Method for determining the concentration of an analyte in a body fluid

Abstract

A method for determining the concentration of an analyte in a bodily fluid is disclosed. The method includes using a mobile device (112) having a camera (114). The method further includes capturing at least one image of at least a portion of an optical test strip (124) having a test field (126) by using the camera (114). The method further includes determining at least one analyte concentration value from the color development of the test field (126), the method comprising: i) providing at least one correlation in the mobile device (112) for converting the color development of the test field (126) into an analyte concentration value; ii) providing at least one item of clearance information at the mobile device (112), the at least one item of clearance information indicating a confidence level for the correlation; iii) if the clearance information item indicates a sufficient level of confidence in the correlation, providing, by the mobile device (112), to the user, an indication that capturing the at least one image does not require the use of the color reference card (128). Further disclosed are a method for controlling an analytical measurement, a mobile device (112), a system (110) for controlling an analytical measurement, and a computer program for performing and / or controlling an analytical measurement.

Description

Technical Field

[0001] Technical Field The present invention relates to a method for determining the concentration of an analyte in a body fluid. The present invention further relates to a method for controlling an analytical measurement using at least one mobile device having a camera. Furthermore, the present invention relates to a mobile device having at least one camera, a system for controlling an analytical measurement, a computer program, and a computer-readable storage medium. The method, mobile device, computer program, and storage medium can be used in medical diagnosis, specifically, for qualitatively or quantitatively detecting one or more analytes in one or more body fluids, such as for detecting glucose in blood and / or interstitial fluid. However, other application fields of the present invention are possible.

Background Art

[0002] Background Art In the field of medical diagnosis, it is often necessary to detect one or more analytes from a sample of a body fluid, such as blood, interstitial fluid, urine, saliva, or other types of body fluids. Examples of analytes to be detected are glucose, triglycerides, lactate, cholesterol, or other types of analytes normally present in these body fluids. Depending on the concentration and / or presence of the analyte, appropriate treatment can be selected as needed. Without limiting the scope, the present invention can be specifically described with respect to blood glucose measurement. However, it should be noted that the present invention can also be used for other types of analytical measurements using test elements.

[0003] Generally, devices and methods known to those skilled in the art utilize test elements containing one or more test chemicals, which can perform one or more detectable detection reactions, such as optically detectable detection reactions, in the presence of the analyte to be detected. Regarding the test chemicals contained in the test elements, references can be made, for example, to J. Hoenes et al.: The Technology Behind Glucose Meters: Test Strips, Diabetes Technology & Therapeutics, Volume 10, Supplement 1, 2008, pp. S-10 to S-26. Other types of test chemicals are possible and can be used to practice the present invention.

[0004] In analytical measurements, particularly those based on colorimetric reactions, one technical challenge lies in the evaluation of color changes resulting from the detection reaction. In addition to using dedicated analytical devices such as handheld blood glucose meters, the use of commonly available electronic devices such as smartphones and portable computers or other mobile devices has been increasingly popular in recent years. As an example, WO 2012 / 131386 pamphlet discloses a test device for performing an assay, comprising a container containing a reagent that reacts with a test sample applied by exhibiting a change in color or pattern, and a portable device such as a mobile phone or laptop equipped with, for example, a processor and an image capturing device, wherein the processor is configured to process data captured by the image capturing device and output the test result of the applied test sample.

[0005] European Patent Application Publication No. 3 477 270 describes a method for evaluating the suitability of a mobile device having at least one camera for performing analytical measurements based on a color reaction. The method includes: a) providing at least one mobile device having at least one camera; b) providing at least one object having at least one reference color field; c) acquiring at least one image of at least one part of the reference color field by using the camera; and d) deriving at least one item of color resolution information by using the image.

[0006] In contrast to laboratory measurements and measurements performed by using a dedicated analytical measurement device, when using a mobile computing device such as a smartphone, various influences need to be considered. By way of example, lighting conditions, positioning, vibration, or other more or less uncontrollable conditions should be taken into account.

[0007] Generally, for optical evaluation, specifically for image evaluation, a plurality of methods have been developed. Among them, a method using an artificial neural network (ANN) is known.

[0008] U.S. Patent No. 6,122,042 discloses an apparatus for photometric analysis and / or identification of properties of a material object. The apparatus comprises a set of light sources having substantially different wavelength envelopes and activated in different combinations in a rapid sequence. The apparatus further comprises a set of spatially distributed photodetectors for detecting radiation from the object and generating a detection signal. A signal processor for controlling the light sources and analyzing the detected signals synchronizes the detected signals with the activation of a series of distinct combinations of the light sources to generate associated combinations of the detected signals, which are then analyzed to determine the physical properties of the object and / or to compare the similarity with previously detected signals from known objects. The photometric data can be combined and correlated with other measurement data to enhance the identification.

[0009] European Patent No. 1051687 discloses a system and method for medical diagnosis or risk assessment of patients. These systems and methods are designed to be used at the point of care, such as in an emergency treatment room or an operating room, or in any situation where quick and accurate results are desired. The systems and methods process patient data, particularly data from point-of-care diagnostic tests or assays, including immunoassays, electrocardiograms, X-rays, and other such tests, and provide indicators of the medical condition or its risk or absence. The system includes equipment for reading and evaluating the test data and software for converting the data into diagnostic information and risk assessment information.

[0010] U.S. Patent No. 10277877 discloses a method for converting a series of two-dimensional images into a series of three-dimensional images. The method includes receiving the series of two-dimensional images, and further converting the series of two-dimensional images into the series of three-dimensional images. The conversion can be based on a neural network to determine respective depth maps associated with each of the series of two-dimensional images, and processing the depth maps to render them as the series of three-dimensional images for display on a 3D display.

[0011] European Patent Application Publication No. 612035 discloses a method for verifying signatures and handwriting based on comparison of extracted features, preferably using a dedicated neural net.

[0012] International Publication No. WO 2018 / 224442 discloses a method and apparatus for analyzing an image using a deep neural network pre-trained for a plurality of classes. The image is processed by a forward pass through an adaptive neural network to generate a processing result. The adapted neural network is adapted from a neural network pre-trained to focus on exactly one selected class. The processing result is then analyzed and focused on features corresponding to the selected class using an image processing algorithm. A modified image is generated by removing the manifestation of these features from the image.

[0013] International Publication No. WO 2018 / 141429 discloses a method and apparatus for detecting an object of interest in an image. The method includes supplying at least one input image to a trained deep neural network including a stack of layers. The method further includes using at least one deconvolution output of at least one learned filter, or combining at least one deconvolution output of learned filters of at least one layer of the trained deep neural network, to detect an object of interest in the supplied image.

[0014] International Publication No. WO 1999 / 053288 discloses the use of an automated system and method for the interpretation of a Western blot. The program can analyze band patterns generated by an immunoblot test, such as a Western blot test, by scanning a test membrane with a digital camera and interpreting the test result as positive or negative. In one embodiment, a statistical analysis of the band data is used, while in another, a neural network is used. The statistical program can incorporate an interpretation algorithm, such as one supported by the CDC / ASTPHLD for a Western blot. The neural network can learn and improve its performance and develop its own criteria for interpretation through the analysis of a large number of positive and negative samples.

[0015] Despite the advantages associated with using a mobile computing device for the purpose of performing analytical measurements, several technical challenges remain. Thus, although artificial neural networks are commonly known for image analysis, the application of mobile-based evaluation of optical test strips such as colorimetric test strips remains difficult. Specifically, due to the vast amount of combined influencing factors and the continuous release of new smartphones using new technologies, it is generally difficult to generate the training data required for artificial neural networks in dedicated tests. Therefore, in principle, for each release of a mobile device, it is necessary to initiate new training tests that generally involve a vast amount of labor.

[0016] Problems to be Solved Therefore, it is desirable to provide an apparatus and method that at least partially addresses the above-described problems. Specifically, it is desirable to provide an apparatus and method that enable a user-friendly mobile-based determination of the concentration of an analyte in a body fluid with high accuracy and reproducibility but with low setup and preparation effort. Summary of the Invention

[0017] Summary This problem is addressed by a method for controlling an analytical measurement using at least one mobile device having a camera to determine the concentration of an analyte in a body fluid. Further, it is addressed by a mobile device having at least one camera having the features of the independent claims, by a system for controlling an analytical measurement, and by a computer program and a computer-readable storage medium. Advantageous embodiments that can be implemented alone or in any combination are described in the dependent claims.

[0018] When used hereinafter, the terms "have", "comprise", "include" or any grammatical variations thereof are used inclusively. Thus, these terms may refer to both situations where there are no additional features in the entity being described in this context in addition to the features introduced by these terms, and situations where there are one or more additional features. By way of example, the expressions "A has B", "A comprises B" and "A includes B" all refer to situations where, in addition to B, there are no other elements in A (i.e., the situation where A consists solely and exclusively of B), and situations where, in addition to B, there are one or more additional elements in entity A, such as element C, elements C and D, and even further elements.

[0019] Furthermore, it should be noted that terms such as "at least one", "one or more" or similar expressions indicating that a feature or element can be present once or multiple times are usually only used once when introducing each respective feature or element. Hereinafter, in most cases, when referring to each respective feature or element, the expressions "at least one" or "one or more" are not repeated, despite the fact that each respective feature or element can be present once or more than once.

[0020] Furthermore, when used hereinafter, terms such as "preferably", "more preferably", "particularly", "more particularly", "specifically", "more specifically" or similar terms are used in conjunction with any feature without restricting the possibility of alternatives. Thus, the features introduced by these terms are any features and are not intended to limit the scope of the claims in any way. As will be appreciated by those skilled in the art, the present invention may be practiced by using alternative features. Similarly, features introduced by expressions such as "in an embodiment of the present invention" or similar expressions are intended to be any features without limitation as to alternative embodiments of the present invention, without limitation as to the scope of the present invention, and without limitation as to the possibility of combining features introduced in such a way with any other optional or non-optional features of the present invention.

