Method for determining an item of information about a quality of an image

Abstract

The present invention relates to a computer-implemented method for determining an item of information about a quality of an image, wherein the item of information about the quality of the image relates to a suitability of the image to be used in an analytical measurement method for detecting at least one analyte in a bodily fluid, wherein the at least one analyte is detected from at least one reagent test region of an optical test element; by using a mobile device having at least one camera, the method comprising: (i) receiving at least one image captured by using the camera, wherein the image comprises at least a part of the reagent test region of the optical test element having the sample of the bodily fluid applied thereto; (ii) deriving, from the image received in step (i), independently from one another, an image quality value QVx for each of at least two different image quality parameters QPx, selected from a pre-determined set of image quality parameters QP1 to QPn, wherein the at least two of the image quality parameters QPx, independently from one another, are configured to assess at least one of: a color characteristic of an image, and a spatial characteristic of an image; (iii) comparing each of the image quality values QVx derived in step (ii), independently from one another, to one or more pre-determined threshold values TV(QPx); and, based on the comparing, assigning, independently from one another, an individual numerical quality assessment value IAV(QPx) to each of the at least two different image quality parameters QPx of step (ii), wherein the individual numerical quality assessment values are selected from a pre-determined set of individual numerical quality assessment values; and (iv) deriving, from the individual numerical quality assessment values IAV(QPx) assigned in step (iii), an overall image quality assessment value OAV; comparing said overall image quality assessment value OAV to an overall pre-determined threshold value OTV; and, based on the comparing, determining the item of information about the quality of the image received in step (i), wherein step iv) is performed only if none of the individual numerical quality assessment values IAV(QPx) assigned in step (iii) to the image quality parameters QPx of step (ii) corresponds to a pre-determined category of an insufficient quality in respect of any of the image quality parameters QPx.

Description

[0001] Method for determining an item of information about a quality of an image

[0002] Technical Field

[0003] The present invention relates to a method for determining an item of information about a quality of an image, by using at least one mobile device having a camera; and to a method for determining a concentration of an analyte in a bodily fluid, which method makes use of the method for determining an item of information about a quality of an image. Further, the invention relates to a mobile device having a camera for carrying out the methods, to a kit comprising a mobile device having a camera, to computer programs and computer-readable storage media. The methods, mobile devices, computer programs and storage media specifically may be used in medical diagnostics, for example in order to qualitatively or quantitatively detect one or more analytes in bodily fluids, such as for detecting glucose or virus load in blood or in interstitial fluid.

[0004] Background art

[0005] In the field of medical diagnostics, in many cases, one or more analytes have to be detected in samples of a bodily fluid, such as blood, interstitial fluid, urine, saliva or other types of bodily fluids. Examples of analytes to be detected are glucose, triglycerides, lactate, cholesterol or other types of analytes, like one or more types of viruses, typically present in these body fluids. According to the concentration and / or the presence of the analyte, an appropriate treatment may be chosen, if necessary.

[0006] Generally, devices and methods known to the skilled person make use of test elements comprising one or more test chemicals, which, in the presence of the analyte to be detected, are capable of performing one or more detectable detection reactions, such as optically detectable detection reactions. With regard to the test chemicals comprised in test elements, reference may be made e.g. to J. Hoenes et al.: The Technology Behind Glucose Meters: Test Strips, Diabetes Technology & Therapeutics, Volume 10, Supplement 1, 2008, S-10 to S-26. Other test chemicals, e.g. for detecting viruses, particularly by using lateral flow assays, are known to the person skilled in the art.

[0007] In analytical measurements, specifically analytical measurements based on color formation reactions, one technical challenge resides in the evaluation of the color change which is due to the detection reaction. Besides using dedicated analytical devices, such as handheld analytical meters, such as blood glucose meters, the use of generally available electronics such as smart phones and portable computers or other mobile devices has become more and more popular over the recent years.

[0008] As opposed to laboratory measurements and measurements performed by using dedicated analytical measurement devices, when using mobile computing devices such as smart phones, various additional influences need to be taken into account, such as for example, lighting conditions and positioning aspects, which may be rather difficult to take into account in view of a vast variety of situations where such measurements may be performed. In order to improve the reliability of results of analyte detection in these cases nonetheless, it is therefore desirable to further improve the accuracy when performing such measurements.

[0009] When using a mobile device for an analytical measurement based on a color formation reaction, wherein an image of an optical test element is captured by the camera of the mobile device, the objective quality of the image captured is an important factor. Many different aspects may have an impact on the objective quality of such an image, including in particular ambient lighting conditions and camera settings during the capturing of an image. These influencing factors may negatively impact image characteristics like noise (also referred to as “image noise”), homogeneity of illumination, ill-defined intensity spots, color resolution, spatial resolution, and the like.

[0010] Furthermore, when performing analytical measurements by using a built-in camera of a mobile device to capture an image of an optical test element, in order to satisfy accuracy requirements and to also account for device-specific characteristics, usually either a color reference card (providing for a plurality of reference colors) is used, or such analytical measurements are restricted to pre-calibrated or pretested mobile devices, e.g. a limited number of smartphone types or smartphone models, such that a minimum level of image quality may be ensured. If no color reference card is used, a pre-testing of specific smartphone models may ensure a minimum level of measurement performance, e.g. in terms of reliability or accuracy; but such a pre-testing cannot ensure a proper functionality for each individual measurement. Especially, when taking into account impact factors like automatic image acquisition settings, various scenes to be captured, different ambient light conditions, etc., any influences of the afore -mentioned impact factors on image compression may result in varying image quality, and therefore in varying measurement performances. Generally, in an approach to take into account quality aspects of images, some factors known to have an impact on image quality have been considered to some extent in non-diagnostic fields.

