte in a sample," often referred to as "analytical measurement," is a broad term whose usual and customary meaning to those skilled in the art is should be given and should not be limited to any special or customized meaning. The term may specifically, without limitation, refer to the qualitative and/or quantitative determination of at least one analyte in a sample. As an example, the result of an analytical measurement can be the concentration and/or presence or absence of the analyte to be determined.

The term "image" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should not be limited to a specific or customized meaning. do not have. The term may specifically, without limitation, refer to data or information recorded by using a camera, such as multiple electronic readings from pixels of a camera chip. Thus, by way of example, an image may contain a one-dimensional or two-dimensional array of data. Thus, the image itself may contain pixels, for example pixels of the image that are correlated with pixels of the camera chip. Consequently, when we refer to a "pixel" we mean either the unit of image information produced by a single pixel of the camera chip, or directly a single pixel of the camera chip.

The camera may specifically be a color camera. Thus, for each pixel, for example, color information, such as color values for the three colors R, G, B, may be provided or generated. A higher number of color values, such as four colors for each pixel, is possible as well. Color cameras are generally known to those skilled in the art. Thus, by way of example, each pixel of a camera chip may have three or more different color sensors, such as one pixel for red (R), one pixel for yellow (G) and one pixel for blue (B). It can have color recording pixels and the like. For each pixel, such as for R, G, B, a value may be recorded by the pixel, such as a digital value in the range of 0-255 depending on the intensity of the respective color. Instead of using a 3-color set such as R, G, B, a 4-color set such as C, M, Y, K can be used as an example. The color sensitivities of the pixels can be produced by color filters or by appropriate intrinsic sensitivities of the sensor elements used within the camera pixels. These techniques are generally known to those skilled in the art.

The term "analyte" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should be limited to specific or customized meanings. is not. The term may specifically, without limitation, refer to one or more specific compounds and/or other parameters to be detected and/or measured. As an example, at least one analyte can be a compound that participates in metabolism, such as one or more of glucose, cholesterol or triglycerides. Additionally or alternatively, other types of analytes or parameters such as pH values may also be determined.

The term "sample" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should not be limited to a specific or customized meaning. do not have. The term may specifically refer, without limitation, to a quantity of any material to be analyzed, such as one or more of a liquid, solid or gaseous material. More specifically, the term can refer to a volume of bodily fluid, such as one or more of blood, interstitial fluid, urine, saliva, and the like. Additionally or alternatively, however, other types of samples, such as water, may also be used.

The term "color coordinate system" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should be limited to a special or customized meaning. not a thing The term specifically means, without limitation, an arbitrary coordinate system that can characterize, for example, mathematically or physically the colors of an object, the colors of a test field or the colors of an image recorded by a camera. can. Various color coordinate systems are generally known to those skilled in the art, such as the CIE defined color coordinate system. Various examples are given below. Color coordinates as a whole can cover or define a color space, for example by defining three or four basis vectors.

These color coordinate systems can be stored, for example, in a data storage device or database, and/or can be expressed by a general formula for a coordinate system having one or more parameters to provide color coordinate systems. A color coordinate system can be defined. The color coordinate system can be provided automatically or by human action. The color coordinate system can be an input for the calibration method provided by the user of the calibration method.

The term "test sample" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should be limited to specific or customized meanings. is not. The term may specifically, without limitation, refer to a sample having a determined or determinable quantity or concentration of an analyte therein.

Further, as used herein, the term "set" generally refers to a defined quantity of identical or similar objects or elements each having known or determinable properties. means. Thus, in general each set may contain two or more, more preferably three or more objects or elements. A set may contain a finite number of objects or elements or an infinite number of objects or elements.

Thus, by way of example, a test sample set can generally include a plurality of at least two defined or determinable quantities of samples. As an example, a test sample set includes at least a first quantity of test samples having a first known or determinable concentration of an analyte and a second known or determinable concentration of an analyte therein. A second quantity of test sample can be included, where the second concentration is different than the first concentration. Optionally, a third, fourth, etc. quantity of test samples having a third, fourth, etc. concentration of the analyte contained therein may be included in the test sample set. Thus, by way of example, a test sample set may include three or more different concentrations of analyte in the test samples. As an example, test sample sets may be provided in vials or other containers. Concentrations can be known, for example, by predetermining these concentrations using certified or reliable laboratory analyses, and/or by preparing test samples with known amounts of the constituents. Alternatively, concentrations can be known later in time by post-processing the test sample with subsequent analysis, for example using certified or reliable laboratory analytical methods.

Providing the test samples may include preparing the test samples or other means of providing these test samples, such as purchasing these test samples from a suitable supplier. Other means of providing the test sample are possible.

The method may further include providing test elements, wherein each test element contains at least one test chemical configured to undergo an optically detectable detection reaction with the analyte. has at least one test field containing

The term "test element" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should be limited to specialized or customized meanings. is not. The term may specifically, without limitation, refer to any element or device configured for detecting an analyte in a sample, specifically in the sense of the definitions given above. As an example, a test element can comprise at least one substrate, such as at least one carrier, with at least one test field applied or incorporated. By way of example, at least one carrier may be strip-shaped, thus the test element being a test strip. These test strips are generally widely used and available. A test strip can carry a single test field or multiple test fields with the same or different test chemicals therein. Known in the art are a plurality of test elements comprising at least one test chemical, also called reagent, which produces a colored reaction in the presence of at least one analyte to be detected. Some basic principles for test elements and reagents that can likewise be used within the scope of the present invention are described, for example, in J. Am. Hones et al. : Diabetes Technology and Therapeutics, Vol. 10, Supplement 1, 2008, pp. 10-26.

Further, the term "test field" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should be limited to specific or customized meanings. not a thing The term can specifically refer, without limitation, to a consistent amount of test chemical, e.g., a field, e.g., a round, polygonal or rectangular shaped field with one or more layers of material, Here at least one layer of the test field has a test chemical contained therein. Others that provide specific optical properties such as reflective properties, provide spreading properties for spreading the sample, or provide separation properties for separating particulate components of the sample, e.g., cellular components. Layers may also be present.

The term "test chemical" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should be limited to specific or customized meanings. not a thing The term can specifically, without limitation, refer to a compound or compounds, such as a mixture of compounds, that are suitable for conducting a detection reaction in the presence of an analyte, wherein the detection reaction is It is detectable by specific means, eg optically. Detection reactions can be specifically analyte-specific. In this case, the test chemical can in particular be an optical test chemical, eg a color-changing test chemical that changes color in the presence of the analyte. The color change may specifically depend on the amount of analyte present in the sample. As an example, a test chemical can include at least one enzyme, such as glucose oxidase and/or glucose dehydrogenase. Additionally, other ingredients such as one or more dyes, mediators, etc. may be present. Test chemicals are generally known to those skilled in the art and are also described in J. Am. Hones et al. : Diabetes Technology and Therapeutics, Vol. 10, Supplement 1, 2008, pp. 10-26 references may be indicated. However, other test chemicals are possible as well.

A test element set may specifically include a plurality of at least three test elements, for example at least three test elements of the same setup. Here, a test element setup can be defined by a specific architecture that includes a defined substrate, a defined geometry, and a defined top of a test field containing one or more defined test chemicals. Specifically, the test elements of the test element set can be selected from the same production lot, thus providing the highest possible degree of identity of the test elements.

Method step c includes applying the test sample to the set of test elements. Thus, as an example, a droplet of test sample can be applied to the test field, or the test field can be wetted with the test sample by other means, such as by dipping the test element into the test sample. can. This produces, by way of example, a set of test elements wetted with the test sample applied to the test field, wherein by way of example, at least three test elements are provided with a different test sample applied to each test field. exist. As an example, a first test element or field can be provided with a first test sample applied and a second test element or field with a second test sample applied. and a third test element or third test field having a third test sample applied thereto may be provided. Further test samples can be applied to further test elements or test fields. Thereby, the test element set may comprise a plurality of test fields of the same type after application of the test sample, wherein at least three test fields having different colors after different test samples have been applied and thus detection reactions have taken place. A test field is provided. Thus, in general the term "colored test field" can specifically mean a test field in which a detection reaction takes place after application of a sample or test sample, where the color of the test field is determined by the result of the detection reaction. be.

Further, as used herein, the term "obtaining an image of the colored test field by using a camera" used in step d generally refers to the above definition by using a camera. means the process of recording at least one image according to Here, in particular, at least one image can be acquired from each of the colored test fields generated in step c. Nonetheless, it is also possible to exclude one or more test fields. Generally, step d. at least one image of at least one first test field with at least one first test sample applied; at least one second test field with at least one second test sample applied; at least one image, and preferably at least one further image of at least one further test field, e.g. at least one third test field to which at least one further test sample, such as at least one third test sample, has been applied An image set can be created that includes at least one image of .

step e. generates color coordinates for the image of the colored test field by using the color coordinate system of the color coordinate system set, thereby creating a color coordinate set for the test sample and color coordinate system. Thus, by way of example, for each test field wetted with a test sample and thus formed into a colored test field, color coordinates according to the color coordinate system of the color coordinate system set can be generated. Specifically, for each of the images of the colored test fields, or for each of the at least one image of each colored test field, color coordinates each describing the coloration of the respective colored test field can be generated. As an example, color coordinates ( _Fi,j , _mi,j , bi _,j ), ( _Ai,j , _Bi,j , Ci _,j ) or ( _Ai,j , _Bi,j , C _i,j , D _i,j ) may be generated, where i represents the color coordinate system identity of the color coordinate system set and j represents the colored test field identity and/or the test sample identity. Here, each of the color coordinate systems of the color coordinate system set can be used, or a subset of the color coordinate system set containing the selected color coordinate system can be used. Additionally, each of the images or a subset of the images can be analyzed.