[0021] In a first aspect of the present invention, a method for determining the concentration of an analyte in a body fluid is disclosed, which includes using a mobile device having a camera. The method may include the following steps, which may be performed in a given order by way of example. However, it should be noted that different orders are also possible. Further, one or more method steps may be performed once or repeatedly. Additionally, two or more method steps may be performed simultaneously or overlapping in a timely manner. The method may include additional method steps not described. The method includes imaging at least one image of at least a portion of an optical test strip having a test field, and imaging includes using the camera of the mobile device. The method further includes determining at least one analyte concentration value from the color development of the test field.

[0022] The method includes i) providing, in the mobile device, at least one correlation for converting the color development of the test field into an analyte concentration value; ii) providing, in the mobile device, at least one clearance information item, wherein the at least one clearance information item indicates a reliability level for the correlation; iii) when the clearance information item indicates a sufficient reliability level for the correlation, providing, by the mobile device, an indication to the user that imaging at least one image does not require the use of a color reference card and further includes.

[0023] As used herein, the term "determining the concentration of an analyte in a body fluid", also referred to as "analytical measurement", is a broad term and should be given its ordinary and customary meaning to one of ordinary skill in the art and should not be limited to a special or customized meaning. Specifically, but not limited to, this term can refer to the quantitative and / or qualitative determination of at least one analyte in any sample or aliquot of a body fluid. For example, the body fluid can include one or more of blood, interstitial fluid, urine, saliva or other types of body fluids. The result of the concentration determination can be, by way of example, the concentration of the analyte and / or the presence or absence of the determined analyte. Specifically, by way of example, the analytical measurement can be a blood glucose measurement, and thus the result of the analytical measurement can be, for example, the blood glucose concentration. In particular, the analytical measurement result value can be determined by the analytical measurement.

[0024] Accordingly, the term "analyte concentration value", often also referred to as "analytical measurement result value" as used herein, is a broad term and should be given its ordinary and customary meaning to one of ordinary skill in the art and should not be limited to a special or customized meaning. Specifically, but not limited to, this term can refer to the numerical indication of the analyte concentration in a sample.

[0025] By way of example, at least one analyte can be or can include one or more specific chemical compounds and / or other parameters. By way of example, one or more analytes involved in metabolism such as blood glucose can be determined. Additionally or alternatively, other types of analytes or parameters such as, for example, pH values can be determined.

[0026] The method outlined above involves using at least one mobile device having at least one camera. As used herein, the term "mobile device" is a broad term and should be given its ordinary and customary meaning to one of ordinary skill in the art and should not be limited to a special or customized meaning. Specifically, but not limited to, this term can refer to a portable electronic device, and more specifically, a portable communication device such as a mobile phone or a smartphone. Additionally or alternatively, as further outlined in more detail below, the mobile device can also refer to a tablet computer or another type of portable computer having at least one camera.

[0027] As used herein, the term "camera" is a broad term and should be given its ordinary and customary meaning to one of ordinary skill in the art and should not be limited to a special or customized meaning. Specifically, but not limited to, the term can refer to a device having at least one imaging element configured to record or image spatially resolved one-dimensional, two-dimensional, or three-dimensional optical data or information. By way of example, a camera can include at least one camera chip such as at least one CCD chip and / or at least one CMOS chip configured to record an image. As used herein, without limitation, the term "image" can specifically relate to data recorded by using a camera, such as a plurality of electronic readings from an imaging element such as a pixel of a camera chip.

[0028] In addition to at least one camera chip or imaging chip, the camera can include one or more optical elements, such as additional elements like one or more lenses. By way of example, the camera can be a fixed-focus camera having at least one lens that is fixedly adjusted with respect to the camera. However, alternatively, the camera can also include one or more variable lenses that can be adjusted automatically or manually. The present invention must be applicable in particular to cameras that are commonly used in mobile applications such as notebook computers, tablets, or specifically mobile phones such as smartphones. Thus, specifically, the camera can be part of a mobile device that includes one or more data processing devices such as one or more data processors in addition to at least one camera. However, other cameras can also be used.

[0029] Specifically, the camera can be a color camera. Thus, for each pixel and the like, color information such as color values of three colors, R, G, and B, can be provided or generated, and more color values such as four color values for each pixel such as R, G, G, B are also achievable. Color cameras are generally known to those skilled in the art. Thus, by way of example, the camera chip can be composed of three or more different color sensors, such as color recording pixels like one pixel for red (R), one pixel for green (G), and one pixel for blue (B). For each pixel such as R, G, B, depending on the intensity of each color, the value can be recorded by a pixel such as a digital value in the range from 0 to 255. By way of example, instead of using a color triple such as R, G, B, four parts such as R, G, G, B can be used. The color sensitivity of the pixel can be generated by a color filter or by the appropriate inherent sensitivity of the sensor element used in the camera pixel. These techniques are generally known to those skilled in the art.

[0030] The method further includes using at least one optical test strip having at least one test field. As used herein, the term "optical test strip" is a broad term and should be given its ordinary and customary meaning to one of ordinary skill in the art and should not be limited to a special or customized meaning. This term can specifically, but not limited to, refer to any element or device configured to perform a color change detection reaction. The optical test strip can also be referred to as a test strip or a test element, and all three terms can refer to the same element. In particular, the optical test strip can have a test field containing at least one test chemical for detecting at least one analyte. By way of example, the optical test strip can include at least one substrate, such as at least one carrier, having at least one test field applied thereto or incorporated therein. In particular, the optical test strip can further include at least one white area, such as a white field, specifically in proximity to the test field, for example surrounding or enclosing the test field. The white area can be a separate field disposed independently on the substrate or carrier. However, additionally or alternatively, the substrate or carrier itself can be or can include a white area. By way of example, at least one carrier can be strip-shaped, thereby making the test element a test strip. These test strips are generally widely used and are available. One test strip can carry a single test field or multiple test fields having the same or different test chemicals contained therein.

[0031] As further used herein, the term "test field" is a broad term and should be given its ordinary customary meaning to one of ordinary skill in the art and should not be limited to a special or customized meaning. The term can refer, without being specifically limited to, an aggregated amount of test chemical, such as a circular, polygonal or rectangular shaped field having one or more layers of material having at least one layer of the test field in which the test chemical is included.

[0032] Furthermore, as outlined above, the method involves imaging at least one image of at least a portion of at least one optical test strip having at least one test field by using a camera. The term "imaging at least one image" as used herein is a broad term and should be given its ordinary customary meaning to those skilled in the art and should not be limited to a special or customized meaning. Specifically, without limitation, this term can refer to one or more of imaging, image recording, image acquisition, and image capturing. The term "imaging at least one image" can include imaging a single image and / or multiple images such as a series of images. For example, imaging an image can include continuously recording a series of images such as a video or a movie. Imaging at least one image can be initiated by a user action or can be automatically initiated, for example, when the presence of at least one object within the field of view of the camera and / or within a predetermined sector of the field of view is automatically detected. These automatic image acquisition techniques are known, for example, in the field of automatic barcode readers such as automatic barcode reading applications. Imaging of the image can be performed, by way of example, by the camera acquiring a stream or "lifestyle stream" of images, and one or more of the images are stored and used respectively as at least one first image or at least one second image automatically or by user interaction such as pressing a button. Image acquisition may be supported by a processor of the mobile device, and storage of the image may be performed within a data storage device of the mobile device.

[0033] At least one image of at least a portion of the optical test strip can specifically include an image of at least a portion of the test field. Further, the image can include an image of other portions of the optical test strip such as a white reference portion of the test strip.

[0034] Imaging at least one image can include applying a sample of body fluid to a test strip and imaging at least one image, and further optionally, for example, imaging at least one image without applying a sample of body fluid to the test strip, such as before applying the sample to the test strip and imaging the image. The latter image can specifically be used for comparison purposes and can also be referred to as a "blank image" or a "dry image". Application of the sample can generally be performed directly or indirectly, for example, via at least one capillary element. At least one image captured after sample application can typically also be referred to as a "wet image", even if the sample may be dry when the image is actually captured. The wet image can typically be captured after waiting at least a predetermined waiting time, for example, a waiting time of 5 seconds or more, in order to enable a detection reaction to occur. Thus, by way of example, the method can include waiting at least a predetermined minimum amount of time between imaging at least one arbitrary dry image and at least one wet image. This predetermined minimum amount of time can specifically be sufficient for a detection reaction to occur within the test strip. By way of example, the minimum amount of the waiting time can be at least 5 seconds.

[0035] This method includes determining an analyte concentration value from the color development of a test field. Accordingly, this method can be an analytical measurement that includes a change in at least one optical property of an optical test strip, and this change can be visually measured or determined by using a camera. Specifically, the analytical measurement can be, or can include, a color development reaction in the presence of at least one analyte to be determined. As used herein, the term "color development reaction" 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 special or customized meaning. This term can specifically, but not limited to, refer to a chemical, biological, or physical reaction in which the color, specifically the reflectance, of at least one element involved in the reaction changes as the reaction progresses. The color development can be detected by a mobile device, such as by a processor of the mobile device, and can be quantitatively evaluated, for example, by deriving at least one parameter for quantifying or characterizing the color development of the test field due to the presence of an analyte in a body fluid from at least one image. As an example, one or more of the above-described color coordinates can be used. Accordingly, a mobile device, specifically a processor of the mobile device, can be configured to determine a color change by determining a change in one or more color coordinates resulting from the detection reaction.