[0011] For example, in the context of facial recognition of movers, US11475596B2 describes a method which i.a. comprises: receiving first images in an image sequence from a camera device; determining a first portion of a target object detected in the first images; determining a confidence level for a second portion of the target object in the first images, and adjusting a configuration of the camera device; processing the image sequence, to determine the predicted location of the target object identified in the image sequence based on movement of the target object in the image sequence; and adjusting the configuration of the camera device. In this method, one or more quality parameters of images may be determined, and whether or not they pass a quality test; said quality tests may evaluate whether or not images include faces of movers that result in successful facial recognition thereof, e.g. whether the images are overexposed or underexposed, or whether the mover in the images is blurry or not blurry.

[0012] Another example, in the context of a transaction of a financial document, is US10803431B2 describing a method of presenting a graphical user interface on a portable device for walking a user through an image capturing process for improving a visibility level of images to identify magnetic ink character recognition code for performing a transaction of a financial document, the method i.a. comprising: capturing, using an image sensor of the portable device, a plurality of images of a video feed depicting a financial document; deriving a visibility level of said financial document in said respective image, based on an alignment; extracting a threshold value from a predefined array of a plurality of thresholds corresponding to a plurality of image quality parameters, said extracted threshold corresponding to said alignment; comparing said visibility level to said extracted threshold; calculating, based on an outcome of said analysis, at least one user operation required to change a tilt orientation of said portable device for increasing said visibility level. The image quality parameters may include brightness parameters, orientation parameters, and / or sharpness parameters; sufficient visibility level may be evaluated based on a match with a predefined threshold; further, sufficient visibility level may be evaluated based on a match with a set of thresholds, each for another of values each set according to another of the image quality parameters.

[0013] US10171773B2 relates to a computer system for dynamic video image management, wherein, with respect to a dynamic video image, a set of dynamic image quality factors is collected; and wherein, based on the set of dynamic image quality factors, a set of display parameter values, of a set of display parameters for a set of computing assets to benefit the set of dynamic image quality factors with respect to the dynamic video image, is determined. The set of dynamic image quality factors may be evaluated with respect to a set of image quality factor benchmarks, which may include a threshold level of quality. Exemplarily, a user may be a participant in a video conference call, the user and their surroundings may appear unnatural with a high intensity of color; by considering dynamic image quality factors, which may include a white balance factor and a saturation factor, the set of display parameters may allow the facial-related features of the user to appear more natural with a lower intensity of color.

[0014] US2022 / 0317050A1 relates to a method of adjusting a setup used in an analytical method of determining a concentration of an analyte in a body fluid based on a color formation reaction in an optical test strip, the analytical method comprising using a mobile device having a camera, wherein the adjustment method comprises: a) carrying out a plurality of analyte measurement attempts with the mobile device set in a standard measuring mode, the analyte measurement attempts comprising: capturing images of an optical test strip, checking for the fulfillment of one or more measurement rejection criteria, and when one or more measurement rejection criteria are fulfilled, rejecting the measurement attempt and logging the rejection events in the memory, and b) analyzing the logged rejection events in an error analysis and, based on the result, adjusting the setup by placing the device (i) in a reduced measuring mode wherein one or more of the measurement rejection criteria are deactivated, and / or (ii) in an enhanced measurement mode wherein during a measurement attempt corrective feedback is provided to the user.

[0015] Summarizing, factors impacting image quality, as far as they have been taken into account in some technology fields of the art, have been evaluated one by one only, i.e. each factor separately, and generally independent from one another.

[0016] Despite the advantages involved in using mobile computing devices for the purpose of performing an analytical measurement, one of the remaining technical challenges still is to appropriately evaluate a level of quality of an image captured by the mobile computing device.

[0017] Problem to be solved It is therefore desirable to provide devices and methods which at least partially address the above- mentioned challenges. Specifically, it is desirable to provide devices and methods which allow for a reliable evaluation of a level of quality of an image captured by a mobile device, such that accurate measurement results may be obtained by a mobile-based detection of an analyte in a bodily fluid. Moreover, the devices and methods provided should reduce a number of failed attempts to perform a mobile-based detection of an analyte in a bodily fluid, wherein the failed attempts are attributable, at least partially, to a poor level of image quality.

[0018] Summary

[0019] This problem is addressed by a method, specifically a computer-implemented method, for determining an item of information about a quality of an image by using at least one mobile device having at least one camera, and specifically having at least one processor; further, by an analytical measurement method, specifically a computer-implemented analytical measurement method, for detecting at least one analyte in a sample of a bodily fluid by using a mobile device having at least one camera, and specifically having at least one processor; even further, by a mobile device having at least one camera, and specifically having at least one processor; by a kit comprising a mobile device and an optical test element; and by computer programs and computer-readable storage media; with the features of the independent claims. Advantageous embodiments which might be realized in an isolated fashion or in any arbitrary combinations are listed in the dependent claims.

[0020] As used in the following, the terms “have”, “comprise” or “include” or any arbitrary grammatical variations thereof are used in a non-exclusive way. Thus, these terms may both refer to a situation in which, besides the feature introduced by these terms, no further features are present in the entity described in this context and to a situation in which one or more further features are present. As an example, the expressions “A has B”, “A comprises B” and “A includes B” may both refer to a situation in which, besides B, no other element is present in A (i.e. a situation in which A solely and exclusively consists of B) and to a situation in which, besides B, one or more further elements are present in entity A, such as element C, elements C and D or even further elements.

[0021] Further, it shall be noted that the terms “at least one”, “one or more”, or similar expressions indicating that a feature or element may be present once or more than once typically will be used only once when introducing the respective feature or element. In the following, in a plurality of cases, when referring to the respective feature or element, the expressions “at least one” or “one or more” will not be repeated, non-withstanding the fact that the respective feature or element may be present once or more than once.

[0022] Further, as used in the following, the terms „preferably“, „more preferably“, „particularly“, „more particularly“, „specifically“, „more specifically“, or similar terms are used in conjunction with optional features, without restricting alternative possibilities. Thus, features introduced by these terms are optional features and are not intended to restrict the scope of the claims in any way. The invention may, as the skilled person will recognize, be performed by using alternative features. Similarly, features introduced by „in an embodiment of the invention“ or similar expressions are intended to be optional features, without any restriction regarding alternative embodiments of the invention, without any restrictions regarding the scope of the invention and without any restriction regarding the possibility of combining the features introduced in such way with other optional or non-optional features of the invention.