Further, the color coordinate sets for the test samples and for the color coordinate system are, by way of example, color coordinates (F, m, b) for each colored test field or test sample within each color coordinate system of the color coordinate system set. thus resulting in at least p q color coordinates, where p is the number of colored test fields or test samples and q is the number of color coordinates in the color coordinate system set, where: It is also possible to take multiple images of a single colored test field, resulting in an increase in the number p. Other options are possible as well.

step f. provides a coding function set that includes a plurality of coding functions for converting color coordinates of a test field to corresponding concentrations of analytes in the sample.

The term "coding function" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should be limited to special or customized meanings. is not. This term is specifically, but not exclusively, for converting the color coordinates of a test field, specifically a colored test field moistened with the sample to be analyzed, into the corresponding concentration of the analyte in the sample. It can imply any transformation algorithm. For this purpose, the coding function is, by way of example, one or more analytical functions, e.g. It can contain a function that converts to density. Additionally or alternatively, the coding function may include one or more matrix algorithms or operations for converting a vector of color coordinates into concentrations of at least one analyte. Again, additionally or alternatively, the coding function may also be one or more curves, e.g., one or more one-dimensional, two-dimensional, three-dimensional Or it can include a 4-dimensional curve, or the like. Additionally or alternatively, the coding function may also include one or more lookup tables or other tables for assigning analyte concentrations to respective values or value ranges of color coordinates. .

A coding function set may be defined, in particular by providing a similar function with one or more parameters to be defined, as described in more detail below. Specifically, therefore, a coding function set may be defined by one or more parameters that may be determined to define a particular coding function of the coding function set. Examples are shown below.

step g. in step e. The set of color coordinates generated in is transformed into a set of measured densities by using a set of coding functions. As used herein, the term "measured density" is generally based on one or more items of experimental data, such as one or more measurements, in this case specifically one or more color coordinates. can refer to the experimental result representing the concentration in the sample. Therefore step g. in step f. The coding functions of the coding function set provided in step e. applied to the color coordinates generated by Here, the coding function is step e. may be applied to all of the color coordinates generated in step e. may be applied to only a subset of these color coordinates generated by Additionally, step f. All the coding functions or only a subset of the coding functions of the set of coding functions provided in can be applied to the color coordinates. Thus, as an example, if the coding function is defined by one or more parameters, only a subset of the possible coding functions of the coding function set, such as by subdividing the parameter range into discrete steps, e.g. A subset defined by one or more of the parameter ranges for one or more or even all and/or by a finite number of values for at least one parameter can be applied. Generally, step g. In , a set of measured densities is generated. Thus, by way of example, generating at least one measured concentration for a plurality of test samples having different concentrations of an analyte, e.g., for each coding function of a coding function set or each coding function of a coding function subset. can. As an example, if n images of colored test fields are evaluated and/or the corresponding test fields are moistened to generate their color coordinates, thus resulting in n color coordinates or n color coordinate vectors. If n test samples of different concentrations are evaluated by and if m coding functions are included in the coding function set or coding function subset used for the evaluation, n m measured concentrations to step g. can be generated with

式中、△_ｉ，ｊは、コーディング関数ｉおよび色座標系ｊについての測定濃度ｃ^ｊ _Ｍと対応する公知の濃度ｃ^ｋ _Ｋとの間の類似度についての尺度であり、ここでｋはテスト試料セットの濃度を表わす数である。他のタイプの比較が概して可能であり、概して当業者には公知である。グラフ表示のためには、一例として、測定濃度をそれぞれのテスト試料中の分析物の濃度に対してプロットし、一例として、それぞれのテスト試料の各々の公知の濃度についての一群の測定濃度を結果として得ることができる。 step h. , the measured concentration set is compared to the known concentrations of the test samples in the test sample set. The term "comparing" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should be limited to specific or customized meanings. is not. The term specifically includes, but is not limited to, any type of qualitative or quantitative relationship or degree of similarity between at least one first item to be compared and at least one second item to be compared. can mean the determination of In the comparison, by way of example, at least one item of information representing a degree of similarity or identity between at least two items being compared may be generated. As an example, at least one item of information representing similarity or identity may include at least one item of statistical information, such as standard deviation. As an example, statistical information terms representing the degree of similarity or identity between a measured concentration set and a known concentration set can include:

where Δ _i,j is a measure for the similarity between the measured density c ^j _M and the corresponding known density c ^k _K for coding function i and color coordinate system j, where k is the test A number representing the concentration of the sample set. Other types of comparison are generally possible and generally known to those skilled in the art. For graphical representation, the measured concentrations are plotted against the concentration of the analyte in each test sample, in one example, resulting in a panel of measured concentrations for each known concentration of each test sample, in one example. can be obtained as

step h. further includes determining a best-match color coordinate system of the set of color coordinate systems and a best-match coding function of the set of coding functions that best matches the set of measured densities to the known densities. The term "match" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should not be limited to a specific or customized meaning. do not have. The term can specifically, without limitation, refer to the quality of two items being similar or identical. As a result, the term "best match" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and not limited to a particular or customized meaning. not what should be done. This term specifically indicates the highest degree of similarity or identity by comparison in the sense of the definitions given above, including, without limitation, the option of matching items within a given range. An item selected from at least two itemsets. Thus, as an example, according to the formula given above, the best-match coding function _i ^* and the best-match color coordinate system j ^* is and the color coordinate system of the color coordinate set. Other means of determining the best match are generally known in the field of statistics, step h. It is also applicable in

The result of the calibration method can thus be an indication of the best match coding function and an indication of the best match color coordinate system. This result may in particular be camera-specific and specific to the type of test element to be used. Consequently, for each camera type, e.g. each portable device or smart phone with a particular camera may be required to perform the calibration method, and for each test element type e.g. each product of the test element. It may also be required for lots. The calibration method can be carried out in advance for each known type of smart phone to be used, for example, and for each type of test element to be used, so that the result of the calibration method is stored in the corresponding computer program, e.g. It may already be pre-programmed or provided in a corresponding app to be run on a particular smartphone. The app or computer software may also be configured to detect the type of smart phone or portable device it runs on, and is known to be the best match for the test elements and cameras to use. A corresponding coding function and color coordinate system can be selected.

As outlined above, the camera may specifically be a portable electronic device camera, such as a portable computer camera, such as a laptop or template computer camera. However, in particular, many users carry their smartphones with them all day long, so smartphones are ubiquitous measuring devices, and their use for determining analytes such as blood glucose is highly advantageous. As such, the camera may be a smartphone camera.

As outlined above, the term "set" as used in various instances herein can generally refer to a finite or infinite number of elements. As outlined above, in relation to color coordinate systems, a set of color coordinate systems is specifically a set of parameterized functions for transforming color coordinates, namely one or more parameters can be defined by one or more of the functions that have Specifically, one or more parameterized functions can be used to transform the color coordinates provided by the camera into transformed color coordinates. The transformed color coordinates may specifically be camera-independent transformed color coordinates. Generally, a set of one or more parameters of the parameterized function can characterize each of the color coordinate systems.

A method step for generating color coordinates for the image of the colored test field by using the color coordinate system of the color coordinate system set to create a color coordinate set for the test sample and the coordinate system e. may be a single step or may include multiple substeps. Specifically, this step may involve first transforming the color coordinates for the image provided by the camera, which are typically camera dependent, into independent color coordinates. Thus, by way of example, the camera may include a camera chip, generally a camera chip, an optical setup of the camera or a device for recording images after an evaluation of the image has been recorded with this camera chip. Create a color coordinate (R,G,B) or quadruple of color coordinates that depends on the physical properties of the electronic component. Thus, as an example, the sensitivity of a camera chip can depend on the spectral range. As a result, the method avoids the camera-independent color coordinates, e.g. , which are comparable in the sense of being identical or at least comparable with a given tolerance. Consequently, camera-independent color coordinates as used herein can be color coordinates that can be obtained from different images taken with different cameras from one and the same colored test field. There, in general, a standardized camera-independent color coordinate system such as the CIE coordinate system can be used. Generally, however, as outlined above, method step e. can include the following substeps:
e1. generating camera-dependent color coordinates for the image of the colored test field;
e2. converting camera dependent color coordinates to camera independent color coordinates by using a first conversion algorithm;
e3. A second transformation algorithm is used to transform the camera-independent color coordinates into color coordinates for the color coordinate system of the color coordinate system set, which yields a color coordinate set for the test sample and the color coordinate system. step to be created.