[0036] At least one analyte concentration value is determined from the color development of the test field. For this purpose, at least one image can be used. The analyte concentration value can be, for example, a numerical index of the result of an analytical measurement, such as indicating the concentration of at least one analyte in a sample, such as a blood glucose concentration.

[0037] Furthermore, as outlined above, in step i), the method includes providing, in the mobile device, at least one correlation for converting the color development of the test field into an analyte concentration value. As an example, the correlation can be provided in an electronic format such as a data storage device and / or via at least one interface of the mobile device. The correlation can be provided in various ways, such as, for example, by providing one or more parameters that define the correlation, such as parameters that define a linear relationship between the analyte concentration value and at least one item of information derived from at least one image, as outlined in more detail below. Other types of correlations are possible.

[0038] Accordingly, as used herein, the term "correlation" as used herein is a broad term and should be given its ordinary customary meaning to those of ordinary skill in the art and should not be limited to a special or customized meaning. Specifically, without limitation, this term can refer to a predetermined or determinable relationship between information derived from at least one image, such as color information or color change information, and at least one analytical measurement concentration value. To determine an analytical measurement result value from at least one image, for example, a correlation or a predetermined or determinable relationship between information derived from at least one image, such as color information or color change information, and at least one analytical measurement result value can be used. This correlation or predetermined or determinable relationship can be stored, for example, in the data storage device of the mobile device and / or in the processor of the mobile device. By way of example, the processor may be configured by software programming to derive at least one piece of information from at least one image, such as at least one color coordinate, and apply a predetermined or determinable relationship to the at least one piece of information. By way of example, a correlation that is a conversion function, conversion table, or look-up table can be determined empirically, for example, and stored in at least one data storage device of the mobile device by an app downloaded from software, particularly an app store. By way of example for deriving at least one information item, the processor may be programmed to preferably automatically recognize a test field or at least a part of the test field in the image, for example, by pattern recognition and / or other algorithms. As a result, the processor may be programmed to determine at least one information item, such as one or more color coordinates.Each at least one information item derived from at least one arbitrary blank or dry image can be used for normalization, for example, by dividing at least one information item derived from a wet image by at least one information item derived from a corresponding blank image, or by subtracting at least one information item derived from a wet image from at least one information item derived from a blank image, or vice versa. Other normalization methods are also feasible. As an example, a correlation, which is a conversion function, a conversion table, or a look-up table, can be determined empirically, for example, and can be stored in at least one data storage device of a mobile device by an app downloaded from software, especially an app store, etc.

[0039] As will be outlined in more detail below, the correlation can generally be determined by using an empirical or semi-empirical method, also generally referred to as "training". Training can include, for example, performing a plurality of measurements that convert color to analyte concentration values, and comparing the results to at least one known result, and / or using other preferred means such as at least one color reference card, which will be described in more detail below. As an example, as will be outlined in more detail below, training can also include the use of one or more artificial neural networks. As an example, a plurality of images can be used as inputs to one or more artificial neural networks for determining analyte concentration values by using reference information such as from one or more color reference cards and / or from one or more reference measurement values for feedback. Other training means are possible, such as by using a regression method such as linear regression to determine the parameters of the correlation, etc., and are generally known to those skilled in the art. As a result of the training, one or more parameters characterizing the correlation can be obtained.

[0040] The method may further include the step of displaying analyte concentration values on a display of a mobile device or the like. Additionally or alternatively, the method may include storing at least one analyte concentration value in at least one data storage device of the mobile device. Again, additionally and alternatively, the method may further include transmitting at least one analyte concentration value to another computer or the like, for example, for further evaluation, via at least one interface and / or via at least one data transmission network.

[0041] As further outlined above, step ii) includes providing at least one clearance information item within the mobile device, and the at least one clearance information item indicates a reliability level for the correlation. The at least one clearance information item can be provided, for example, in an electronic format such as within at least one data storage device and / or via at least one interface of the mobile device. The term "clearance information" as used herein is a broad term and should be given its ordinary and customary meaning to those of ordinary skill in the art and should not be limited to a special or customized meaning. This term can specifically, but not limitedly, refer to any information item that certifies and / or quantifies the reliability level for the correlation. Among them, the term "reliability level" as used herein is also a broad term and should be given its ordinary and customary meaning to those of ordinary skill in the art and should not be limited to a special or customized meaning. This term can specifically, but not limitedly, refer to one information item that quantifies at least one quality of the correlation. Thus, for example, the at least one information item can statistically quantify, such as, the accuracy of the conversion of the color development in the test field to the analyte concentration value by using the correlation. In particular, the reliability level can quantify the quality or accuracy of the conversion of the color development in the test field to the analyte concentration value. Specifically, the reliability level can be configured to, for example, quantitatively grade and / or evaluate the quality of the correlation such as the conversion of the color development in the test field to the analyte concentration value. Thus, the reliability level can indicate how well the conversion of the color development in the test field to the analyte concentration value is. For example, various means such as correlation coefficient, standard deviation, interval or degree of uncertainty are generally known to those of ordinary skill in statistics to quantify the accuracy of the conversion. The at least one clearance information item indicates the level of reliability, for example, by using one or more numerical values such as one or more boolean values and / or one or more digital values such as "sufficient" and "insufficient".As an example, as will be further described in more detail below, the clearance information item may be set to a specific value when at least one reliability level exceeds or falls below a predetermined threshold. As an example, one or more reliability thresholds can be used, and when the reliability level exceeds the reliability threshold, the clearance information item can be set to "sufficient", and if not, it can be set to "insufficient", or vice versa.

[0042] Experimentally or semi-experimentally, the reliability level for the correlation can be determined by converting the color development of the test field of each test sample into a calculated analyte concentration value for a plurality of test samples, and using a correlation for comparing these calculated analyte concentration values with the known analyte concentration values of each test sample, such as the known analyte concentration values determined by reference measurements such as laboratory measurements. The reliability level can be determined by statistical methods such as the regression method, and can be quantified, for example, by determining the standard deviation and the like, as will be recognized by those skilled in the art.

[0043] As outlined above, in step iii), if the clearance information item indicates a sufficient level of reliability for the correlation, the method includes providing an instruction by the mobile device to the user that imaging at least one image does not require the use of a color reference card. Thus, by way of example, the mobile device, specifically at least one processor of the mobile device, may be configured to evaluate at least one clearance information item, such as by evaluating whether the clearance information item indicates a sufficient or insufficient level of reliability for the correlation. If at least one clearance information item indicates a sufficient level of reliability for the correlation, an instruction that imaging at least one image does not require the use of a color reference card is provided to the user. Thus, by way of example, by default, the user may need to use a color reference card. However, by way of example, if at least one clearance information item indicates an insufficient level of reliability for the correlation, the method can simply proceed without providing an instruction to the user that imaging at least one image does not require imaging at least one image of at least one color reference card. Otherwise, as outlined above, it can be indicated to the user that imaging at least one image does not require imaging at least one image of at least one color reference card.

[0044] As used herein, the term "color reference card" as used herein is a broad term and should be given its ordinary customary meaning to those of ordinary skill in the art and should not be limited to a special or customized meaning. This term can specifically, but not limited to, refer to any item having at least one color reference field with known color characteristics or optical characteristics, such as having one or more colored fields with known color coordinates on at least one surface, disposed therein, or disposed thereon. By way of example, a color reference card can be a flat card comprising at least one substrate having at least one color reference field with known color coordinates disposed on at least one surface and / or therein. However, alternatively, a color reference card may also be fully or partially integrated into an optical test strip. At least one image of at least one color reference card may be fully or partially included by the above-described image of at least one portion of the optical test strip having a test field. Thus, by way of example, when imaging at least one image of a test field, at least one color reference card may be within the field of view of the camera, and thus, at least a portion of the color reference card can be seen within at least one image of at least one portion of the test field. By way of example, the optical test strip may be disposed on the color reference card, and / or the color reference card may comprise one or more windows, and the color reference card having one or more windows is disposed on the optical test strip such that the test field is visible through the window. However, alternatively, it is also possible to image separate images of at least one test field and the color reference card.

[0045] The use of a color reference card can in particular make it possible to correct camera-specific or device-specific variations in the color of at least one image of the test field. Thus, typically, the camera and / or mobile device applies one or more evaluation or pre-evaluation algorithms, such as gamma correction, to the image that must be taken into account when evaluating the image and determining at least one analyte concentration value without notifying the user. By using at least one color reference card with known optical properties, the mobile device can be set to calibrate and / or correct the image, and thus take into account the internal processes and / or characteristics of the camera and / or mobile device when determining or prior to determining at least one analyte concentration value. Furthermore, the influence of ambient light can be taken into account. Thus, if at least one clearance information item indicates that the reliability level for the correlation is insufficient, the use of at least one color reference card provides additional reliability and / or correction to the method of determining the concentration, thereby enhancing the accuracy and / or reliability of the analyte concentration value. Nevertheless, if at least one clearance information item indicates a sufficient reliability level, the use of at least one color reference card may be discarded and thus excluded, as it typically requires additional handling steps and / or additional inconvenience to the user. Thereby, the method makes it possible to increase the accuracy if necessary and if the training of the correlation has not yet been completed, but if the clearance information indicates a sufficient reliability level for the correlation and thus indicates that the training has been completed, the conversion of the color development of the test field to at least one analyte concentration value can be performed without using a color reference card.

[0046] At least one of the clearance information items outlined above may be provided in various ways. By way of example, at least one clearance information item may be set in a data storage device of the mobile device, and / or may be a variable provided to the mobile device via at least one wireless or at least one wire-bound interface. Thus, by way of example, at least one clearance information item can include at least one clearance information flag having a state indicating an insufficient reliability level and a state indicating a sufficient reliability level. Thus, by way of example, at least one clearance information flag can be, or can include, a binary variable that can be set according to at least one clearance information item and / or with respect to the sufficiency or insufficiency of the reliability level regarding the correlation.