[0023] In a first aspect, the present invention relates to a method, specifically a computer-implemented method, for determining an item of information about a quality of an image, by using a mobile device having at least one camera and, specifically, at least one processor. Herein, the item of information about the quality of the image relates to a suitability of the image to be used in an analytical measurement method for detecting at least one analyte in a bodily fluid. The at least one analyte is detected from at least one reagent test region of an optical test element. The at least one reagent test region is configured for performing at least one optical detection reaction in the presence of the analyte.

[0024] The method comprises the following steps which, as an example, may be performed in the given order. It shall be noted, however, that a different order may also be possible. Further, it may also be possible to perform one or more of the method steps once or repeatedly. Further, it may be possible to perform two or more of the method steps simultaneously or in a timely overlapping fashion. The method may comprise further method steps which are not listed.

[0025] The method comprises, in a first step i), receiving, specifically by the processor of the mobile device, at least one image captured by using the camera of the mobile device. The image comprises at least a part of, specifically all of, the reagent test region of the optical test element having the sample of the bodily fluid applied thereto. The method further comprises, in step ii): determining, specifically by the processor of the mobile device, from the image received in step (i), independently from one another, an image quality value QVx for each of at least two different image quality parameters QPx. The image quality parameters QPx are selected from a pre-determined set of image quality parameters QP1 to QPn, wherein x is a numerical value from 1 to n, and wherein n is the total number of image quality parameters contained in the pre-determined set of image quality parameters. Herein the at least two of the image quality parameters QPx, independently from one another, are configured to assess at least one of: a color characteristic of an image, e.g. of the image received in step i), and a spatial characteristic of an image, e.g. of the image received in step i).

[0026] The method further comprises, in step iii): comparing, specifically by the processor of the mobile device, each of the image quality values QVx derived in step (ii), independently from one another, to one or more pre-determined threshold values TV(QPx).

[0027] Step iii) further comprises: Based on the comparing, assigning, specifically by the processor of the mobile device, independently from one another, an individual numerical quality assessment value IAV(QPX) to each of the at least two different image quality parameters QPx of step (ii). Herein, the individual numerical quality assessment values are selected from a pre -determined set of individual numerical quality assessment values.

[0028] The method further comprises, in step iv): deriving, specifically by the processor of the mobile device, from the individual numerical quality assessment values IAV(QPX) assigned in step (iii), an overall image quality assessment value OAV.

[0029] Step iv) further comprises: Comparing, specifically by the processor of the mobile device, said overall image quality assessment value OAV to an overall pre-determined threshold value OTV.

[0030] Step iv) still further comprises: Based on the comparing, determining, specifically by the processor of the mobile device, the item of information about the quality of the image received in step (i). Step iv) of the method is performed only if none of the individual numerical quality assessment values IAV(QPX) assigned in step (iii) to the image quality parameters QPx of step (ii) corresponds to a predetermined category of an insufficient quality in respect of any of the image quality parameters QPx.

[0031] The term “an item of information about a quality of an image”, as used herein, is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to any piece of information which may be representative for a given level or degree of quality of an image, specifically of an electronic image, e.g. an image captured by a camera of a mobile device. Particularly, the “quality of an image” may refer to one or more physical parameters or attributes characteristic of the image; e.g. the “quality of an image” may refer to a set of, specifically pre-defined, parameters, wherein the parameters may be quantifiable. More specifically, the parameters may quantify a level or a degree of quality of an image, e.g. in terms of numerical values indicating a specific level or degree of quality of the image. Further, the physical parameters which may be employed in order to specify the “quality of an image” specifically may include, without limitation, any parameters relating to a color characteristic of an image, and / or to a spatial characteristic of an image; more specifically, the physical parameters may include one or more of the following: illumination inhomogeneity, maximum illumination, noise, and sharpness. In this regard, other physical parameters may be contemplated as well. Herein, the “item of information about the quality of the image” relates to a suitability of the image to be used in an analytical measurement method for detecting at least one analyte in a bodily fluid.

[0032] The term “suitability of the image to be used in an analytical measurement method for detecting at least one analyte in a bodily fluid”, as used herein, is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an indication that an image can be properly used in an analytical measurement method which involves detection of at least one analyte in a bodily fluid. In other words, if an image is suitable to be used in such an analytical measurement method, then the image, specifically an electronic image, may be used as an input in the analytical measurement method, and may be further evaluated in order to obtain an analytical measurement result. The “suitability of the image” may be represented by Boolean information, specifically by Boolean information indicating that the image, specifically a quality of the image, is either sufficient for use of the image in an analytical measurement method for detecting at least one analyte in a bodily fluid, or indicating that the image, specifically a quality of the image, is not sufficient for use of the image in the analytical measurement method.

[0033] The term “analytical measurement”, also referred to as “determining a concentration of an analyte in a bodily fluid”, as used herein, is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a quantitatively and / or qualitatively determination of at least one analyte in an arbitrary sample or aliquot of bodily fluid. For example, the bodily fluid may comprise one or more of blood, interstitial fluid, urine, saliva or other types of body fluids, particularly blood. The result of the analytical measurement, or of the determining of the concentration, as an example, may be a concentration of the analyte and / or the presence or absence of the analyte to be determined. Specifically, as an example, the analytical measurement may be a blood glucose measurement, thus the result of the analytical measurement may for example be a blood glucose concentration. In particular, an analytical measurement result value may be determined by the analytical measurement.

[0034] Consequently, the term “analyte concentration value”, often also referred to as “analytical measurement result value”, as used herein, is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a numerical indication of an analyte concentration in a sample.