The term "camera dependent color coordinates" as used herein generally means that the results depend on certain characteristics of the camera and/or the camera chip contained therein, such as a CCD and/or CMOS chip. means the color coordinates generated by using a camera to image a colored object. As an example, a camera chip may include a color sensor that records a value for each color, such as a tricolor set such as RGB or a quadrucolor set such as CMYK, where the value depends on the sensitivity of the camera chip. Therefore, when recording images of one and the same colored object by using different cameras, the camera-dependent color coordinates produced by these cameras may be different.

Therefore, in general, step e1. comprises evaluating an image of a colored test field, i.e. a test field wetted by a test sample and colored due to subsequent detection reactions in the test field. As an example, at least one image of each colored test field, such as each image of each colored test field, can be evaluated for the purpose of deriving its camera-dependent color coordinates. Thus, by way of example, a plurality of camera-dependent color coordinates (R _i , G _i , B _i ) can be generated.

Furthermore, the term "camera-independent color coordinates" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and no special or customized meaning. should not be limited to The term can specifically, and without limitation, refer to color coordinates that are independent of the sensitivity of the camera or camera chip, at least up to a predetermined tolerance. In other words, up to an acceptable difference, the camera-independent color coordinates of one and the same object do not depend on the sensitivity of the camera in use.

を使用することによってカメラ依存型色座標（Ｒ，Ｇ，Ｂ）をカメラ非依存型色座標（Ｘ，Ｙ，Ｚ）へと変換するための行列演算を含み得る。ここで、Ｍは、非依存型の較正プロセスによって生成され得る変換行列を表わし得る。一例として、カメラを較正するためおよび変換行列および／またはこの変換行列の係数を決定するために、基準色フィールドを使用することができる。一例として、カメラ依存型色座標Ｘ、Ｙ、Ｚは、ＣＩＥ色座標などの人間の眼の知覚に基づく座標であり得る。座標の４色組が使用される場合には、等式（１）の場合と類似の行列変換も使用することができる。色座標の行列変換は概して、例えばＦ．Ｋｏｎｉｇ：「Ｄｉｅ Ｃｈａｒａｋｔｅｒｉｓｉｅｒｕｎｇ ｖｏｎ Ｆａｒｂｓｅｎｓｏｒｅｎ」，Ｄｉｓｓｅｒｔａｔｉｏｎ，Ｌｏｇｏｓ Ｖｅｒｌａｇ，Ｂｅｒｌｉｎ，２００１，ｐｐ．４８～４９から公知である。 step e2. A calibration process may be used, or a predefined calibration function may be applied, to perform . As an example, a first transformation algorithm is a matrix operation by using matrix M, specifically the following transformation:

may include a matrix operation to convert camera-dependent color coordinates (R,G,B) to camera-independent color coordinates (X,Y,Z) by using Here M may represent a transformation matrix that may be produced by an independent calibration process. As an example, a reference color field can be used to calibrate the camera and to determine the transformation matrix and/or the coefficients of this transformation matrix. As an example, the camera dependent color coordinates X, Y, Z may be coordinates based on human eye perception, such as CIE color coordinates. A matrix transformation similar to that in equation (1) can also be used if a quartet of coordinates is used. Matrix transformations of color coordinates are generally described, for example, in F. Konig: "Die Charakterisierung von Farbsensoren", Dissertation, Logos Verlag, Berlin, 2001, pp. 48-49.

step e2. in i=l. . . Let p be the number of colored test fields or test samples to be evaluated, the set of camera-dependent color coordinates (R _i , G _i , B _i ) to the camera-independent color coordinates (X _i , Y _i , Z _i ). In a similar manner, transformations for four-color pairs of color coordinates are also possible.

を使用することによって、カメラ非依存型色座標（Ｘ，Ｙ，Ｚ）を色座標セット（Ｆ，ｍ，ｂ）へと変換するステップを含むことができる。 A second transformation algorithm can include transforming camera-independent color coordinates, such as coordinates X, Y, Z, into color coordinates of a color coordinate set by using a parameterized function. Thus, by way of example, the second transformation algorithm is the following parameterized function, where P ₁ -P ₁₁ are parameters, specifically real and/or rational numbers:

can include transforming the camera-independent color coordinates (X, Y, Z) to the color coordinate set (F, m, b) by using .

Here, step e2. in i=l. . . p, where p is the number of colored test fields or test samples to be evaluated, if a set of camera-independent color coordinates (X _i , Y _i , Z _i ) has been generated, step e3. The result of may be a set of parameterized color coordinates (F _i , m _i , b _i ).

The camera-independent color coordinates may specifically be color coordinates based on the sensitivity of the human eye, specifically color coordinates according to standards, more specifically CIE standards. Specifically, the camera-independent color coordinates can be tristimulus values.

のうちの１つ以上を使用することによって、相対的色座標（Ｆ_ｒｅｌ，ｍ_ｒｅｌ，ｂ_ｒｅｌ）へと変換され得る。 A second transform algorithm can also take into account the illumination of the test field as well. In particular, the second transformation algorithm can take into account the illumination of the test field, in particular by detecting at least one reference color, in particular the reference color of the white field. Thus, as an example, the illumination dependent color coordinates (F,m,b) are given by the following equations, where (F _R ,m _R ,b _R ) are the color coordinates derived from the image of the illumination reference field: :

can be converted to relative color coordinates (F _rel , m _rel , b _rel ) by using one or more of:

This gives i=l. . . A set of relative color coordinates (Fi, _{rel , mi,rel} , bi _,rel ) can be generated, where p is the number of colored test fields or test samples to be evaluated.

A first transformation algorithm, specifically the matrix M, may be determined in the camera calibration process by acquiring at least one image of at least one reference color field with known camera-independent color coordinates. Thus, by way of example, a plurality of reference color fields having known camera-independent color coordinates may be provided, such as color fields according to a color scale or the like. Images of these reference color fields can be recorded to generate a system of equations for deriving the coefficients of the matrix M or for any other transformation algorithm. Specifically, again, the known color coordinates of the reference color field may be known CIE coordinates and/or tristimulus values. However, other embodiments are possible.

を伴う有色テストフィールドの色座標（Ｆ_ｐ，ｍ_ｐ，ｂ_ｐ）、（Ｆ_ｖ，ｍ_ｖ，ｂ_ｖ）を結果としてもたらす可能性がある。εの精確な値は、選択される色空間に依存する。一例として、絶対値１００に正規化された色空間もあれば、１という絶対値に正規化された色空間もあり得る。座標Ｆ、ｍおよびｂに同じ重みを与えるために、重み付け因子によって色座標を重み付けすることさえできる。概して、εは、ｃｏｎｓｔより著しく小さく、すなわちε＜＜ｃｏｎｓｔ、例えばε＜０．１・ｃｏｎｓｔ、ε＜０．０１・ｃｏｎｓｔ、ε＜０．００１・ｃｏｎｓｔなどとして選択され得る。εの精確な値は、概して、予め決定されており、該方法について求められてはいない。 As outlined above, step h. , the set of measured densities is compared to the best match color coordinate system of the set of known densities and color coordinate systems of the test samples of the set of test samples to find the best match of the coding function set in which the set of measured densities best matches the known densities. A coding function is determined. Here, specifically, step h. can be performed in such a way that equidistant density samples lead to color coordinates within the best-match color coordinate system that have essentially equidistant color differences over a given range of density measurements. Therefore, one idea of a photometric coordinate system, such as the CIE coordinate system, may be transferred to density measurement. Specifically, over a given measurement range, test samples with different analyte concentrations C _p , C _v when applied to the test field have the following color differences, with ε as a given range:

may result in color coordinates (F _p , m _p , b _p ), (F _v , m _v , b _v ) of a colored test field with The exact value of ε depends on the color space chosen. As an example, some color spaces may be normalized to an absolute value of 100, while others may be normalized to an absolute value of 1. The color coordinates can even be weighted by a weighting factor to give equal weight to the coordinates F, m and b. In general, ε may be chosen as significantly smaller than const, ie ε<<const, eg ε<0.1·const, ε<0.01·const, ε<0.001·const, etc. The exact value of ε is generally predetermined and not determined for the method.

The color coordinate system of the set of color coordinate systems and the coding function of the set of coding functions in which the set of measured densities best match the known densities may form a pair, specifically including the best match color coordinate system and the best match coding function. . This pair may specifically be camera-specific and may be provided to the mobile device containing the camera, eg, via an app or other software.

からなる群から選択され得る。しかしながら、他のタイプのコーディング関数も使用可能であること、およびさらにコーディング関数セットが内部にパラメータを有する異なるタイプのコーディング関数の混合も含み得ること、が指摘されるものとする。しかしながら、以下でさらに詳述するように、多項式関数の使用により計算は簡素化され、分析物測定について優れた結果が得られる。 The coding function set may specifically include a parameterized polynomial function of color coordinates, as outlined above. As an example, the parameterized polynomial function is c(F,m,b) where the measured concentration of the analyte when the colored test field has color (F,m,b), where N is a positive integer, and where a _{i , j, k} as parameters of the polynomial function:

can be selected from the group consisting of However, it should be pointed out that other types of coding functions can also be used and that the coding function set may also contain a mixture of different types of coding functions with parameters therein. However, the use of polynomial functions simplifies the calculations and provides excellent results for analyte determination, as will be detailed further below.