[0047] The method can further include the following: iv) When the clearance information item indicates an insufficient reliability level for the correlation, providing an instruction by the mobile device to the user that imaging at least one image requires imaging at least one image of at least one color reference card.

[0048] Generally, an instruction that imaging at least one image does not require the use of a color reference card and / or an instruction that imaging at least one image does not require the use of a color reference card may be provided by the mobile device on the display of the mobile device. However, other instruction means such as audible instructions are also possible.

[0049] As outlined above, at least one clearance information item can specifically be stored in a data storage device of the mobile device. Thus, by way of example, a clearance information flag can be or can include at least one variable such as a binary variable, a bit, a character, etc. stored in at least one data storage device of the mobile device. Similarly, at least one correlation for converting the color development of a test field into an analyte concentration value may be stored in a data storage device of the mobile device such as the same data storage device used for storing at least one clearance information. Thus, by way of example, one or more parameters of a correlation such as a linear correlation, an offset parameter and / or a gradient parameter for converting at least one color information into at least one analyte concentration value can be stored in the data storage device.

[0050] At least one correlation for converting the color development of a test field into an analyte concentration can specifically include at least one of an algorithm, a correlation matrix, a coding curve or a look-up table. Thus, as outlined above, the correlation can include, by way of example, a coding curve such as a linear correlation coding curve, for example a coding curve characterized by an offset and a gradient, and at least one color information item derived from an image can be converted into at least one analyte concentration value by a linear transformation.

[0051] As an example, the algorithm can be based on the virtual reference device approach. Thus, as an example, an assembly of several mobile devices such as smartphones can be used to generate a reference relative relaxation. Based on the reference relative relaxation, for example, inclination and offset corrections such as inclination and offset corrections specific to a smartphone can be determined. The inclination and offset corrections can be used to equalize the behavior of two or more mobile devices in the corrected relative relaxation. Based on the corrected relative relaxation, a common code function and / or a code curve such as a mathematical function describing the relationship between the corrected relative relaxation and the analyte concentration can be determined and / or inferred.

[0052] At least one correlation for converting the color development of the test field into the analyte concentration can include the conversion of at least one color information item derived from at least one image into the analyte concentration. Thus, as outlined above, the mobile device may be set to derive at least one color information such as at least one color coordinate such as R, G, or B coordinates from at least one image, for example, by software programming of at least one processor. The at least one color information item can also indicate a change, for example, by considering the color coordinates of the dry image compared to the wet image.

[0053] In a further aspect of the present invention, a method for controlling an analytical measurement using at least one mobile device having a camera is disclosed. The method includes, as an example, the following steps that may be executed in a given order. However, it should be noted that different orders are also possible. Furthermore, it is also possible to execute one or more method steps once or repeatedly. Furthermore, it is possible to execute two or more method steps simultaneously or timely and repeatedly. The method can include additional method steps not described.

[0054] The method is I.) A data collection process, a. Performing a plurality of analytical measurements, wherein the analytical measurements include imaging at least a part of an optical test strip having a test field, at least in part, by using a camera, and further include imaging an image of at least one color reference card; performing a plurality of analytical measurements; b. Evaluating the plurality of analytical measurements and thereby determining at least one correlation, wherein the correlation is configured to convert the color development of the test field into an analyte concentration value without the need to use a color reference card; determining at least one correlation; c. Determining a reliability level for the correlation determined in step b; d. Setting at least one clearance information, wherein at least one clearance information item indicates the reliability level for the correlation; setting at least one clearance information including a data collection process, and II.) Executing a method for determining the concentration of an analyte in a body fluid according to the present invention, as disclosed in any one of the above-described embodiments and / or described in any one of the embodiments to be further described in detail below; including.

[0055] As used herein, the term "method for controlling analytical measurements" is a broad term and should be given its ordinary customary meaning to those skilled in the art and should not be limited to a special or customized meaning. Specifically, but not limited to, this term can refer to any method suitable for performing, optimizing, improving, starting, triggering, or processing one or more of the above-defined analytical measurements, and at least one analytical measurement includes using at least one mobile device having at least one camera.

[0056] As used herein, the term "data collection process" is also referred to as "training process", "data collection period", or "setup process", and is a broad term that should be given its ordinary and customary meaning to those of ordinary skill in the art and should not be limited to a special or customized meaning. Specifically, without limitation, this term can refer to a process that can occur over a long period of time, during which data is collected and used for one or more purposes of control, such as improving at least one analytical measurement or optimizing one or more analytical measurements. By way of example, as described in further detail below, the data collection process can include connecting information regarding analytical measurements and utilizing this information to improve the level of confidence in the correlations described above.

[0057] As outlined above, data collection process I.) includes performing a plurality of analytical measurements in step a. As defined above, analytical measurements, also referred to as "analyte measurements", generally, but not limited to, can refer to the quantitative and / or qualitative determination of at least one analyte in any sample or aliquot, particularly body fluids. For further options, reference can be made to the definitions given above. The analytical measurements in step a. or at least some of these analytical measurements include imaging at least a portion of an optical test strip having a test field by using a camera, and further include imaging an image of at least one color reference card. Thus, the analytical measurements during the data collection process or at least some of the analytical measurements of the data collection process include the above-described process using a color reference card. These analytical measurements can be used as training measurements for the purposes of the data collection process. For details of the color reference card and possible options for using the color reference card, reference can be made to the above description. Specifically, the image of the optical test strip and the image of the color reference card may be separate images, or at least a portion of the color reference card and at least a portion of the test field can be seen in one and the same image, thereby forming, for example, separate sub-images of one and the same image.

[0058] Furthermore, as outlined above, step b. of the data collection process involves evaluating a plurality of analytical measurements, specifically training measurements including the use of color reference cards, thereby determining at least one correlation, the correlation being configured to convert the color development of the test field into analyte concentration values without the need to use color reference cards. For possible solutions and embodiments of the correlation, reference can be made to the description of the method for determining the analyte given above. Furthermore, various options, also at least partially described above and generally known to those skilled in the art, are available for determining the correlation. Thus, generally, an image of at least one color reference card can provide reference data, and the correlation can be selected to take into account the reference data, such as by converting at least one information item derived from an image of at least a part of the optical test strip having the test field into analyte concentration using the correlation, thereby enabling future analytical measurements to be performed using the correlation without using reference data derived from color reference cards.

[0059] In the data collection process of step b, information derived from an image of at least one color reference card can be used as reference information. Thus, by way of example, color reference information can be derived from an image of at least one color reference card, for each of the analytical measurements, or for at least some of a plurality of analytical measurements of the data collection process. By way of example, color reference information derived from an image can provide standardized information, the color reference card having, for example, one or more known colors, and at least one color reference information item can be derived from the image. As a result, the corresponding color reference information derived from a corresponding image of the test field can be compared with the color reference information derived from the image of the color reference card, thereby making it possible to correct for color changes induced by the camera and / or mobile device. Thus, by way of example, when at least one piece of color information derived from an image of the test field is equal to a color information item derived from an image of at least one color reference field of the color reference card, and this color reference field is known to correspond to a particular analyte concentration of an analyte in a sample of body fluid, a particular correlation, such as a conversion factor and / or a linear correlation and / or another type of correlation such as a mathematical relationship for converting a color information item of an image of the test field to a corresponding analyte concentration value, can be derived for converting the color development of the test field, i.e., for converting at least one piece of color information derived from an image of the test field to an analyte concentration value. By way of example, regression analysis can be used. Additionally or alternatively, an artificial neural network may be used, as outlined in more detail below. Thus, the correlation can be specifically configured to convert at least one piece of color information derived from an image of an optical test strip having a test field to an analyte concentration value. Thus, by way of example, the correlation can be or can include one or more of direct reference color comparison, interpolation, and absolute color determination.

[0060] Furthermore, as outlined above, in step c., a reliability level for the correlation determined in step b. is determined. As an example, this reliability level can be derived by using statistical analysis, as described above. Thus, as an example, statistical analysis can be used to derive the degree of uncertainty of the correlation from the evaluation of the training analysis measurements in step a. and from those evaluations in step b. As an example, the standard deviation is used or can be a similar value known to those skilled in the art to account for the degree of uncertainty when using a correlation, for example when using regression analysis, to convert at least one color information item to a corresponding analysis concentration value. Similar reliability information can be derived when using any other classification method such as artificial neural networks and / or decision trees, nearest neighbors, etc. to determine the correlation.

[0061] Also, as described above, in step d., at least one clearance information is set, and the at least one clearance information indicates the reliability level for the correlation derived in step b. As an example, the at least one clearance information item can simply indicate whether the reliability level indicates sufficient or insufficient reliability, and as described above in the context of a method for determining the concentration of an analyte in a body fluid, the method for determining the concentration of the analyte can be performed without using a color reference card after reaching a sufficient reliability level. As an example, as will be outlined in more detail below, the at least one clearance information item may be set according to a step function, and when the at least one clearance information item reaches at least one threshold, the step function changes from a level indicating an insufficient reliability level to a level indicating a sufficient reliability level or vice versa.