[0035] The at least one analyte, as an example, may be or may comprise one or more specific chemical compounds and / or other parameters. As an example, one or more analytes may be determined which take part in metabolism, such as blood glucose. Additionally or alternatively, other types of analytes or parameters may be determined, e.g. a pH value, a virus, such as influence virus type A or type B, SARS-CoV-2, etc. Without narrowing the scope, the invention specifically may be described with respect to blood glucose measurements. It shall be noted, however, that the present invention may also be used for other types of analytical measurements using test elements, in particular for analytes other than blood glucose, specifically analytes to be detected by using a lateral flow assay, such as various types of viruses. In an analytical measurement method for detecting at least one analyte in a bodily fluid, the analyte is detected from a reagent test region of an optical test element. The reagent test region is configured for performing at least one optical detection reaction in the presence of the analyte. Specifically, the reagent test region is adapted for application of a sample of the bodily fluid, and the reagent test region is adapted to undergo, at least partially, an optical detection reaction, when the sample of the bodily fluid is applied to the reagent test region. The reagent test region may also be referred to as a “test field” herein. The optical detection reaction may specifically comprise “a color formation reaction”, specifically involving a color-change. More specifically, a color-change involved in an optical detection reaction may result in a change of a color of a reagent test region, e.g. from yellow to green, from white to grey or black, or from white to red or purple. Additionally or alternatively, the optical detection reaction may specifically comprise a development of a color which has not been present before the optical detection reaction was performed; this type of optical detection reaction typically is observed with lateral flow assays. In such lateral flow assays, usually one or more bands, or lines, can be observed in the reagent test region after sample application, and usually after a pre-determined period of waiting time, in case of a positive test result. The terms “optical detection reaction” and “color formation reaction” specifically may refer, without limitation, to a chemical, biological or physical reaction during which a color, specifically a reflectance, of at least one element involved in the reaction, changes with the progress of the reaction.

[0036] The term “optical test element”, as used herein, is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an arbitrary element or device configured for performing an optical detection reaction, specifically involving a color change . The optical test element may also be referred to as test strip or test element, wherein all three terms may refer to the same element. The optical test element has a reagent test region containing at least one test chemical for detecting at least one analyte. The optical test element, as an example, may comprise at least one substrate, such as at least one carrier, with the at least one reagent test region applied thereto or integrated therein. In particular, the optical test element may further comprise one or more reference areas, such as a white field and / or a black field. Additionally or alternatively, the substrate or carrier itself may be or may comprise such a reference area. As an example, the at least one carrier may be strip-shaped, thereby rendering the test element a test strip. These test strips are generally widely in use and available. One test strip may carry a single test field or a plurality of test fields having identical or different test chemicals comprised therein. Furthermore, lateral flow assays are widely used for various analytes to be detected, e.g. for viruses. Such lateral flow assays usually are comprised of a housing carrying a membrane, a reagent test region being integrated or impregnated into the membrane. The architectures and designs of such lateral flow assays are generally known to the person skilled in the art.

[0037] As further used herein, the term “reagent test region” (also referred to as a “test field” herein) is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a coherent amount of the test chemical, such as to a field or a membrane, e.g. a field or a membrane of round, polygonal or rectangular shape, having one or more layers of material, with at least one layer of the test field having the test chemical comprised therein. With regard to the test chemicals comprised in optical test strips, as an example reference is made to J. Hoenes et al.: The Technology Behind Glucose Meters: Test Strips, Diabetes Technology & Therapeutics, Volume 10, Supplement 1, 2008, S-10 to S-26. Other types of test chemistry are possible and may be used for performing the present invention.

[0038] The method, as outlined above, comprises using at least one mobile device having at least one camera. The term “mobile device” as used herein is a broad term it is to be given its ordinary and customary meaning to person of ordinary skill in the art and is not to be limited to a special customized meaning. The term specifically may refer, without limitation, to a mobile electronics device, more specifically to a mobile communication device, such as a cell phone and / or a smart phone. Additionally or alternatively, the mobile device may also refer to a notebook, a tablet computer or another type of portable computer having at least one camera. Thus, generally, the mobile device may be selected from the group consisting of: a cell phone having at least one camera, specifically a smart phone; a portable computer having at least one camera, specifically at least one of a notebook and a tablet computer. The mobile device may specifically be used in the method for determining an item of information about a quality of an image, as described herein, particularly for performing said method, and / or in an analytical measurement method, particularly for performing said analytical measurement method, as will be outlined in further detail below. The mobile device, besides the at least one camera, may comprise further elements, such as one or more processors and one or more displays or screens. As an example, the mobile device may comprise more than one camera, such as a front camera, e.g. arranged on the same side as the one or more displays or screens, and a back camera, e.g. arranged on a side opposing the side on which the front camera is arranged. As mentioned above, the mobile device may further comprise at least one processor. The term “processor” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an arbitrary logic circuitry configured for performing basic operations of a computer or system, and / or, generally, to a device which is configured for performing calculations or logic operations. In particular, the processor may be configured for processing basic instructions that drive the computer or system. As an example, the processor may comprise at least one arithmetic logic unit (ALU), at least one floating-point unit (FPU), such as a math coprocessor or a numeric co-processor, a plurality of registers, specifically registers configured for supplying operands to the ALU and storing results of operations, and a memory, such as an LI and L2 cache memory. In particular, the processor may be a multi-core processor. Specifically, the processor may be or may comprise a central processing unit (CPU). Additionally or alternatively, the processor may be or may comprise a microprocessor, thus specifically the processor’s elements may be contained in one single integrated circuitry (IC) chip. Additionally or alternatively, the processor may be or may 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 unit (TPU) and / or one or more chip, such as a dedicated machine learning optimized chip, or the like. The processor specifically may be configured, such as by software programming, for performing one or more operations of determination of an image quality and / or one or more operations of detection of an analyte, as will be outlined in further detail below.