As further outlined above, specifically, step c. The test elements of the test element sets used in may be of the same type, in particular by being all identical from setup. Specifically, these test elements can be from the same manufacturing lot. The calibration method can further include defining at least one standard test element setup, the standard test element setup comprising at least one test element configured for performing an optically detectable detection reaction with the analyte. At least one test chemical type is defined for at least one test field containing one test chemical. A standard test element setup may further include a defined substrate to which test chemicals are applied.

As outlined above, the test sample set may include at least three different test samples providing at least three different concentrations of the analyte. Specifically, the concentrations may be distributed equidistantly over the measurement range. The analyte may specifically be glucose. However, as outlined above, it is equally possible to detect other types of analytes. The sample may specifically be a bodily fluid, such as blood or interstitial fluid. However, it should be pointed out that other types of samples are possible as well. A test sample set may contain test samples of different concentrations over a predetermined measurement range of 0 mg/dl to 600 mg/dl. Thus, by way of example, for a sample of blood or interstitial fluid with glucose as the analyte, the measurement range may be from 0 mg/dl to 600 mg/dl, and the concentration of the test sample may be, for example, equidistantly spaced over the entire measurement range. can be distributed.

As will be described in further detail below, the calibration method can specifically be performed by using a computer or computer network. Specifically, therefore, step a. , d. , e. , f. , g. , or h. At least one of the may be performed using a computer or computer network and/or may be encoded in software.

In a further aspect of the invention, a detection method for detecting an analyte in a sample is disclosed. As detailed further below, the method uses the calibration method described above. Accordingly, reference may be made to the disclosure given above or further detailed below for possible definitions and embodiments. The method specifically includes the following steps, which can be performed in a given order. Moreover, different orders are possible as well. Moreover, it is possible to perform two or more method steps wholly or partially concurrently. Furthermore, it is possible to perform one or more or even all method steps once or repeatedly. The method may include additional method steps not listed herein. Generally, a method for detecting an analyte in a sample includes the steps of:
A. providing a camera;
B. calibrating the camera by using a calibration method;
C. applying the sample to the test element, the test element forming at least one test field containing at least one test chemical configured to undergo an optically detectable detection reaction with the analyte; creating at least one colored test field for the sample;
D. acquiring at least one image of at least one colored test field;
E. generating color coordinates for the test field by using the best match color coordinate system;
F. Converting the color coordinates to the measured concentration of the analyte in the sample by using the best match coding function.

Method step A. and B. can be broadly referred to the description of the calibration method given above. Method step C. for the above method step c. This step can be done in a very similar way to Method step C. Specifically, the test elements used in method step c. or be of the same type, preferably from the same production lot, as the test element used in . Due to the detection reaction in the presence of the analyte to be detected, the test field of the test element varies depending on the concentration of the analyte in the sample, including, by way of example, the possibility that no analyte is present in the sample. changes into a colored test field. Generally, one or more test fields can be wetted with at least one sample.

Method step D. For , above step d. You can refer to the description of Method step D. Acquisition of images in can be performed in a similar manner.

Method step E. and F. With respect to step h. Use the best match color coordinate system and best match coding function determined in . Again, generating the color coordinates of the test field may be a single-step process or may be a multi-step process. In the latter case, as an example, the camera can first generate camera-dependent color coordinates for a test field such as (R,G,B) in a manner analogous to step e1 above. These camera-dependent color coordinates can then be transformed into camera-independent color coordinates, where step e2. You can refer to the description of Thus, for example, camera-independent color coordinates (X, Y, Z) can be generated for colored test fields. Further, then, with the best match parameter set P, camera-independent color The coordinates can be transformed into color coordinates in the best match color coordinate system. Then step F. , the color coordinates can then be converted to measured concentrations of the analytes by using, for example, equation (5) above with the best match parameters.

Specifically, step C. ~F. can be performed iteratively. Step B. first step C. ~F. only once or each time for multiple iterations of step C. ~F. can be done before doing

As outlined above, step C. The test elements used in step c. can be of the same type as the test elements in the test element set in Therefore, the test elements may have the same set-up as used for the calibration method or may be manufactured in the same lot.

Again, the detection method may be supported by a computer or computer network, such as one or more of portable devices, such as tablets, laptops or mobile phones such as smart phones. Specifically, the computer or computer network performs step B.1. , D. , E. , or F. may be used to perform one or more of

It is further disclosed and suggested herein to implement the invention in one or more of the embodiments disclosed herein when the program is executed on a computer or computer network. A computer program product comprising computer-executable instructions for performing such a method. Thus, as generally described above, the computer program may specifically include computer-executable instructions for performing one or more or even all of the steps of the calibration method. Specifically, the computer program performs steps a. , d. , e. , f. , g. , or h. may include computer-executable instructions for doing one or more of, or even all of, Additionally or alternatively, step B. of the detection method. , D. , E. , or F. A computer program is proposed with computer-executable instructions for performing at least some or all of the method steps of the detection method, such as one or more and all of Specifically, the computer program can be stored on a computer-readable data storage medium.

Further disclosed and proposed herein is the ability of the present specification, such as one or more of the method steps described above in connection with a computer program, to execute the program when the program is executed on a computer or computer network. A computer program product comprising program code means for carrying out the method according to the invention in one or more of the embodiments disclosed therein. Specifically, the program code means may be stored on a computer-readable data storage medium.

Further disclosed and proposed herein is the above-described, e.g., computer program, after loading into a computer or computer network, e.g. A data storage medium having stored thereon a data structure capable of performing a method according to one or more of the embodiments disclosed herein by performing one or more of the steps.

Further disclosed and proposed herein are steps, such as one or more of the steps discussed above in connection with a computer program, which may be performed herein when the program is executed on a computer or computer network. A computer program product with program code means stored on a machine-readable storage medium for performing the method according to one or more of the embodiments disclosed therein. A computer program product, as used herein, means a program as a tradeable product. This product may generally exist in any format, for example in paper format or on a computer readable data storage medium. In particular, computer program products may be distributed over a data network.

Finally, disclosed and proposed herein is one or more of the embodiments disclosed herein, such as one or more of the steps discussed above in connection with a computer program. A modulated data signal containing instructions readable by a computer system or computer network to perform a method according to

For computer-implemented aspects of the invention, one or more and even all of the method steps of the method according to one or more of the embodiments disclosed herein may be performed using a computer or computer network. can be done by Thus, in general any of the method steps involving providing and/or manipulating data can be performed using a computer or computer network. In general, these method steps include any of the method steps except for some aspects of method steps that typically require manual intervention, such as providing a sample and/or performing the actual measurement. can be

Specifically, further disclosed herein are:
- a computer or computer network comprising at least one processor, the processor being adapted, in whole or in part, to perform one of the methods according to one of the embodiments described herein;
- computer-loadable, adapted to perform, wholly or partly, one of the methods according to one of the embodiments described herein while the data structure is running on a computer; data structure,
- a computer program adapted to perform, wholly or partly, one of the methods according to one of the embodiments described herein while the program is running on a computer;
- a computer comprising program means for performing, wholly or partly, one of the methods according to one of the embodiments described herein while the computer program is running on a computer or computer network; program,
- a computer program comprising program means according to the preceding embodiment, wherein the program means are stored on a computer-readable storage medium;
- of the method according to one of the embodiments described herein after the data structure has been stored on a storage medium and the data structure has been loaded into the primary and/or working storage of a computer or computer network. a storage medium adapted wholly or partly to carry out
- the program code means for performing wholly or partly one of the methods according to one of the embodiments described herein when the program code means are executed on a computer or computer network; A computer program product having program code means capable of being stored on or having been stored thereon a storage medium.

In a further aspect of the invention, a calibration system is disclosed for calibrating a camera for the purpose of detecting an analyte in a sample using the camera. A calibration system includes at least one computer or computer network. The calibration system is configured, for example by suitable software programming of a computer or computer network, to perform the calibration method according to the invention in any one of the embodiments described herein. Additionally, the calibration system can include a set of test samples and a set of test elements having known concentrations of analytes, each test element having at least one test field. Additionally, the calibration system can include a camera.

A further aspect of the invention employs at least one test element having at least one test field containing at least one test chemical configured to perform an optically detectable detection reaction with an analyte. Accordingly, a detection system is disclosed that is intended to detect an analyte in a sample. The detector system includes at least one camera, such as that of a mobile device, such as a smart phone. The detector system further comprises at least one computer or computer network, in particular at least one smart phone. The detection system is configured to perform a detection method according to any one of the embodiments described herein. Furthermore, the detection system can include at least one sample and at least one test element, the test element having at least one test field. Additionally, the detection system includes a test sample set having a known concentration of the analyte and a set of test elements, each test element having at least one test field.