[0062] As further discussed above, the method of controlling the analytical measurement also includes, in addition to the data collection process and / or the training process in step I.), in step II.), performing a method for determining the concentration of an analyte in a body fluid according to the present invention, i.e., according to any one of the above-disclosed embodiments and / or according to any one of the embodiments of this method further disclosed in detail below. It should be noted that the training process in step I.) may be partially executed outside the mobile device, and step II.) is executed using the mobile device. Thus, by way of example, specifically, one or more of steps b., c., or d. of the training process in step I.) may be fully or partially executed by at least one computer or computer system separate from the mobile device. Thus, by way of example, the training data may be generated by using the mobile device to perform step a., and for example, the training data or its derived data including an image or at least one information item derived from the image may be transmitted wirelessly, for example, to at least one evaluation computer, and one or more or all of steps b., c., and d. may be at least partially executed by the evaluation computer. At least one clearance information item may be returned by the evaluation computer to the mobile device. Further, information characterizing the correlation or correlations, such as data characterizing the offset and / or slope of a linear correlation, etc., may also be returned from the evaluation computer to the mobile device. However, in parallel or alternatively, the training measurements may also be fully or partially evaluated by the mobile device itself, for example, by the mobile device fully or partially performing one or more of steps b., c., and d.

[0063] The training process of step I.) can precede the active measurement process of step II.), i.e., before performing step II.), a plurality of training samples can be evaluated in step I.). However, additionally or alternatively, the training may be performed iteratively, and one or more iterations of step I.) are performed after performing step II.) at least once. Further, steps I.) and II.) can utilize at least partially the same analytical measurement, i.e., the analytical measurement of step I.)a., and can also function at least partially as an analytical measurement for the purpose of determining the concentration of the analyte in step II.), which enables the use of "actual" measurement data for training purposes as long as the measurement involves imaging an image of at least one color reference card. As soon as at least one clearance information item indicates a sufficient level of reliability for the correlation, the training may be stopped, or, for example, at regular or irregular intervals, to avoid and / or account for the effects of degradation, electronic shifts or other changes in settings, etc., the color reference card may be used to further perform training steps to improve the level of reliability for the correlation and / or to confirm the level of reliability of the correlation.

[0064] The analytical measurements performed in step a. may all be performed under the same measurement conditions, or may be performed under various different measurement conditions. Thus, by way of example, different measurement conditions such as different lighting conditions, different sample conditions, different temperatures, different analyte concentrations, etc. can be used. These different measurement conditions can help improve the correlation by taking into account factors other than the concentration of the analyte in the sample of the body fluid, such as, for example, the hematocrit value, sample temperature, lighting, etc. Specifically, not only when using an artificial neural network, but also when using other correlation evaluation and determination means such as regression methods, these factors other than the concentration of the analyte in the sample of the body fluid can be taken into account, and as a result, the correlation is a function or correlation taking these factors into account.

[0065] As outlined above, in step b., the multiple analytical measurements of step a. are evaluated. This evaluation can utilize various means such as statistical analysis or the use of artificial neural networks, as outlined above. Specifically, the evaluation can also search for patterns or similarities within the image in order to take these patterns or similarities into account to generate a correlation. Thus, generally, step b. can include evaluating the image captured in step a., thereby identifying the similarity of at least a portion of the image with reference to at least one of the similar patterns or similar variables, and setting the similarity regarding specific conditions of the analytical measurement. The specific conditions of the analytical measurement can include, by way of example, at least one specific analyte concentration, at least one specific range of analyte concentrations, at least one specific type of mobile device, for example, at least one specific type of mobile phone, manufacturer, location, for example geographical location, and date, for example the image capture date, and at least one additional piece of information of the like.

[0066] Correspondingly, the reliability level can also depend on specific conditions of the analytical measurement. Thus, the reliability level can take into account factors such as, by way of example, a specific analyte concentration or range of analyte concentrations and / or a specific type of mobile phone. However, additionally or alternatively, the reliability level can also be specific to certain lighting conditions and the like. Thus, generally, the reliability level determined in step c. can specifically be a specific reliability level for at least one of the specific conditions of the analytical measurement of a specific type of mobile phone. By way of example, several reliability levels can be determined, such as the reliability level for different conditions of the analytical measurement. By way of example, different reliability levels can be determined for different ranges of analyte concentrations and / or different reliability levels can be determined for different types of mobile devices. Thus, by way of example, the reliability level of one type of mobile phone can be sufficient, while the reliability level of another type of mobile phone can be insufficient, indicating the need for further training.

[0067] As a result, the identification of the similarity of at least a part of the images can include the identification of the similarity in a group of images. Thus, by way of example, the evaluation in step b. can include grouping the analytical measurements and / or the images according to certain features and / or according to other grouping criteria such as the conditions of the analytical measurements and / or the grouping criteria derived from the images.

[0068] Step b. may include using at least one artificial neural network as outlined above. As used herein, the term "artificial neural network" is a broad term and should be given its ordinary customary meaning to those skilled in the art and should not be limited to a special or customized meaning. Specifically, without limitation, by considering one or more examples, this term can refer to a computer, computer system, computer network, or computer program that can learn to perform one or more tasks without being programmed by task-specific rules. Generally, this term can refer to a system capable of implementing a deep learning process. Specifically, at least one artificial neural network can include at least one self-learning or machine learning system. By way of example, an artificial neural network can include at least one input layer having a plurality of nodes, at least one output layer having a plurality of nodes, and optionally one or more hidden layers between the input layer and the output layer. Nodes between adjacent layers can be interconnected by signal connections. An artificial neural network can specifically simulate the learning process of the human brain. At least one artificial neural network can specifically be or include at least one convolutional neural network. As will be understood by those skilled in the art, an artificial neural network can specifically be used for identifying or analyzing images and / or for identifying features or similarities of multiple images. Thus, by way of example, at least one artificial neural network may be used to identify similarities of at least a part of an image. Similarities can specifically refer to at least one of similar patterns, similar variables, and similarities regarding specific conditions of analytical measurements.

[0069] As outlined above, several means for setting at least one clearance information item may generally be possible, such as using a comparison with at least one threshold value and / or applying at least one step function. Thus, by way of example, step d. can include comparing the reliability level with at least one predetermined threshold value, specifically a predetermined minimum value, and based on the comparison, setting at least one clearance information item to a value indicating a reliability level insufficient for measurement without using a color reference card, or to a value indicating a reliability level sufficient for measurement without using a color reference card, respectively.

[0070] In a further aspect of the present invention, a mobile device is disclosed that has at least one camera and is configured to perform a method for determining the concentration of an analyte in a body fluid according to any one of the above-disclosed embodiments and / or according to any one of the embodiments further disclosed in detail below, etc., according to the present invention. Specifically, the mobile device can comprise at least one processor. As used herein, the term "processor" is a broad term and should be given its ordinary customary meaning to those skilled in the art and should not be limited to a special or customized meaning. This term can specifically refer to, but is not limited to, any logic circuit configured to perform the basic operations of a computer or system, and / or generally, a device configured to perform computational or logical operations. In particular, the processor can be configured to process the basic instructions that drive a computer or system. By way of example, the processor can include at least one arithmetic logic unit (ALU), at least one floating-point unit (FPU) such as a numeric co-processor or a numeric processor, a plurality of registers, specifically registers configured to supply operands to the ALU and store the operation results, and memory such as L1 and L2 cache memories. In particular, the processor can be a multi-core processor. Specifically, the processor can be or include a central processing unit (CPU). Additionally or alternatively, the processor can be or include a microprocessor, and thus, specifically, the elements of the processor can be included in one single integrated circuit (IC) chip. Additionally or alternatively, the processor can be or comprise one or more application-specific integrated circuits (ASICs) and / or one or more field-programmable gate arrays (FPGAs) and / or one or more tensor processing units (TPUs) and / or one or more dedicated machine learning optimization chips, etc.

[0071] The processor can be specifically configured to execute and / or support the method method steps, for example, by software programming. Specifically, the processor can be configured to support imaging at least one image of at least a part of an optical test strip having a test field by using a camera. The processor can be further configured to determine at least one analyte concentration value from the color development of the test field, such as by evaluating the image, deriving at least one information item from the image, and converting the at least one information item into at least one analyte concentration value. The processor can be further configured to support one or more or all of method steps i), ii), and iii), such as by providing and / or receiving a correlation, for example, for providing an answer, or for receiving and evaluating at least one clearance information item, and for further providing the user with an indication that imaging at least one image does not require the use of a color reference card. The processor can be further configured to support applying a sample to the test strip, for example, by providing user guidance in a visual format and material, or in an audible format. The processor can be further configured to support imaging at least one image, for example, by automatically detecting the test strip or a part thereof within the field of view, and / or by prompting the user to image the image.

[0072] In a further aspect of the present invention, a system for controlling an analytical measurement is disclosed. The system comprises at least one mobile device having at least one camera. The system is further configured to execute a method for controlling an analytical measurement using at least one mobile device according to any one of the above-disclosed embodiments and / or according to any one of the embodiments further disclosed below in more detail. Thus, the system can generally comprise a plurality of components configured to interact. Thus, in addition to at least one mobile device, the system can specifically comprise at least one evaluation device configured to execute at least steps b., c. and d. The evaluation device may be connected to the mobile device, in particular wirelessly, for example via the Internet and / or a wireless communication network. By way of example, the evaluation device can comprise one or more of computers and computer systems, such as one or more of a server, a server system or a cloud-based server or server system configured by software programming to execute at least steps b., c. and d. The evaluation device can be further configured to specifically receive wirelessly the results generated in step a., such as at least one information item derived from an image, such as an image and / or a part thereof, and / or at least one color information item and / or at least one derived analytical measurement value thereof. Next, the evaluation device can be further configured to transmit at least one clearance information item to the mobile device, for example wirelessly. For reception and transmission, the evaluation device can comprise at least one receiver and / or at least one transmitter.

[0073] The evaluation device may be separated from the mobile device, for example, remotely from the mobile device. As outlined above, the evaluation device can be configured to communicate with the mobile device, specifically, by a wireless method. The evaluation device can include at least one of a server device and a cloud-based evaluation device. The evaluation device can be configured to transmit at least one clearance information item to the mobile device.