[0039] The term “camera” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a device having at least one imaging element configured for recording or capturing spatially resolved one-dimensional, two-dimensional or even three-dimensional optical data or information. As an example, the camera may comprise at least one camera chip, such as at least one CCD chip and / or at least one CMOS chip configured for recording images. As used herein, without limitation, the term “image” specifically may relate to data recorded by using a camera, such as a plurality of electronic readings from the imaging device, such as the pixels of the camera chip. The camera, besides the at least one camera chip or imaging chip, may comprise further elements, such as one or more optical elements, e.g. one or more lenses. As an example, the camera may be a fix-focus camera, having at least one lens which is fixedly adjusted with respect to the camera. Alternatively, however, the camera may also comprise one or more variable lenses which may be adjusted, automatically or manually. The invention specifically shall be applicable to cameras as usually used in mobile applications such as notebook computers, tablets or, specifically, cell phones such as smart phones. Thus, specifically, the camera may be part of a mobile device which, besides the at least one camera, comprises one or more data processing devices such as one or more data processors. Other cameras, however, are feasible.

[0040] As outlined above, the method comprises, in step i), receiving at least one image captured by using the camera of the mobile device. The image comprises at least a part of, specifically all of, the reagent test region of the optical test element having the sample of the bodily fluid applied thereto. The term “receiving at least one image” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to one or more of imaging, image recording, image acquisition, image capturing. The term “receiving at least one image” may comprise receiving and / or capturing a single image and / or a plurality of images such as a sequence of images. For example, the receiving and / or capturing of the image may comprise recording continuously a sequence of images such as a video or a movie. The receiving and / or capturing of the at least one image may be initiated by the user action or may automatically be initiated, e.g. once the presence of the at least one object within a field of view and / or within a predetermined sector of the field of view of the camera is automatically detected. These automatic image acquisition techniques are known e.g. in the field of automatic barcode readers, such as from automatic barcode reading apps. The receiving and / or capturing of the images may take place, as an example, by acquiring a stream or “life stream” of images with the camera, wherein one or more of the images, automatically or by user interaction such as pushing a button, are stored and used as the at least one first image or the at least one second image, respectively. The image acquisition may be supported by a processor of the mobile device, and the storing of the images may take place in a data storage device of the mobile device.

[0041] The receiving and / or capturing of the at least one image comprises receiving and / or capturing at least one image with having the sample of the bodily fluid applied to the test strip and, further and optionally, such as before capturing the image with the sample applied to the test strip, receiving and / or cap- taring at least one image without having the sample of the body fluid applied to the test strip. The latter image specifically may be used for comparative purposes and may also be referred to as a “blank image” or “dry image”. The sample application generally may take place, as an example, directly or indirectly, e.g. via at least one capillary element. The at least one image received and / or captured after sample application may typically also be referred to as the “wet image”, even though the sample may have dried when the image is actually captured. The wet image typically may be received and / or taken after having waited for at least a predetermined waiting time, such as after five seconds or more, in order to allow for the detection reaction to take place. Thus, as an example, the method may comprise, between receiving and / or taking the at least one optional dry image and the at least one wet image, waiting for at least a predetermined minimum amount of time. This predetermined minimum amount of time specifically may be sufficient for a detection reaction to take place in the test strip. As an example, the minimum amount of waiting time may be at least 5 s, or up to 15 min in the case of SARS- CoV-2 rapid antigen LFA (lateral flow assay) self-tests for use by a patient.

[0042] The analytical measurement method for detecting an analyte in a sample of a bodily fluid may comprise detecting the presence or absence of the analyte. Additionally or alternatively, the analytical measurement method may comprise determining the analyte concentration, particularly an analyte concentration value, from color formation of the reagent test region. Thus, the analytical measurement method may include a change of at least one optical property of an optical test element, such as an optical test strip or a lateral flow assay, which change may be measured or determined visually by using the camera. Specifically, the analytical measurement may be or may comprise a color formation reaction in the presence of the at least one analyte to be determined. The term “color formation reaction” as used herein has been defined herein above. The color formation may be detected by the mobile device, such as by a processor of the mobile device, and may be evaluated qualitatively, and specifically quantitatively, such as by deriving, from the at least one image, at least one parameter quantifying or characterizing the color formation of the test field due to the presence of the analyte in the bodily fluid. To this end, one or more specific color coordinates may be used. Thus, the mobile device and specifically the processor of the mobile device may be configured for determining a color change by determining a change of one or more color coordinates taking place due to the detection reaction.

[0043] The presence or absence of the analyte, or the concentration of the analyte, particularly the analyte concentration value, is determined from a color formation of the test field. For this purpose, the at least one image is used. The analyte concentration value, as an example, may be a numerical value indicator of a result of the analytical measurement, such as indicative of the concentration of at least one analyte in the sample, such as a blood glucose concentration or virus load.

[0044] The method may further comprise a step of displaying the item of information about a quality of an image, such as on a display of the mobile device. Additionally or alternatively, the method may comprise storing the item of information about a quality of an image in at least one data storage device of the mobile device. Again additionally and alternatively, the method may further comprise transmitting the item of information about a quality of an image via at least one interface and / or via at least one data transmission network, such as to another computer, e.g. for further evaluation or processing.

[0045] Accordingly, in the first aspect, the present invention particularly relates to method, specifically a computer-implemented method, for determining an item of information about a quality of an image, wherein the item of information about the quality of the image relates to a suitability of the image to be used in an analytical measurement method for detecting at least one analyte in a bodily fluid, wherein the at least one analyte is detected from at least one reagent test region of an optical test element, and wherein the at least one reagent test region is configured for performing at least one optical detection reaction in the presence of the analyte; by using a mobile device having at least one camera, the method comprising:

[0046] (i) receiving at least one image captured by using the camera of the mobile device, wherein the image comprises at least a part of, specifically all of, the reagent test region of the optical test element having the sample of the bodily fluid applied thereto;

[0047] (ii) determining, from the image received in step (i), independently from one another, an image quality value QVx for each of at least two different image quality parameters QPx, selected from a pre -determined set of image quality parameters QP1 to QPn, wherein x is a numerical value from 1 to n, and wherein n is the total number of image quality parameters contained in the predetermined set of image quality parameters, wherein at least two, specifically the at least two, of the image quality parameters QPx, independently from one another, are configured to assess at least one of: a color characteristic of an image, and a spatial characteristic of an image;