Furthermore, when the computer program is executed by the calibration system described above or described in further detail below or by the detection system described above or described in more detail below, the above or described in more detail below, or the detection system described above or described in more detail below. A computer program is disclosed that includes instructions for performing each of the steps of the calibration method or the detection method described above or described in more detail below.

In a further aspect of the invention, the computer or computer network, when executed by the computer or computer network, performs steps a. of the calibration method as described above or described in more detail below. , d. , e. , f. , g. , or h. or step B. of the detection method. , D. , E. , or F. A computer program is disclosed that includes instructions for performing one or more of:

In a further aspect of the invention, a system for detecting an analyte in a sample is disclosed. The system includes a calibration system and a detection system according to the invention.

The methods, computer programs and systems of the present application can provide numerous advantages over known applications for calibrating and/or using cameras to detect analytes in samples. Specifically, therefore, the present invention can improve the accuracy of the determination of analyte concentration values. In particular, the present application can provide specifically adapted color spaces for use within algorithms for determining the concentration of an analyte in a sample, for example for determining blood glucose concentration. Specifically, the present application describes a specific method that creates a linear relationship between the color of the test strip and the concentration of the analyte, specifically between the color of the test strip and its resulting numerical representation. can provide a color space adapted to The linear relationship of the adapted color space can achieve uniform accuracy over the entire range of measurements. Furthermore, the present application may alternatively allow for extremely high accuracy of analyte concentration determination within a defined range of measurements, in particular within a predefined range of measurements.

Furthermore, the present application can improve the validity of optical determination of analyte concentration values. In particular, the validity of optical measurements can be improved compared to applications known from the art. Specifically, the specifically adapted color spaces provided in this application may be based on externally determined analyte concentrations. More particularly, the methods, computer programs and systems optimize and adapt the color space to externally determined analyte concentrations, such as laboratory determined analyte concentrations, and in particular to the determined A specifically adapted color space by enabling extremely high plausibility of measured analyte concentrations, thus improving the plausibility of optically determined analyte concentrations using the adapted color space. can be provided. In contrast, the prior art usually aims at optimization based on human color perception.

Moreover, unlike other methods known from the prior art, the methods, computer programs and systems of the present application can take into account non-linear factors that affect the detection of analytes in samples. Thus, the impact of non-linear factors on the detection of analytes in a sample is minimized or even eliminated by the present application. Specifically, the methods, computer programs and systems of the present application take into account non-linear factors such as different or variable lighting conditions or the individual technical and optical characteristics of the vast number of commercially available cameras. can do.

In summary, without excluding further possible embodiments, the following embodiments can be envisioned:
Embodiment 1. In a calibration method for calibrating a camera for detecting an analyte in a sample:
a. providing a color coordinate system set, the color coordinate system set including a plurality of different color coordinate systems configured to describe the color of an object;
b. providing a test sample set having a known concentration of the analyte;
c. applying a test sample to a set of test elements, each test element comprising at least one test chemical configured to undergo an optically detectable detection reaction with an analyte; having four test fields, thereby creating at least one colored test field for each of the test samples;
d. obtaining an image of a colored test field by using a camera;
e. generating color coordinates for the image of the colored test field by using the color coordinate system of the color coordinate system set, thereby creating a color coordinate set for the test sample and the color coordinate system; ;
f. providing a coding function set, the coding function set comprising a plurality of coding functions for converting color coordinates of the test field to corresponding concentrations of analyte in the sample;
g. By using the coding function set, step e. converting the set of color coordinates generated in to a set of measured densities;
h. Comparing the known densities of the test samples of the test sample set with the measured density set, and the best match of the color coordinate system set where the measured density set best matches the known densities the best match of the color coordinate system and the coding function set the coding function and
Calibration method including.

Embodiment 2. 2. The calibration method of embodiment 1, wherein the camera is a smartphone camera.

Embodiment 3. A set of color coordinate systems for transforming color coordinates, specifically camera-provided color coordinates to transformed color coordinates, more specifically transformed camera-independent color coordinates. 3. A calibration method as in any one of embodiments 1-2, defined by a parameterization function set, the parameter set of the parameterization function characterizing each of the color coordinate systems.

Embodiment 4. step e. But:
e1. generating camera-dependent color coordinates for the image of the colored test field;
e2. converting the camera dependent color coordinates to camera independent color coordinates by using a first conversion algorithm;
e3. A second transformation algorithm is used to transform the camera-independent color coordinates into color coordinates for the color coordinate system of the color coordinate system set, thereby creating a color coordinate set for the test sample and the color coordinate system. and
4. A calibration method as in any one of embodiments 1-3, comprising:

を使用することによってカメラ依存型色座標（Ｒ，Ｇ，Ｂ）をカメラ非依存型色座標（Ｘ，Ｙ，Ｚ）へと変換するための行列演算を含む、実施形態４．に記載の較正方法。 Embodiment 5. A first transformation algorithm is a matrix operation by using matrix M, specifically the following transformation:

Embodiment 4, which includes a matrix operation to convert the camera-dependent color coordinates (R,G,B) to camera-independent color coordinates (X,Y,Z) by using Calibration method described in .

Embodiment 6. 6. A calibration as in any one of embodiments 4-5, wherein the second transformation algorithm comprises transforming camera independent color coordinates into color coordinates of the color coordinate set by using a parameterized function. Method.

を使用することによってカメラ非依存型色座標（Ｘ，Ｙ，Ｚ）を前記色座標セット（Ｆ，ｍ，ｂ）へと変換するステップを含む、実施形態６に記載の較正方法。 Embodiment 7. A second conversion algorithm is
Given that P ₁ -P ₁₁ are parameters, specifically real and/or rational numbers, the following parameterized functions:

7. The calibration method of embodiment 6, comprising transforming camera-independent color coordinates (X, Y, Z) into the set of color coordinates (F, m, b) by using .

Embodiment 8. 8. A calibration method as in any one of embodiments 4-7, wherein the second transformation algorithm takes into account illumination of the test field.

Embodiment 9. 9. As in any one of embodiments 4-8, wherein the camera-independent color coordinates are specifically color coordinates based on the sensitivity of the human eye, specifically color coordinates according to a standard, more specifically a CIE standard. A calibration method according to claim 1.

Embodiment 10. 10. The calibration method of embodiment 9, wherein the camera independent color coordinates are tristimulus values.

Embodiment 11. 11. Calibration method according to embodiment 10, wherein the second transformation algorithm takes into account the illumination of the test field, in particular by detecting at least one reference color, in particular a white field reference color.

のうちの１つ以上を使用することによって、相対的色座標（Ｆ_ｒｅｌ，ｍ_ｒｅｌ，ｂ_ｒｅｌ）へと変換される、実施形態１１に記載の較正方法。 Embodiment 12. Illumination dependent color coordinates (F,m,b) are given by the following equations, where (F _R ,m _R ,b _R ) are color coordinates derived from the image of the illumination reference field:

12. The calibration method of embodiment 11, wherein the conversion to relative color coordinates (F _rel , m _rel , b _rel ) is performed by using one or more of:

Embodiment 13. At least one image of at least one reference color field in which the first transformation algorithm, specifically the matrix M, has known camera-independent color coordinates, specifically known CIE coordinates and/or tristimulus values. 13. A calibration method as in any one of embodiments 4-12, wherein is determined in the camera calibration process by obtaining .

Embodiment 14. step h. is carried out over a predetermined range of density measurements in such a way that equidistant density samples lead to color coordinates in a best-match color coordinate system with essentially equidistant color differences. A calibration method according to claim 1.

を伴う有色テストフィールドの色座標（Ｆ_ｐ，ｍ_ｐ，ｂ_ｐ）、（Ｆ_ｖ，ｍ_ｖ，ｂ_ｖ）を結果としてもたらす、実施形態１４に記載の較正方法。 Embodiment 15. Over a defined measurement range, test samples with different analyte concentrations C _p , C _v when applied to the test field have the following color differences, with ε as a defined range:

_15. A calibration method according to embodiment 14, resulting in color coordinates ( _Fp , _{mp,bp), (Fv,mv , bv )} of a colored test field with

Embodiment 16. The color coordinate system of the color coordinate system set and the coding function of the coding function set in which the measured density set best matches the known density form a pair, specifically including the best match color coordinate system and the best match coding function. 16. A calibration method according to any one of embodiments 1-15.

Embodiment 17. 17. A calibration method as in any one of embodiments 1-16, wherein the coding function set comprises a parameterized polynomial function of color coordinates.

からなる群から選択される、実施形態１７に記載の較正方法。 Embodiment 18. A parameterized polynomial function is obtained where c(F,m,b) is the measured concentration of the analyte when the colored test field has color (F,m,b), N is a positive integer, and α _{i,j , k} as parameters of the polynomial function:

18. A calibration method according to embodiment 17, selected from the group consisting of:

Embodiment 19. step c. 19. A calibration method according to any one of embodiments 1-18, wherein the test elements of the test element set used in are all of the same type, preferably all are identical.

Embodiment 20. Further comprising defining at least one standard test element setup, the standard test element setup including at least one test chemical configured to undergo an optically detectable detection reaction with the analyte. 20. A calibration method according to any one of embodiments 1-19, wherein at least one test chemical type for at least one test field is defined.