[0074] In a further aspect of the present invention, there is disclosed a computer program and a computer-readable storage medium that fully or partially support or execute one or both of the methods according to the present invention, i.e., a method for determining the concentration of an analyte and / or a method for controlling an analytical measurement, in one or more of, for example, one or more of the above-disclosed embodiments and / or one or more of the embodiments disclosed in further detail below. Thus, in a further aspect, there is a computer program comprising instructions which, when executed by a mobile device having a camera, specifically by a processor of the mobile device, cause the mobile device to execute a method for determining the concentration of an analyte in a body fluid according to the present invention, such as a method according to any one of the above-disclosed embodiments and / or a method according to any one of the embodiments disclosed in further detail below. Similarly, the computer-readable storage medium comprises instructions which, when executed by a mobile device having a camera, specifically by a processor of the mobile device, cause the mobile device to execute a method for determining the concentration of an analyte in a body fluid according to the present invention, such as a method according to any one of the above-disclosed embodiments and / or a method according to any one of the embodiments disclosed in further detail below. Further, there is disclosed a computer program comprising instructions which, when the program is executed by a system for controlling an analytical measurement according to the present invention, such as a method according to any one of the above-disclosed embodiments and / or a method according to any one of the embodiments disclosed in further detail below, cause the system to execute a method for controlling an analytical measurement according to the present invention, such as a method according to any one of the above-disclosed embodiments and / or a method according to any one of the embodiments disclosed in further detail below.Similarly, when executed by a system for controlling analytical measurements according to the present invention, such as according to any one of the above-disclosed embodiments and / or according to any one of the embodiments further disclosed in detail below, a computer-readable storage medium including instructions for causing the system to execute a method for controlling analytical measurements according to the present invention, such as according to any one of the above-disclosed embodiments and / or according to any one of the embodiments further disclosed in detail below, is disclosed.

[0075] As used herein, the term "computer-readable storage medium" can specifically refer to non-transitory data storage means such as a hardware storage medium storing computer-executable instructions. The computer-readable data carrier or storage medium can specifically be or include a storage medium such as random access memory (RAM) and / or read-only memory (ROM).

[0076] The computer program may also be embodied as a computer program product. As used herein, the computer program product can refer to a program as a tradable product. The product generally exists in any format such as a paper format or on a computer-readable data carrier and / or a computer-readable storage medium. Specifically, the computer program product may be distributed on a data network.

[0077] The method and apparatus according to the present invention provide many advantages over similar methods and apparatuses known in the art. Thus, compared to the methods and apparatuses known in the art, the methods and apparatuses described herein can improve the flexibility and handling of measurements. Specifically, the flexibility and handling of measurements can be improved by enabling the use of a vast number of mobile devices for analyte measurement determination and control. The vast number of mobile devices that can be enabled for use in analytical measurements by the present invention can be made even more numerous due to the continuous release of new smartphones and the like. In particular, the method and apparatus according to the present invention can enable the use of these mobile devices by performing a training process, for example, by generating training data. In particular, generating training data while performing analyte measurements may be preferable to complex and time-consuming dedicated tests for generating training data in a laboratory environment.

[0078] Furthermore, the method and apparatus can enhance measurement safety compared to known methods and apparatuses, for example, by using a reference card. Specifically, using the reference card according to the present invention, such as generating reference card measurement data, can be used to train artificial neural networks such as image-based neural nets. Thus, the analytical measurements according to the present invention can be made safer than measurements known in the art, for example, by enabling adaptation of the method and apparatus to actual situations and conditions such as smartphone-specific and scene-specific aspects.

[0079] Furthermore, for example, after the initial data collection process, the measurement performance can be improved by the method and apparatus due to the ability to determine the analyte measurement concentration independently of the reference card. Thus, once sufficient data has been collected and / or the artificial network has been trained, the reference card may no longer be required. The simplicity of no longer requiring a reference card to determine the analyte concentration can further improve the user experience.

[0080] In summary, without further excluding possible embodiments, the following embodiments can be envisioned:

[0081] Embodiment 1: A method for determining the concentration of an analyte in a body fluid, comprising using a mobile device having a camera, the method comprising imaging at least one image of at least a portion of an optical test strip having a test field using the camera, the method further comprising determining at least one analyte concentration value from the color development of the test field, the method comprising: i) providing, in the mobile device, at least one correlation for converting the color development of the test field into an analyte concentration value; ii) providing, in the mobile device, at least one clearance information item, the at least one clearance information item indicating a reliability level for the correlation; iii) when the clearance information item indicates a sufficient reliability level for the correlation, providing to the user an indication by the mobile device (112) that imaging at least one image does not require the use of a color reference card. A method comprising the above.

[0082] Embodiment 2: The method according to Embodiment 1, wherein the at least one clearance information item comprises a clearance information flag having a state indicating an insufficient reliability level and a state indicating a sufficient reliability level.

[0083] Embodiment 3: The method according to embodiment 1 or 2, further comprising providing, by the mobile device (112), an instruction to the user to capture at least one image of at least one color reference card when the clearance information item indicates an insufficient confidence level for the correlation.

[0084] Embodiment 4: The method according to any one of embodiments 1 to 3, wherein the instruction is provided by the mobile device on the display of the mobile device.

[0085] Embodiment 5: The method according to any one of embodiments 1 to 4, wherein at least one clearance information item is stored in the data storage device of the mobile device.

[0086] Embodiment 6: The method according to any one of embodiments 1 to 5, wherein at least one correlation for converting the color development of the test field into an analyte concentration value is stored in the data storage device of the mobile device.

[0087] Embodiment 7: The method according to any one of embodiments 1 to 6, wherein at least one correlation for converting the color development of the test field into an analyte concentration comprises at least one of an algorithm, a correlation matrix, a coding curve, or a look-up table.

[0088] Embodiment 8: The method according to any one of embodiments 1 to 7, wherein at least one correlation for converting the color development of the test field into an analyte concentration comprises the conversion of at least one color information item derived from at least one image into an analyte concentration.

[0089] Embodiment 9: A method for controlling an analytical measurement using at least one mobile device having a camera, comprising: I.) A data collection process, comprising: a. Performing a plurality of analytical measurements, the analytical measurements including at least in part imaging at least a portion of an optical test strip having a test field by using a camera, and further including imaging an image of at least one color reference card, performing a plurality of analytical measurements; b. Evaluating the plurality of analytical measurements and thereby determining at least one correlation, the correlation being configured to convert the color development of the test field into an analyte concentration value without the need to use a color reference card, determining at least one correlation; c. Determining a reliability level for the correlation determined in step b; d. Setting at least one clearance information, at least one clearance information item indicating the reliability level for the correlation, setting at least one clearance information including a data collection process, and II.) Executing a method for determining the concentration of an analyte in a body fluid according to any one of Embodiments 1 to 8 including a method.

[0090] Embodiment 10: The method according to Embodiment 9, wherein the analytical measurements performed in step a are performed at least in part under various different measurement conditions.

[0091] Embodiment 11: The method according to Embodiment 9 or 10, wherein step b includes evaluating the image captured in step a and thereby identifying the similarity of at least a part of the image, the similarity specifically referring to at least one of a similar pattern, a similar variable, and a similarity regarding specific conditions of the analytical measurement.

[0092] Embodiment 12: The method according to embodiment 11, wherein specific conditions of the analytical measurement include at least one of at least one specific analyte concentration, at least one specific range of analyte concentrations, at least one specific type of mobile device, manufacturer, location, such as a geographical location, and date, such as an image capture date, and at least one further piece of information.

[0093] Embodiment 13: The method according to embodiment 12, wherein the reliability level determined in step c. is a specific reliability level for at least one of the specific conditions of the analytical measurement, specifically for a specific type of mobile device.

[0094] Embodiment 14: The method according to any one of embodiments 11 to 13, wherein identifying the similarity of at least a part of the image includes identifying the similarity in a group of images.

[0095] Embodiment 15: Step b. specifically uses an artificial neural network to identify the similarity of at least a part of the image, where the similarity refers to at least one of a similar pattern or a similar variable, and includes using the artificial neural network and setting the similarity regarding specific conditions of the analytical measurement. The method according to any one of embodiments 9 to 14.

[0096] Embodiment 16: Step d. includes comparing the reliability level with a predetermined threshold, specifically a predetermined minimum value, and based on the comparison, setting at least one clearance information item to a value indicating an insufficient reliability level for measurement without using a color reference card or a value indicating a sufficient reliability level for measurement without using a color reference card. The method according to any one of embodiments 9 to 15.

[0097] Embodiment 17: A mobile device having at least one camera, configured to execute the method for determining the concentration of an analyte in a body fluid according to any one of embodiments 1 to 8.

[0098] Embodiment 18: The mobile device according to Embodiment 17, further comprising at least one processor.

[0099] Embodiment 19: A system for controlling analytical measurements, comprising at least one mobile device having at least one camera, and configured to execute the method according to any one of Embodiments 9 to 16.

[0100] Embodiment 20: The system according to Embodiment 19, comprising at least one evaluation device configured to execute at least steps b., c. and d.

[0101] Embodiment 21: The system according to Embodiment 20, wherein the evaluation device is separated from the mobile device and configured to communicate with the mobile device.

[0102] Embodiment 22: The system according to Embodiment 20 or 21, wherein the evaluation device comprises at least one of a server device and a cloud-based evaluation device.

[0103] Embodiment 23: The system according to any one of Embodiments 20 to 22, wherein the evaluation device is configured to transmit at least one clearance information item to the mobile device.

[0104] Embodiment 24: A computer program comprising instructions which, when executed by a mobile device having a camera, specifically by a processor of the mobile device, cause the mobile device to execute the method according to any one of Embodiments 1 to 8.