[0048] (iii) comparing each of the image quality values QVx determined in step (ii), independently from one another, to one or more pre-determined threshold values TV(QPx); and, based on the comparing, assigning, independently from one another, an individual numerical quality assessment value IAV(QPX) to each of the at least two different image quality parameters QPx of step (ii), wherein the individual numerical quality assessment values are selected from a pre -determined set of individual numerical quality assessment values; and

[0049] (iv) deriving, from the individual numerical quality assessment values IAV(QPX) assigned in step (iii), an overall image quality assessment value OAV; comparing said overall image quality assessment value OAV to at least one overall pre -determined threshold value OTV; and, based on the comparing, determining the item of information about the quality of the image received in step (i), wherein step iv) is performed only if none of the individual numerical quality assessment values IAV(QPX) assigned in step (iii) to the image quality parameters QPx of step (ii) corresponds to a predetermined category of an insufficient quality in respect of any of the image quality parameters QPx.

[0050] The method proposed provides for a reliable evaluation of a level of quality of an image captured by a mobile device, which is an important aspect in mobile-based analytical measurements, wherein an analyte is to be accurately detected in a bodily fluid by using a mobile consumer device, like a smartphone, a tablet computer, or a laptop computer. Particularly, the method takes into account a selected set of image quality attributes (such as image quality parameters QPx) which can be quantified using simple algorithms. Therein, an “optimal” (i.e. a high-quality) range of values of each parameter or quality attribute is considered, each of which contributes to enhancing an overall level of quality of an image. By a combination of the selected parameters or quality attributes, an overall level of image quality (such as an overall image quality assessment value) can be derived (particularly by calculation), thereby enhancing an overall performance of a subsequent analytical measurement.

[0051] Additionally, the selected set of parameters or quality attributes used in the method of the present invention differs from other methods in that, most often, quantifying an image quality aims at optimizing how pleasant an image appears to human perception, e.g. when photos are taken in everyday-life situations, e.g. during vacation, at celebrations or events, and the like. In the field of analytical measurements, however, the goal is to achieve an accurate and reliable measurement performance of an algorithm implemented for detection of an analyte. In view of this goal, the set of physical parameters or quality attributes which are used to determine image quality is selected based on a measurement performance of such an algorithm for detecting an analyte, specifically wherein said algorithm takes into account an extensive set of data, particularly image data, wherein a wide range of image aberrations may be present in the set of data (i.e. deviations from a “high-quality” image, wherein “high- quality” relates to an objectively high, and quantifiable, quality of an image, such as having a high signal -to-noise ratio). Specifically, for an analyte measurement which involves detecting an analyte based on a color formation reaction, the measurement performance of an algorithm for detecting the analyte will particularly depend on an accurate and reliable determination of the color formed during the color formation reaction. Accordingly, the set of parameters or quality attributes to be used in the method of the present invention is specifically selected such that an accurate and reliable determination of the color formed during the color formation reaction is enabled, or supported. More specifically, since said parameters or quality attributes (i.e. the image quality parameters QPx) are quantifiable, their evaluation, by assigning an individual numerical quality assessment value IAV(QPX) to each of QPx, may allow to perform the analytical measurement method of the present invention with sufficient accuracy and reliability, even if one or more of the individual numerical quality assessment values IAV(QPX) may indicate a poor, or even insufficient, level of, or category of, a quality of the image received in step i), with respect to one or more of the image quality parameters QPx.

[0052] Moreover, the devices and methods provided contribute to reducing a number of failed attempts to perform a mobile-based detection of an analyte in a bodily fluid, wherein the failed attempts are attributable, at least partially, to a poor level of image quality. This additional advantage is achieved by appropriately balancing an impact of each of the selected parameters or quality attributes when an overall image quality assessment value is derived in order to assess an overall quality of an image for the purpose intended, namely to make use of the image in a (subsequent) analytical detection of an analyte in a bodily fluid.

[0053] Specifically, the item of information about the quality of the image captured in step (i) may comprise Boolean information, such as “sufficient” or “not sufficient”; more specifically, Boolean information indicating that the quality of the image is either sufficient for use of the image in the analytical measurement method for detecting the at least one analyte in the bodily fluid, or that the quality of the image is not sufficient for use of the image in the analytical measurement method.

[0054] In the method, the image quality parameters QPx are selected from a pre-determined set of image quality parameters QP1 to QPn, which may relate to any physical parameter suitable to characterize, specifically quantify, a quality aspect of the image received in step i). In step ii), the at least two, specifically all, of the image quality parameters QPx are configured to assess at least one of: a color characteristic of an image, specifically of the image received in step i), and a spatial characteristic of an image, specifically of the image received in step i). More specifically, the at least two, even more spe- cifically all, of the image quality parameters QPx may, independently from one another, be configured to assess at least one of: illumination inhomogeneity, maximum illumination, noise, and sharpness. Advantageously, the pre-determined set of image quality parameters QP1 to QPn comprises, specifically consists of, illumination inhomogeneity, maximum illumination, noise, and sharpness.

[0055] In step ii), from the image received in step (i), independently from one another, an image quality value QVx is determined for each of at least two different image quality parameters QPx. The image quality parameters QPx are physical parameters, which relate to quality aspects of an image, and which are quantifiable, in particular by image quality values QVx. The image quality values QVx may be, or may be represented by, numerical values corresponding to each of a respective one of the image quality parameters QPx. Thus, by way of example, if an image quality parameter QPx relates to a color characteristic of an image, such as an illumination inhomogeneity parameter, then said image quality parameter QPx can be quantified, e.g. by evaluating the distribution of the brightness across regions of an image that represent white regions of a test element, such as a lateral flow assay, or a test cassette. As is known to the skilled person, brightness is an intensity which may be measured, or determined, in one or more color channels, depending on the color space adopted, e.g. in one single color channel, such as any of the R, G, or B, channel of the RGB color space, or any of the X, Y, or Z, channel of the XYZ color space; any combination of two or more color channels is possible as well in order to derive an intensity value. If the brightness profile, i.e. a distribution of brightness along a specific direction in a two-dimensional image, specifically along the direction of the lateral flow in a lateral flow assay, in said regions is found to have a wide range, a significant amount of illumination inhomogeneity is present in the image. In this regard, a range of the brightness profile may be derived by determining a difference of a minimum of the brightness profile and a maximum of the brightness profile, i.e. max(brightness profile) - min(brightness profile). Herein, the brightness profile should be smoothed to some extent, so as to avoid any undesired impact of noise, or of outliers.