Embodiment 21. 21. A calibration method according to embodiment 20, wherein the standard test element setup further comprises a defined substrate to which the test chemistry is applied.

Embodiment 22. 22. A calibration method according to any one of embodiments 1-21, wherein the test sample set comprises at least three different test samples providing at least three different concentrations of the analyte.

Embodiment 23. 23. A calibration method according to any one of embodiments 1-22, wherein the analyte is glucose.

Embodiment 24. 24. A calibration method according to embodiment 23, wherein the sample is a bodily fluid.

Embodiment 25. 25. A calibration method according to any one of embodiments 23-24, wherein the test sample set comprises test samples of different concentrations over a predetermined measurement range of 0 mg/dl to 600 mg/dl.

Embodiment 26. Specifically, step a. , d. , e. , f. , g. , or h.

Embodiment 27. In a detection method for detecting an analyte in a sample:
A. providing a camera;
B. calibrating the camera by using the calibration method of any one of embodiments 1-26;
C. applying the sample to the test element, the test element forming at least one test field containing at least one test chemical configured to undergo an optically detectable detection reaction with the analyte; creating at least one colored test field for the sample;
D. acquiring at least one image of at least one colored test field;
E. generating color coordinates for the test field by using the best match color coordinate system;
F. converting the color coordinates to the measured concentration of the analyte in the sample by using a best match coding function;
method including.

Embodiment 28. step C. If the test element used in step c. of the calibration method 28. A detection method according to embodiment 27, wherein the test elements of the test element set of are of the same type.

Embodiment 29. step C. ~F. is performed iteratively, step B is first followed by step C. ~F. only once or each time for multiple iterations of step C. ~F. 29. The method of detection according to any one of embodiments 27-28 of the method of detection, performed prior to performing the.

Embodiment 30. Specifically, step B. , D. , E. , or F. 30. A method of detection according to any one of embodiments 27-29 which relates to the method of detection by using a computer or computer network to perform one or more of:

Embodiment 31. When the computer program is executed by the computer or computer network, the computer or computer network can read any of the calibration method according to any one of the calibration method embodiments 1 through 26 or any of the detection method embodiments 27 through 30. A computer program comprising instructions for performing the steps of the detection method according to one.

Embodiment 32. 27. A calibration system for calibrating a camera for the purpose of detecting an analyte in a sample by using the camera, comprising at least one computer or computer network, according to any one of the calibration method embodiments 1-26. A calibration system configured to perform the described calibration method.

Embodiment 33. 33. The calibration system of embodiment 32, further comprising a set of test samples having known concentrations of analytes, and a set of test elements, each test element having at least one test field.

Embodiment 34. 34. A calibration system as in any one of embodiments 32-33, further comprising a camera.

Embodiment 35. An analyte in a sample is detected by using at least one test element having at least one test field containing at least one test chemical configured to undergo an optically detectable detection reaction with the analyte. Embodiment 27 relating to a detection method comprising at least one camera, in particular a camera of a smart phone, and at least one computer or computer network, in particular at least one smart phone, in a detection system for detecting 31. A detection system configured to perform the detection method of any one of Claims 1-30.

Embodiment 36. 36. A detection system according to embodiment 35, further comprising at least one sample and at least one test element, the test element having at least one test field.

Embodiment 37. 37. A detection system according to any one of embodiments 35-36, further comprising a set of test samples and a set of test elements having known concentrations of analytes, each test element having at least one test field.

Embodiment 38. 38. A system for detecting an analyte in a sample, comprising the calibration system and detection system of embodiments 36 and 37.

Further optional features and embodiments are described in more detail in the subsequent description of the embodiments, preferably in conjunction with the dependent claims. Here, each optional feature can be implemented singly as well as in any possible combination, as the person skilled in the art will appreciate. The scope of the invention is not limited by the preferred embodiments. Embodiments are depicted schematically in the figures. Here, identical reference numbers in these figures denote identical or functionally comparable elements.

1 illustrates a system embodiment, a detection system embodiment and a calibration system embodiment; 4 illustrates an embodiment of a calibration method flow diagram. 4 illustrates an embodiment of a calibration method flow diagram. 1 illustrates an embodiment of a detection method flow diagram. 4 illustrates part of a flow diagram of one embodiment of a calibration method; 4 illustrates a chart representing the relationship between actual blood glucose levels and camera-independent color coordinates X; 4 illustrates an embodiment of a chart representing the relationship between actual blood glucose levels and selected CIE coordinates; Actual blood glucose using the general method and system for determining blood glucose levels (Figure A) and using the method and system of the present application for determining blood glucose levels (Figure B) 4 illustrates an embodiment of a chart representing the relationship between values and determined blood glucose levels.

One embodiment of system 110 is illustrated in FIG. 1 and includes calibration system 112 and detection system 114 . The calibration system 112 illustrated in FIG. 1 is configured to calibrate a camera 116 , such as a camera included in a smart phone 118 . Calibration system 112 includes at least one computer 119 or computer network 120 . In the illustrated embodiment of calibration system 112 , computer 119 may specifically be a stationary computer or computer network 120 . Alternatively, computer 119 may be, for example, a computer network of one or more of mobile or portable devices such as tablets, laptops or cell phones, such as smart phone 118 . Calibration system 112 is further configured to perform a calibration method, specifically the calibration method illustrated in FIGS. Further, FIG. 1 illustrates a test sample 122 , specifically a test sample set 122 that includes two or more test samples 124 . The test sample 124 may specifically be a sample of bodily fluid, such as blood or urine. The test samples 124 included in the test sample set 122 can specifically have different concentrations of analytes. In particular, each test sample 124 may have a known concentration of analyte, particularly when calibrating camera 116 . Further, FIG. 1 illustrates a set of test elements 126, each test element 128 including at least one test chemical configured to undergo an optically detectable detection reaction with an analyte. It has one test field 130 , thereby creating at least one colored test field 130 .

Detection system 114 illustrated in FIG. 1 is configured for detecting analytes in sample 131 by using at least one test element 128 having at least one test field 130 . Sample 131 may be, for example, a single sample intended to detect analyte concentration, and thus, in particular, the concentration of analyte within sample 131 may be unknown. Specifically, detection system 114 may be configured to detect an analyte in sample 131, illustrated separately from test sample set 122 in FIG. Similarly, test element 128 with test field 130 may be test element 128 illustrated separately from test element set 126 in FIG. Test field 130 contains at least one test chemical configured to undergo an optically detectable detection reaction with an analyte. Detection system 114 includes at least one camera 116 , specifically camera 116 of smart phone 118 . Further, detection system 114 includes at least one computer 119 or computer network 120 . In the illustrated embodiment of detection system 114 , computer 119 may specifically be smart phone 118 . The detection system is further configured for performing a detection method, specifically the detection method illustrated in FIG.

In general, typical problems can occur when calibrating cameras for detecting optically detectable detection reactions. As an example, an approach for determining the units of measure for measuring color difference used to calibrate a camera may include the following three steps.

A first step may include, for example, performing an initial transformation. Specifically, the first step includes transforming camera-dependent color coordinates, such as (R,G,B), to camera-independent color coordinates, such as (X,Y,Z). obtain. Typically, the initial transformation can serve the purpose of linking the physical size of the electromagnetic spectrum with physiological color vision. For example, the initial transform can use equation (1) disclosed in the discussion above. Specifically, standard algorithms, such as standard algorithms based on measurements of reference colors, can be used to determine the transformation matrix M used in equation (1). Again, F. Konig: "Die Charakterisierung von Farbsensoren", Dissertation, Logos Verlag, Berlin, 2001, pp. References to 48-49 may be indicated.

A second step may include further transformations. Specifically, the second step is to optimize the camera-independent color coordinates, e.g. (X, Y, Z), to a selected color space, e.g. into a color space suitable for, for example, a color space according to the CIE. The following equations represent the computation of the conversion of camera-independent color coordinates to the CIE L*a*b color space.

A third step may include calculating and/or defining units of measure for measuring differences between colors, eg, color difference. A color difference, such as a color difference between two samples p and v based on the L*a*b color space, can be determined using the following equation:

In particular, other mathematical formulas or equations for defining units of measure can be used, such as non-linear equations, eg more complex non-linear equations that take into account possible non-linear effects on color difference. Specifically, a non-linear equation that takes into account the possible effect of _{ambient luminance} on color difference, e.g. .

The methods described above typically involve several challenges when applied to analytical measurements. Accordingly, mathematical formulas such as those for defining units of measurement based on CIE recommendations may aim to establish a link or relationship between human color perception and the physical causes of color stimuli, thus , can lead to equidistant data, eg, irregular or non-linear representations of equidistant analyte concentrations. In particular, said non-linear representation may lead to non-linear or irregular accuracy of determined analyte concentrations, for example when used to calibrate camera 116 for detecting analytes. Accordingly, the calibration method for calibrating the camera 116 for detecting an analyte in a sample 131 according to the present invention is specifically designed to provide essentially equidistant concentrations of sample over a predetermined concentration measurement range. can be done in such a way as to derive color coordinates with a color difference of . One embodiment of a flow diagram of a calibration method for calibrating camera 116 for detecting analytes in sample 131 is illustrated in FIG.