[0105] Embodiment 25: A computer-readable storage medium comprising instructions which, when executed by a mobile device having a camera, specifically by a processor of the mobile device, cause the mobile device to execute the method according to any one of Embodiments 1 to 8.

[0106] Embodiment 26: A computer program including instructions, which, when the program is executed by the system according to any one of Embodiments 19 to 23, cause the system to execute the method according to any one of Embodiments 9 to 16.

[0107] Embodiment 27: A computer-readable storage medium including instructions that, when executed by the system according to any one of Embodiments 19 to 23, cause the system to execute the method according to any one of Embodiments 9 to 16.

Brief Description of the Drawings

[0108] Any further optional features and embodiments are preferably disclosed in more detail in the following description of the embodiments, in conjunction with the dependent claims. Among them, each optional feature may be realized in an independent manner and in any feasible combination, as understood by those skilled in the art. The scope of the present invention is not limited by the preferred embodiments. The embodiments are schematically shown in the drawings. Among them, the same reference numerals in these drawings refer to the same or functionally equivalent elements. The drawings are as follows:

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Best Mode for Carrying Out the Invention

[0109] In FIG. 1, an embodiment of a system 110 and a mobile device 112 for controlling analytical measurements is shown in a perspective view. The mobile device 112 has at least one camera 114. Further, the mobile device 112 is configured to execute a method 116 for determining the concentration of an analyte in a body fluid. The method 116 for determining the concentration of an analyte in a body fluid may also be referred to as determination method 116. The determination method 116 will be described with reference to exemplary embodiments shown in the flowcharts shown in FIGS. 2 and 3. The system 110 comprises at least one mobile device 112 having at least one camera 114. The system 110 is further configured to execute a method 118 for controlling analytical measurements. The method 118 for controlling analytical measurements may also be referred to as control method 118. The control method 118 will be described with reference to exemplary embodiments shown in the flowcharts shown in FIGS. 4 and 6.

[0110] The system 110 can further comprise at least one evaluation device 120. The evaluation device 120 may specifically be separated from the mobile device 112 and may be configured to communicate with the mobile device 112 shown in FIG. 1 by two arrows pointing in opposite directions. In particular, at least one clearance information item can be transmitted by the evaluation device 120 to the mobile device 112. By way of example, the evaluation device 120 can comprise at least one of a server device 130 and a cloud-based evaluation device 132. The mobile device can further comprise at least one processor 122. The processor 122 can specifically support image acquisition of the mobile device 112, such as imaging at least a part of an optical test strip 124 having a test field 126. A color reference card 128 is further shown in FIG. 1.

[0111] A method 116 for determining the concentration of an analyte in a body fluid includes using a mobile device 112 having a camera 114. The method 116 further includes imaging at least one image of at least a portion of an optical test strip 124 having a test field 126 by using the camera 114. The method 116 further includes determining at least one analyte concentration value from the color development of the test field 126. Further still, the method 116 includes the following steps which can be specifically executed in a predetermined order. Further, different orders may also be possible. Two or more method steps may be executed completely or partially simultaneously. Further, one, two or more, or all method steps may be executed once or repeatedly. The method 116 can include additional method steps not enumerated. The method steps of the method 116 are as follows: i) Providing, in the mobile device 112 (indicated by reference numeral 134), at least one correlation for converting the color development of the test field 126 into an analyte concentration value; ii) Providing, in the mobile device 112 (indicated by reference numeral 136), at least one clearance information item, where the at least one clearance information item indicates a reliability level for the correlation; and iii) When the clearance information item indicates a sufficient reliability level for the correlation, providing, by the mobile device 112, an indication to the user that imaging at least one image does not require the use of a color reference card 128.

[0112] Furthermore, as exemplarily shown in FIG. 3, method 116 can include a branch point 140. The branch point 140 can indicate a conditional query, such as making a determination between a first branch 142 and a second branch 144. For example, the conditional query may utilize a clearance information item. The clearance information item can include, for example, a clearance information flag having a state indicating an insufficient reliability level and a state indicating a sufficient reliability level. Thus, the clearance information item can include boolean information such as "sufficient" ("y") or "insufficient" ("n"). As an example, if the clearance information item indicates an insufficient reliability level for the correlation, the first branch 142 can indicate the insufficient reliability level and can lead to step iv) (indicated by reference numeral 146), where the mobile device 112 provides an instruction to the user that imaging at least one image of at least one color reference card 128 is required for imaging at least one image. The second branch 144 indicates a sufficient reliability level and thus leads to step iii) 138.

[0113] A method 118 for controlling an analytical measurement using at least one mobile device 112 having a camera 114 can include the following steps, which can be specifically executed in a predetermined order. Furthermore, different orders are also possible. Two or more method steps can be executed completely or partially simultaneously. Furthermore, one, two or more, or all method steps can be executed once or repeatedly. Method 118 can include additional method steps not enumerated. The method steps of method 118 are as follows: I.) A data collection process (indicated by reference numeral 148), a. Executing a plurality of analytical measurements (indicated by reference numeral 150), where the analytical measurements include imaging at least a part of an optical test strip 124 having a test field 126, at least partially by using the camera 114, and further including imaging an image of at least one color reference card 128, and executing a plurality of analytical measurements; b. Evaluating a plurality of analytical measurements (indicated by reference sign 152) and thereby determining at least one correlation, wherein the correlation is configured to convert the color development of the test field 126 into an analyte concentration value without the need to use the color reference card 128, and determining at least one correlation; c. Determining a reliability level for the correlation determined in step b. (indicated by reference sign 154); d. Setting at least one clearance information item (indicated by reference sign 156), wherein the at least one clearance information item indicates a reliability level for the correlation, and setting at least one clearance information item including, a data collection process, and II.) Executing a method 116 for determining the concentration of an analyte in a body fluid.

[0114] In particular, step b. can include using an artificial neural network to specifically identify the similarity of at least a part of the image. Specifically, the similarity can refer to at least one of a similar pattern or a similar variable. In detail, the similarity can be related to specific conditions of the analytical measurement. The analytical measurement, specifically the method for determining the concentration of an analyte in a body fluid, can be exposed to various influencing factors as exemplarily shown in FIG. 5. The external factors 158 that may affect the analytical measurement can be or can include handling modes 160, sample variables 162, and further external variables 164. By way of example, the handling mode 160 can be or can include a temporal mode, such as timing, and an angle or spatial orientation, such as the angle or spatial orientation of the optical test strip 124 and the camera 114. The sample variable 162 can be or can include characteristics of the sample that affect the analytical measurement, such as, for example, hematocrit (hct), blood volume, and interference such as maltose. The further external variable 164 can include, for example, temperature and humidity.

[0115] Further influencing factors can affect the analytical measurement, for example, by occurring or interfering when taking at least one image in steps iii) 138 and a. 150. In particular, taking at least one image for extracting, for example, the RGB data 165 of the image may include at least two sub-steps such as image acquisition 166 and image processing 168 that may be affected by different influencing factors. Thus, by way of example, image acquisition 166 can include camera setting variables 170 such as exposure time, ISO setting, RGGB gain, etc., and optical variables 172 such as sensor types including aperture, focal length, reproduction scale, pixel resolution, and Bayer sensor, etc., which may be affected by the nature or characteristics of the mobile device 112 such as a smartphone. Further, image acquisition 166 may be affected by image scene variables 174 such as ambient light, for example, intensity and / or spectral distribution, background, for example, background illumination or color, and the optical test strip 124 to background ratio, for example, the pixel ratio between the pixels representing the optical test strip 124 and the pixels representing the background. The nature or characteristics of the mobile device 112 that may affect image processing 168 can be or can include tone mapping 176, color conversion matrix 178, and demosaicing process 180.

[0116] FIG. 6 shows a flowchart of an embodiment of a method for controlling an analytical measurement. Here, by way of example, the black dot or circle in the upper left corner of FIG. 6 indicates the starting point of method 118 for controlling an analytical measurement. As illustratively shown in FIG. 6, method 118 can start with determining a second branch 144 where branch point 140 can indicate a clearance information flag having a state indicating a “sufficient” (“y”) reliability level, and can lead to step iii) 138. Subsequently, imaging 182 of at least one image of at least a portion of an optical test strip 124 having a test field 126 by using a camera 114 can be performed without the need to use a color reference card 128. After step 182, specifically, step 184 of determining at least one analyte concentration value from the color development of test field 126 can follow.

[0117] The first branch 142 can indicate a clearance information flag having a state indicating an "insufficient" ("n") reliability level and can lead to the next branch point 186 that decides between the first branch 188 and the second branch 190. The first branch 142 can be a "default" branch or default setting such that a user can be required to use the default color reference card 128. The branch point 186 can include a confirmation of cloud access, such as a determination of whether the mobile device 112 can have access to the data collected in the data collection process I.) 148, which is stored, for example, in the cloud-based evaluation device 132. Thus, the first branch 188 can indicate "cloud access" ("y") and can lead to a further branch point 192 that decides between the first branch 194 and the second branch 196 regarding whether the data collected in the data collection process I.) 148 indicates a "sufficient" ("y") or "insufficient" ("n") reliability level. Specifically, the query 192 can include a clearance confirmation of only the strip of the mobile device 112, such as by determining whether the reliability level determined in step c. 154 is sufficient or insufficient. As shown, the first branch 194 can indicate a "sufficient" ("y") reliability level and thus can lead to the step 198 of setting the clearance information flag to a state indicating a sufficient reliability level, and subsequently, as described above, can lead to the execution of the method 116 that specifically leads to step iii) 138. The second branch 190 can indicate "no cloud access". The second branch 196 can indicate the data collected in the data collection process I.) 148 to indicate an "insufficient" reliability level.