[0056] A wide range of the brightness profile is reflected by a corresponding image quality value QVx, which may be a comparatively high numerical value, particularly higher than, e.g., an image quality value QVx which represents a rather narrow range of a brightness profile of an image, or of a part of an image. Similarly, the image quality values QVx may be, or may be represented by, different numerical values, in each case corresponding to a respective one of the other image quality parameters QPx, such as when an image quality parameter QPx is selected from one or more of maximum illumination, noise, and sharpness. For example, a maximum illumination of an image can be used to identify dark or saturated images, or dark or saturated sub-regions within an image. In this regard, a distribution of a brightness across regions of the image that represent white regions of a test element, such as a lateral flow assay, may further be used to fdter out outliers, and / or to reliably detect intensity maxima. As another example, noise typically is, or is represented by, a signal-to-noise ratio. Still another example is sharpness, wherein particularly an image power spectral density reflects an effect of e.g. bad focus when capturing an image, or smoothing applied to an image; therefore, the image power spectral density provides an image quality measure independent of any reference card or smartphone specific pretesting or pre-calibration. Particularly in images containing sharp edges, smoothing, or extensive image compression, can be detected from the two-dimensional Fourier transform. The information contained in the two-dimensional Fourier transform may be averaged radially (particularly with the center located at 0,0 frequency). Such a radial averaging reduces the dimensionality, and facilitates interpretation of the spectral characteristics of an image. Lossy compression of images, i.e. compression of images wherein a relevant part of information contained in a non-compressed image is lost during a compression process, generally alters the power spectral density of an image. Said alteration can be deduced from the radial average of the two-dimensional Fourier transform. Summarizing, any of the image quality parameters QPx contemplated herein, specifically illumination inhomogeneity, maximum illumination, noise, and sharpness, and the determination of such parameters, particularly a quantification of such parameters, e.g. by determination of numerical values for the image quality values QVx, are generally known in the art. Generally, an illumination or an intensity, respectively, is measured in counts, e.g. 0 to 255 for each color channel. If 16bit-images are used, 0 to 65535 counts may be obtained per color channel. Specifically, for image noise, a standard deviation or a variance of the counts within a homogeneous region of interest (ROI), e.g. a white ROI, specifically a white ROI having no particular feature (such as a geometrical element like a line), may be used as a unit, e.g. measured in counts, or counts2, respectively.

[0057] When the image quality values QVx, i.e. numerical values, have been determined in step ii) for each of the at least two different image quality parameters QPx, then, in step iii), each of the image quality values QVx determined in step (ii) is compared, independently from one another, to one or more predetermined threshold values TV(QPx). The pre -determined threshold values TV(QPx) are numerical values. Thus, a full range of numerical values which may be obtained in the form of image quality values QVx as a result of an evaluation (e.g., of a measurement, of an analysis, or of a mathematical calculation) of a specific image quality parameter QPx, will be separated into two or more, e.g. two or three, sub-ranges of numerical values. For example, if there is only one threshold value TV(QPx), then said threshold value TV(QPx) will separate the full range of (numerical) image quality values QVx, which theoretically may be obtained for the specific image quality parameter QPx, into two sub-ranges of numerical values, one of which is a sub-range of numerical values below the one threshold value TV(QPx), and the other sub-range of numerical values being above the one threshold value TV(QPx), wherein the (numerical) one threshold value TV(QPx) will be a part of only one of said two subranges. If there is more than one threshold value TV(QPx) for a given image quality parameters QPx, e.g. two, then at least three sub-ranges of (numerical) image quality values QVx will be present.

[0058] Specifically, the pre -determined threshold values TV(QPx) may be selected for each of the image quality parameters QPx, independently from one another, such that the resulting sub-ranges of image quality values QVx may be categorized in terms of different levels of a (objective) quality of an image. Thus, a first sub-range of image quality values QVx may be representative of a category of an insufficient quality in respect of a corresponding image quality parameter QPx, whereas a second subrange of image quality values QVx may be representative of a category of an acceptable quality in respect of said image quality parameter QPx. More than two sub-ranges of image quality values QVx allow for differentiating between several acceptable levels of quality of an image. Thus, in addition to a first sub-range of image quality values QVx representative of a category of an insufficient quality in respect of a corresponding image quality parameter QPx, a second and a third sub-range of image quality values QVx, respectively, may be representative of a category of an acceptable quality and of a high quality, respectively, in respect of said image quality parameter QPx. Specifically, since in step iii) each of the image quality values QVx determined in step (ii) is compared, independently from one another, to one or more pre-determined threshold values TV(QPx), a number of threshold values TV(QPx) selected for a given image quality parameter QPx may differ from a number of threshold values TV(QPx) selected for another image quality parameter QPx. Thus, for a first image quality parameter QPx, only one threshold value TV(QPx) may be selected, whereas, for a second image quality parameter QPx, two threshold values TV(QPx) may be selected.