FIG. 2 shows a calibration method according to the invention. The calibration method illustrated in FIG. 2 includes steps a. to provide a color coordinate system set. (method step 132), these color coordinate system sets include a plurality of different color coordinate systems configured to describe the color of an object. The calibration method further comprises a step b. for providing a test sample set 122 having a known concentration of analyte. (method step 134). Specifically, the test specimen set 122 illustrated in FIG. 1 can be provided.

Further, the calibration method includes step c. for applying test sample 124 to test element set 126. (method step 136). Specifically, test specimen 124 may be applied to test element set 126 illustrated in FIG. Specifically, each of test samples 124 from test sample set 122 can be applied to test elements 128 from test element set 126, respectively. Each test element 128 has at least one test field 130 containing at least one test chemical configured to undergo an optically detectable detection reaction with an analyte, whereby each test sample 124 At least one colored test field 130 for is created.

The calibration method further includes step d. for acquiring an image of the colored test field 130 by using the camera 116. (method step 138). Specifically, an image of the colored test field 130 can be acquired using the camera 116 included in the smart phone 118 illustrated in FIG. Further, the calibration method generates color coordinates for the image of the colored test field 130 by using the color coordinate system of the color coordinate system set, thereby creating a color coordinate set for the test sample and the color coordinate system. step e. (method step 140).

Further, the calibration method includes step f. for providing a set of coding functions. (method step 142). The coding function set includes a plurality of coding functions for converting color coordinates of test field 130 to corresponding concentrations of analytes in test sample 124 . The calibration method further uses the coding function set to perform step e. for converting the set of color coordinates generated in g. to a set of measured densities. (method step 144).

Additionally, the calibration method compares the set of measured densities to the known densities of the test sample 124 to determine the best match color coordinate system and best match coding function of the set of coding functions in which the set of measured densities best matches the known densities. a step for h. (method step 146). Specifically, the calibration method may be performed in such a manner that equidistant density samples 124 lead to color coordinates within a best match color coordinate system having essentially equidistant color differences.

For example, one, more or all of the color coordinates can be taken from different color coordinate systems, specifically different color coordinate systems from the set of color coordinate systems, and used to determine the best match color coordinates. system can be covered. Specifically, the best match color coordinate system may consist of two or more, preferably three or more color coordinates. Specifically, multiple color coordinates can be referred to as multidimensional parameters. Typically, in order to determine the best match color coordinate system, the difference, eg error, specifically between the measured density and the known density, eg between the measured result and the reference result, can be minimized. In particular, the error can be minimized, for example, by adapting the values of the parameters, eg coefficients, of the color coordinate system, eg of the color space. Furthermore, the coding function set can specifically include two or more coding functions, specifically referred to as multi-dimensional code functions. As an example, a multidimensional code function can be used to calculate a result, eg, determine the analyte concentration of sample 131 .

In FIG. 3 a further embodiment of a calibration method is shown, which for the most part corresponds to the method of FIG. Therefore, reference can be made to the description of FIG. 2 above for most of the steps. As illustrated in FIG. 3, in this embodiment of the calibration method, step e. (method step 140) may include three substeps. Specifically, a first sub-step e1. for generating camera-dependent color coordinates for the image of the colored test field 130; (method step 148), a second sub-step for transforming camera-dependent color coordinates into camera-independent color coordinates by using a first transformation algorithm e2. (method step 150), and transforming the camera-independent color coordinates into color coordinates for the color coordinate system of the color coordinate system set by using a second transformation algorithm, thereby for the test sample and the color coordinate system a third sub-step for creating the color coordinates of e3. (method step 152).

FIG. 4 illustrates a flow diagram of one embodiment of a detection method for detecting analytes in sample 124 . The detection method includes step A.1 to provide camera 116. (method step 154). Specifically, a camera 116 as illustrated in FIG. 1 may be provided. Further, the detection method includes step B.1 for calibrating the camera 116 by using the calibration method. (method step 156). Specifically, the calibration method illustrated in FIGS. 2 or 3 can be used to calibrate camera 116 .

Additionally, the detection method includes step C.1 for applying the sample to the test element 128. (method step 158), wherein the test element 128 has at least one test field 130 containing at least one test chemical configured to undergo an optically detectable detection reaction with the analyte; This creates at least one colored test field 130 for the sample 124 . Specifically, sample 124, illustrated separately from test sample set 122 in FIG. It can be applied to element 128 .

Further, the detection method includes a step D.1 for acquiring at least one image of at least one colored test field 130. (method step 160). The detection method further includes step E. for generating color coordinates of test field 130 by using a best match color coordinate system. (method step 162). Specifically, the best match color coordinate system can be composed of three or four color coordinates from different color coordinate systems of the color coordinate system set. Further, the detection method includes step F. for converting color coordinates to measured concentrations of analytes in sample 124 by using a best match coding function. (method step 164).

FIG. 5 illustrates part of a flow diagram of one embodiment of a calibration method for calibrating camera 116 for detecting analytes in sample 124 . This embodiment can be considered part of the specific embodiment of the method shown in FIG. 2 or FIG. Specifically, substep e2. and e3. (method steps 150 and 152) and step f. , g. , and h (method steps 142, 144 and 146) may be illustrated in FIG.

step e2. The first transformation algorithm of (method step 150) may include, for example, matrix operations. Specifically, the first transformation algorithm is a matrix operation by using a matrix M, specifically camera dependent color coordinates (R,G,B) to camera independent color coordinates (X,Y, Z) can include matrix operations. The first transform may specifically use the transform shown in equation (1), specifically the transform exemplified in the first box on the left hand side of FIG.

step e3. The second transformation algorithm of (method step 152) may include, for example, transforming the camera-independent color coordinates into color coordinates of the color coordinate set by using a parameterized function. Specifically, the camera-independent color coordinates (X, Y, Z) are converted from the color coordinate set (F, m, b). As illustrated in FIG. 5, the step of transforming the camera-independent color coordinates (X,Y,Z) into a set of color coordinates (F,m,b) comprises: can be done with The second transformation algorithm can also take into account the illumination of the test field, in particular by detecting at least one reference color, in particular the reference color of the white field. Specifically, the illumination dependent color coordinates (F,m , b) into relative color coordinates (F _rel , m _rel , b _rel ).

Thereafter, step f. g. and h. (method steps 142, 144 and 146) may be performed. Specifically, step h. (Method step 146) may be performed in such a way that the equidistant density samples 124 lead to color coordinates within the best match color coordinate system that have essentially equidistant color differences over a predetermined density measurement range. As an example, the rightmost box shown in FIG. 5 shows the linear relationship between the actual analyte concentration (c) of sample 124 (x-axis) and the essentially equidistant color difference (ΔE) of sample 124. It shows an ideal example where a relationship exists.

In addition, FIG. 6 shows the relationship between the actual blood glucose level (c), e.g., the predefined blood glucose level of the test sample 124, and the ratio X/(X+Y) of the camera-independent color coordinates (X, Y, Z). A diagram showing the relationship between is illustrated. In the example shown, the color coordinate Z is set to Z=0. Specifically, the _{ratio of} camera _- independent color coordinates _{a 0} ·X _{/ ( a} 1.X+a _2.Y ) are plotted on the y-axis of the diagram illustrated in FIG. Plotted on the x-axis is the concentration of the analyte in the sample 124, e.g., the actual blood glucose level (c) expressed in milligrams per deciliter (mg/dl) is the concentration of the analyte. Specifically, this chart shows the non-linear dependence between the ratio of camera-independent color coordinates and the actual blood glucose level. Thus, in the example shown, equidistant density samples do not lead to essentially equidistant color differences in the chosen color coordinate system (X,Y,Z). Therefore, the selected color coordinate system may not be the best match color coordinate system, and further steps of calibration methods may be required, as illustrated in FIGS. 2 and 3, for example.

Further, FIGS. 7A-E show actual blood glucose levels (c) and selected CIE coordinates, eg, L ^* in FIG. 7A, a* in FIG. 7B, b ^* in FIG. 7C, and u′ in FIG. 7D ^. and v′ in FIG. 7E. These charts represent different relationships between actual blood glucose levels and CIE coordinates for all CIE coordinates. Specifically, as illustrated, different color coordinates have different suitability for matching with actual blood glucose concentration. Specifically, the illustrated samples of equidistant densities, which are the same for all FIGS. 7A-E, do not lead to essentially equidistant color differences for all illustrated CIE coordinates. For example, the chart illustrated in FIG. 7D represents a more linear relationship between color coordinate u′ and sample than the chart illustrated in FIG. 7B, based on color coordinate a ^* .