[0118] Both the second branch 190 and the second branch 196 can reach step iv) 146. Thus, for example, the results can be the same in both cases where the mobile device 112 is considered "without cloud access" and where the data collected in the data collection process I.) 148 exhibits an "insufficient" reliability level. Specifically, a method 116 for determining the concentration of an analyte in a body fluid can be executed, and imaging 182 of at least one image of at least a part of an optical test strip 124 having a test field 126 by using a camera 114 requires using at least one reference card 128. Similarly, after step 182, a step 184 of determining at least one analyte concentration value from the color development of the test field 126 can follow. In particular, the execution of the method 116 where imaging 182 of at least one image requires imaging at least one image of at least one color reference card 128 may further include storing measurement details such as metadata, for example, an image, at least one intermediate step, and at least one smartphone metadata. Specifically, the at least one intermediate step can specifically be or include intensity correction, color correction, and color reference card quality verification. Further, the at least one smartphone metadata can be or include information regarding the smartphone used to image at least one image, such as the manufacturer, model type, automatic settings, for example, the automatic settings used, and hardware information. The measurement details can be used in the data collection process I.) 148, specifically in step b. 152, as exemplarily shown by the arrow in FIG. 6 pointing from the method 116 to step I.) 148. By way of example, measurement details from various measurements can be used, for example, by using different embodiments of the color reference card 128.

[0119] FIG. 7 shows a partial flowchart of a method 118 for controlling an analytical measurement. Specifically, the details of step I.) 148 are shown. Here too, the black dot or circle in the upper left corner of FIG. 7 indicates the starting point. First, on the evaluation device 120, the collected data can be clustered or sorted according to the mobile device 112 used to generate the specific collected data, such as according to the phone model type (indicated by reference numeral 202). Thereafter, for each mobile device 112, for example, for each phone model type, the intensity information can be clustered or sorted (indicated by reference numeral 204). Further, for each intensity information and each mobile device 112, for example, for each intensity information and each smartphone, the color information can be clustered or sorted (indicated by reference numeral 206). Specifically, the clustered intensity information and color information can be suitable for revealing the ambient illumination and image recording information unique to each mobile device 112 (indicated by reference numeral 208). As an example, the ambient illumination and image recording black boxes of each smartphone can be revealed respectively.

[0120] Furthermore, the clustered data can receive a branch point 210 that determines between a first branch 212 and a second branch 214 based on the amount of the clustered data. Specifically, the amount of data for each cluster is checked, and it can be determined whether the amount of data is "sufficient" ("y") or "insufficient" ("n").

[0121] Specifically, for image analysis and the like, an artificial neural network (ANN) can be used. In principle, the optical and / or colorimetric test strip algorithm may be based on the use of an ANN. However, in order to train an image-based neural net such as, for example, any type of convolutional neural network (CNN), sufficient data such as data exceeding a predetermined threshold amount, for example, data covering multiple situations such as blood glucose levels of multiple influencing factors, should be available, for example, independently combined and utilized. Therefore, the second branch 214 indicating an "insufficient" amount of data may lead to the method being aborted (indicated by reference numeral 216).

[0122] For example, when the amount of data is considered to be "sufficient", such as determining the first branch 212, the CNN can be trained according to the clustered data (indicated by reference numeral 218). Thereafter, the CNN quality can be verified by separate test data (indicated by reference numeral 220). Thereafter, the performance of a neural net such as the CNN can receive another conditional query or branch point 222, for example, the performance of the CNN neural net can be verified. A successful verification can lead to step 198 of setting the clearance information flag to a state indicating a sufficient reliability level. If the verification fails, the method can be aborted again (216).

[0123] As an example, reference card measurement data available from any reference card-based application release can be used to partially train an image-based neural net. In particular, the reference card data may be reused to train smartphone-specific aspects as well as scene-specific aspects.

Explanation of symbols

[0124] 110 System 112 Mobile device 114 Camera 116 Decision method 118 Control method 120 Evaluation device 122 Processor 124 Optical test strip 126 Test field 128 Color reference card 130 Server device 132 Cloud-based evaluation device 134 Step i) 136 Step ii) 138 Step iii) 140 Branch point 142 First branch 144 Second branch 146 Step iv) 148 Step I.) 150 Step a. 152 Step b. 154 Step c. 156 Step d. 158 External factor 160 Handling mode 162 Sample variable 164 External variable 165 RGB data of image 166 Image acquisition 168 Image processing 170 Camera setting variable 172 Optical variable 174 Image scene variable 176 Tone mapping 178 Color conversion matrix 180 Demosaicing process 182 Capture at least one image of at least a part of an optical test strip having a test field by using a camera 184 Determine at least one analyte concentration value from the color development of the test field 186 Branch point - Confirmation of cloud access 188 First branch indicating "Cloud access" 190 Second branch indicating "No cloud access" 192 Branch point 194 First branch indicating a "sufficient" reliability level 196 Second branch indicating an "insufficient" reliability level 198 Set the clearance information flag to a state indicating a sufficient reliability level 200 Store measurement details 202 Cluster the data collected according to the mobile device 204 Cluster the intensity information 206 Cluster the color information 208 Reveal the ambient lighting and image recording information 210 Branch point - Check data volume 212 First branch indicating "sufficient" 214 Second branch indicating "insufficient" 216 Abort this method 218 Train a convolutional neural network according to the clustered data 220 Verify the quality of the neural network with separate test data 222 Branch point - Verification

Claims

Claim 1 A method for determining the concentration of an analyte in a body fluid, the method comprising using a mobile device (112) having a camera (114), the method comprising imaging at least one image of at least a portion of an optical test strip (124) having a test field (126) by using the camera (114), the method further comprising determining at least one analyte concentration value from the color development of the test field (126), the method comprising: i) providing, in the mobile device (112), at least one correlation for converting the color development of the test field (126) into the analyte concentration value, the term correlation referring to a predetermined or determinable relationship between information derived from the at least one image and at least one analytical measurement concentration value, providing at least one correlation; ii) providing, in the mobile device (112), at least one clearance information item, the at least one clearance information item indicating a reliability level for the correlation, the clearance information item being an information item that quantifies the reliability level for the correlation, the reliability level being determined and quantified by an experimental or semi-experimental statistical method according to the color development, providing at least one clearance information item; iii) when the clearance information item indicates a sufficient reliability level for the correlation, providing, by the mobile device (112), an indication to the user that imaging the at least one image does not require the use of a color reference card (128); comprising wherein the sufficient reliability level is according to the color development in the strip, a method, wherein the color reference card (128) comprises at least one color reference field having known color characteristics or optical characteristics that enable correction of camera-specific or device-specific variations. Claim 2 The method according to claim 1, wherein the at least one clearance information item comprises a clearance information flag having a state indicating an insufficient reliability level and a state indicating a sufficient reliability level. Claim 3 iv) further comprising providing, by the mobile device (112), an instruction to the user to capture at least one image of at least one color reference card (128) when the clearance information item indicates an insufficient confidence level for the correlation, the method according to claim 1 or 2.

4. The method according to any one of claims 1 to 3, wherein the at least one correlation for converting the color development of the test field (126) into the analyte concentration includes converting at least one color information item derived from the at least one image into the analyte concentration.

5. A method for controlling an analytical measurement using at least one mobile device (112) having a camera (114), comprising: I.) A data collection process, comprising: a. performing a plurality of analytical measurements, the analytical measurements including capturing at least a partial image of an optical test strip (124) having a test field (126) at least in part by using the camera (114), and further including capturing an image of at least one color reference card (128); b. evaluating the plurality of analytical measurements to thereby determine at least one correlation, the correlation being configured to convert the color development of the test field (126) into the analyte concentration value without the need to use the color reference card (128); c. determining a confidence level for the correlation determined in step b., wherein the confidence level is pre-determined and quantified by an experimental or semi-experimental statistical method according to the color development; d. setting at least one clearance information, the at least one clearance information item indicating the confidence level for the correlation with respect to the color development; including a data collection process, and II.) performing the method for determining the concentration of an analyte in a body fluid according to any one of claims 1 to 4, including a color reference card (128) that enables correction of camera-specific or device-specific variations in at least one image of the color of the test field.

6. Step b. includes evaluating the image captured in step a., thereby identifying the similarity of at least a part of the image, the method according to claim 5.

7. The method according to claim 6, wherein identifying the similarity of at least a part of the image includes identifying the similarity in a group of the images.

8. The method according to any one of claims 5 to 7, wherein step b. includes using an artificial neural network.

9. Step d. includes comparing the reliability level with a predetermined threshold value, and based on the comparison, setting the at least one clearance information item to a value indicating an insufficient reliability level for measurement without using the color reference card (128), or setting it to a value indicating a sufficient reliability level for measurement without using the color reference card (128), respectively, the method according to any one of claims 5 to 8.

10. A mobile device (112) having at least one camera (114), configured to execute the method for determining the concentration of an analyte in a body fluid according to any one of claims 1 to 4.

11. A system (110) for controlling an analytical measurement, comprising at least one mobile device (112) having at least one camera (114), configured to execute the method according to any one of claims 5 to 9.

12. The system (110) according to claim 11, wherein the system (110) comprises at least one evaluation device (120) configured to execute at least steps b., c., and d.

13. The system (110) according to claim 12, wherein the evaluation device (120) is separated from the mobile device (112) and configured to communicate with the mobile device (112), and the evaluation device (120) is configured to transmit the at least one clearance information item to the mobile device (112).

14. A computer program including instructions, which when the program is executed by a mobile device (112) having a camera (114), causes the instructions to cause the mobile device (112) to execute the method according to any one of claims 1 to 4.

15. A computer program comprising instructions which, when the program is executed by a system (110) according to any one of claims 11 to 13, cause the system (110) to perform the method according to any one of claims 5 to 9.

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