FIG. 8A illustrates one embodiment of a chart representing the relationship between determined blood glucose levels and actual blood glucose levels using the general method and system for blood glucose determination. Specifically, the chart illustrated in FIG. 8A shows the relationship between the determined or measured blood glucose level (mBG) and the actual blood glucose level (c) using the CIE color coordinate system L ^* a ^* b ^* . represents

In contrast, FIG. 8B illustrates one embodiment of a chart representing the relationship between determined blood glucose levels and actual blood glucose levels using the methods and systems of the present application for blood glucose determination. is exemplified. Specifically, the chart illustrated in FIG. 8B plots between the determined and measured blood glucose level (mBG) and the actual blood glucose level (c) using the BestMatch color coordinate system and the BestMatch coding function. represents a relationship. In particular, comparing Figures 8A and 8B, it can be seen that the accuracy is improved when using the BestMatch color coordinate system and BestMatch coding function compared to using the general approach for determining blood concentration. I understand. Specifically, when using the BestMatch color coordinate system and the BestMatch coding function, a lower degree of scattering of the determined blood glucose level can be achieved. Further, FIGS. 8A and 8B show areas AE of the error grid analysis, specifically areas AE of the Parkes error grid, which quantify the clinical accuracy of the determined blood glucose concentration compared to the actual blood glucose concentration. indicates

Further, FIGS. 8A and 8B show areas AE of the error grid analysis, specifically areas AE of the Parkes error grid, which quantify the clinical accuracy of the determined blood glucose concentration compared to the actual blood glucose concentration. indicates For example, blood glucose levels in the following areas are:
- Blood glucose values within region A include values within 20% of the reference sensor;
- Blood glucose levels within region B include values outside 20% but not likely to lead to inappropriate treatment;
- Blood glucose levels in region C include values that lead to unnecessary therapy;
- Blood glucose values in region D contain values representing potentially dangerous hypoglycemia or hyperglycemia failure to detect.
- Blood glucose levels within region E include values that may confuse treatment of hypoglycemia with hyperglycemia and vice versa.

For more information on error grid analysis, see Clarke WL, Cox D, Gonder-Frederick LA, Carter W, Pohl SL: Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diabetes Care 10:622-628, 1987.

Both Figures 8A and 8B are based on the same sample, specifically the same test sample set is used to determine blood glucose levels in both figures. Table 1 lists the number of determined blood glucose levels for both Figures 8A and 8B sorted according to their respective regions.

表１：それぞれの領域に応じて選別された、図８Ａおよび図８Ｂの両方についての決定された血糖値の数

Table 1: Number of blood glucose values determined for both Figure 8A and Figure 8B, sorted according to their respective regions.

Specifically, the precision and accuracy of the determined blood glucose levels, as illustrated in FIGS. 8A and 8B and indicated by the quantification of the determined blood glucose levels noted in the table above. The accuracy can be improved when using the BestMatch color coordinate system and BestMatch coding function compared to using common approaches to determine blood glucose concentration.

110 System 112 Calibration System 114 Detection System 116 Camera 118 Smartphone 120 Computer Network 122 Test Specimen Set 124 Test Specimen 126 Test Element Set 128 Test Element 130 Test Field 131 Specimen 132 Step a. Providing a color coordinate system set.
134 Step b. Providing a test sample set having a known concentration of analyte.
136 Step c. applying a test sample to a set of test elements, each test element comprising at least one test chemical configured to undergo an optically detectable detection reaction with an analyte; having four test fields, thereby creating at least one colored test field for each of the test samples.
138 Step d. Acquiring an image of the colored test field by using a camera.
140 Step e. Generating color coordinates for the image of the colored test field by using the color coordinate system of the color coordinate system set, thereby creating a color coordinate set for the test sample and the color coordinate system.
142 Step f. Providing a coding function set, the coding function set containing a plurality of coding functions for converting color coordinates of the test field to corresponding concentrations of analytes in the sample.
144 Step g. By using the coding function set, step e. and converting the set of color coordinates generated in to a set of measured densities.
146 Step h. Comparing the known density of the test sample with the set of measured densities to determine the best match color coordinate system of the set of color coordinate systems and the best match coding function of the set of coding functions for which the set of measured densities best matches the known density. .
148 Step e1. Generating camera-dependent color coordinates for the image of the colored test field.
150 Step e2. Transforming the camera dependent color coordinates into camera independent color coordinates by using a first transformation algorithm.
152 e3. A second transformation algorithm is used to transform the camera-independent color coordinates into color coordinates for the color coordinate system of the color coordinate system set, thereby creating a color coordinate set for the test sample and the color coordinate system. steps to be taken.

Claims (16)

In a calibration method for calibrating a camera for detecting an analyte in a sample:
a. providing a set of color coordinate systems, said set of color coordinate systems including a plurality of different color coordinate systems configured to describe the color of an object;
b. providing a test sample set having a known concentration of said analyte;
c. applying a test sample to a set of test elements, each test element comprising at least one test chemical configured to undergo an optically detectable detection reaction with said analyte; having one test field thereby creating at least one colored test field for each of said test specimens;
d. obtaining an image of the colored test field by using the camera;
e. generating color coordinates for the image of the colored test field by using the color coordinate system of the color coordinate system set, whereby a color coordinate set for the test sample and the color coordinate system is a step that is created;
f. providing a coding function set, said coding function set comprising a plurality of coding functions for converting color coordinates of a test field into corresponding concentrations of said analyte in said sample; ;
g. by using said coding function set, step e. converting the set of color coordinates generated in to a set of measured densities;
h. comparing said known densities of said test samples of said set of test samples with said set of measured densities, and said best match color coordinate system of said set of color coordinate systems in which said set of measured densities best match said known densities and said determining a best matching coding function of the set of coding functions;
Calibration method including. 2. The calibration method of claim 1, wherein the camera is a smart phone camera. 3. Calibration according to claim 1 or 2 , wherein the set of color coordinate systems is defined by a set of parameterized functions for transforming color coordinates, the set of parameters of the parameterized functions characterizing each of the color coordinate systems. Method. step e. But:
e1. generating camera dependent color coordinates for said image of said colored test field;
e2. transforming the camera-dependent color coordinates to camera-independent color coordinates by using a first transformation algorithm;
e3. Converting the camera-independent color coordinates to color coordinates for the color coordinate system of the set of color coordinate systems by using a second conversion algorithm, thereby converting the camera-independent color coordinates for the test sample and the color coordinate system a step in which a color coordinate set is created;
4. A calibration method according to any one of claims 1 to 3, comprising: 5. A calibration method according to claim 4, wherein said second transformation algorithm comprises transforming said camera independent color coordinates to said color coordinates of said color coordinate set by using a parameterized function. The second conversion algorithm is
The following parameterized function, where P ₁ -P ₁₁ are parameters:

6. A calibration method according to claim 5, comprising transforming said camera-independent color coordinates (X, Y, Z) into said color coordinate set (F, m, b) by using . wherein the camera-independent color coordinates are color coordinates based on the sensitivity of the human eye, the camera-independent color coordinates are tristimulus values, and the second transformation algorithm takes into account illumination of the test field. Putting in the illumination dependent color coordinates (F,m,b) where (F _R ,m _R ,b _R ) are the color coordinates derived from the image of the illumination reference field:

converted to relative color coordinates (F _rel , m _rel , b _rel ) by using one or more of
A calibration method according to any one of claims 4 to 6. 8. Any one of claims 4 to 7, wherein the first transformation algorithm is determined in a camera calibration process by acquiring at least one image of at least one reference color field with known camera-independent color coordinates. or the calibration method according to item 1. step h. leads to color coordinates within said best-match color coordinate system having essentially equidistant color differences over a given range of density measurements. Calibration method as described. In a detection method for detecting an analyte in a sample:
A. providing a camera;
B. calibrating the camera by using the calibration method according to any one of claims 1 to 9;
C. applying said sample to a test element, said test element comprising at least one test chemical configured to undergo an optically detectable detection reaction with said analyte; having a test field, thereby creating at least one colored test field for the sample;
D. acquiring at least one image of said at least one colored test field;
E. generating color coordinates for the test field by using the best match color coordinate system;
F. converting the color coordinates into measured concentrations of the analyte in the sample by using the best match coding function;
method including. step C. ~F. is performed iteratively, step B is first followed by step C. ~F. only once or each time for multiple iterations of step C. ~F. 11. The method of detection according to claim 10, performed prior to performing the A calibration system for calibrating a camera for the purpose of detecting an analyte in a sample by using a camera comprising at least one computer or computer network, a test sample set having a known concentration of the analyte, and a test Calibration system comprising a set of elements, each test element having at least one test field, configured for performing the calibration method of any one of claims 1 to 9 relating to the calibration method system. detecting an analyte in a sample by using at least one test element having at least one test field containing at least one test chemical configured for performing an optically detectable detection reaction with said analyte comprising at least one camera and further comprising at least one computer or computer network, at least one sample and at least one test element, said test element having at least one test field A detection system configured for performing the detection method according to claim 10 or 11 . 14. A system for detecting an analyte in a sample, comprising the calibration system of claim 12 and the detection system of claim 13 . When a computer program is executed by a calibration system according to claim 12, the steps of the calibration method according to any one of claims 1 to 9 are transferred to the calibration system according to claim 12. A computer program containing instructions for effecting the A computer program comprising instructions which, when executed by a detection system according to claim 13, cause said detection system according to claim 13 to perform the steps of the detection method according to claim 10 or 11. .