[0059] Specifically, the pre -determined threshold values TV(QPx) may be selected, or determined, or defined, as may be appropriate for a particular image quality parameter QPx. Thus, the pre -determined threshold values TV(QPx) may be selected, determined, or defined, for each of the image quality parameters QPx, independently from one another, by taking into account one or more of the following: Experimental data, study data, data from laboratory work examples, physical considerations, signal processing considerations, algorithmic considerations, and any combinations thereof. Such physical con- sideration may comprise, e.g. whether or not any, or a significant number of, pixels are saturated, or are zero; and whether or not any smoothing applied to an image is too extensive, and / or sharpness is too low, for obtaining a physically meaningful result. Further, such signal processing considerations may comprise, e.g. whether or not a specific illumination inhomogeneity present in an image can be corrected by signal processing; and whether or not a specific amount of image noise present in an image may be accounted for by signal processing? Further, such algorithmic considerations may comprise, e.g. whether or not a specific image quality value QVx may prevent the algorithm from detecting a geometrical element, such as a line, in an image; and whether or not a specific QVx may induce a flawed, or a false, detection, e.g. of a specific color, or of a presence or absence of a geometrical element, both of which may correspond to a presence or an absence of an analyte to be detected.

[0060] Generally, any values selected, or determined, for a pre-determined threshold may be verified, by testing a specific algorithm, which is used for evaluating any of the image quality parameters QPx, by applying said algorithm to a set of test data, specifically image test data. Such image test data usually comprise images of different levels of quality with respect to one or more image quality parameters QPx, such as different levels of image noise, of smoothing applied, and / or of intensity gradients. In order to create a suitable set of image test data, said different levels of quality may be introduced artificially, such as by decreasing, e.g. stepwise, a signal-to-noise ratio of an image, by increasingly applying smoothing to an image, or by adding intensity gradients to an image. Thereby, one or more series of test data may be generated, having a decreasing, e.g. a continuously decreasing, image quality value QVx. By testing the algorithm using such series of test data, any ranges of image quality values QVx may be identified, such as insufficient, acceptable, or high-quality ranges; specifically depending on whether or not the algorithm may generate any physically meaningful results.

[0061] Further, in step iii), based on the comparing of the image quality values QVx determined in step (ii) to the one or more pre-determined threshold values TV(QPx), an individual numerical quality assessment value IAV(QPX) is assigned, independently from one another, to each of the at least two different image quality parameters QPx of step (ii). Herein, the individual numerical quality assessment values are selected from a pre -determined set of individual numerical quality assessment values. The predetermined set of individual numerical quality assessment values may comprise, or consist of, e.g. the numbers 0 and 1; 0, 1, and 2; 0, 1, 2, and 3. While it is not mandatory that the pre-determined set of individual numerical quality assessment values comprises a number 0, in some instances it may turn out convenient, and thus efficient, to include a number 0; particularly, if the number 0 is used for a category of an insufficient quality in respect of a corresponding image quality parameter QPx.

[0062] Generally, an individual numerical quality assessment value IAV(QPX) is assigned, independently from one another, corresponding to each one of a plurality of sub-ranges of image quality values QVx, as contemplated herein above. Additionally or alternatively, a specific individual numerical quality assessment value IAV(QPX), such as 0, 1 or 2, may represent a specific category of, or level of, a quality in respect of a corresponding image quality parameter QPx. For example, a specific individual numerical quality assessment value IAV(QPX) of 0 may represent a category of, or a level of, an insufficient quality in respect of a corresponding image quality parameter QPx. Likewise, specific individual numerical quality assessment values IAV(QPX) like 1 or 2, may represent different specific categories of, or levels of, an acceptable quality (e.g. IAV(QPX) = 1), or a high quality (e.g. IAV(QPX) = 2), respectively, in respect of a corresponding image quality parameter QPx.

[0063] In an aspect of the present invention, the set of individual numerical quality assessment values of step (iii) may comprise, for at least two, specifically all, of the image quality parameters QPx, independently from one another, at least a first individual numerical quality assessment value lAVl(QPx) and a second individual numerical quality assessment value IAV2(QPx), wherein lAVl(QPx) < IAV2(QPx). Herein, the first lAVl(QPx) corresponds to a pre-determined category of an insufficient quality in respect of a corresponding image quality parameter QPx. Furthermore, the set of individual numerical quality assessment values may comprise, for at least one, e.g. for one or two, or for all, of the at least two image quality parameters QPx, independently from one another, one or more additional individual numerical quality assessment values, e.g. a third individual numerical quality assessment value IAV3(QPx), wherein lAVl(QPx) < IAV2(QPx) < IAV3(QPx). Generally, the image quality in respect of an image quality parameter QPx may be evaluated the better the higher the actual numerical value of a specific individual numerical quality assessment value, such as lAVl(QPx), IAV2(QPx) and IAV3(QPx), is. As is evident to the person skilled in the art, other ways may be conceived to attribute individual numerical quality assessment values IAV(QPX) to pre-determined categories of a quality of an image, such as a category of an “insufficient quality” in respect of a corresponding image quality parameter QPx; whereby an assessment of the image quality in respect of an image quality parameter QPx may be evaluated differently, e.g. the better the lower the actual numerical value of a specific individual numerical quality assessment value is, as may be the case if negative numerical values are assigned to the individual numerical quality assessment values IAV(QPX). For illustration purposes only, with respect to one of the image quality parameters QPx, e.g. a first image quality parameter QP1, some examples of image quality values QVx, e.g. QV1, the predetermined threshold values TV(QPx), e.g. TV(QPl), and the individual numerical quality assessment values IAV(QPX), e.g. IAV(QP1), respectively, may be considered, and exemplified by using arbitrary numbers. Thus, with respect to QP1, two threshold values TV(QPl) may be selected, e.g. a first threshold value TVi(QPl) = 1, and a second threshold value TV2(QP1) = 5. The full range of image quality values QV1, which may be obtainable for QP1, may include any numerical value in a range of from 0 to 10. Accordingly, the threshold values TVi(QPl) and TV2(QP1) will separate the full range of obtainable image quality values QV1, into three sub-ranges of image quality values QV1. Each of the three sub-ranges of image quality values QV1 may correspond to a different one of three individual numerical quality assessment values lAV(QPl), respectively. Thus, a first sub-range of image quality values QV1, comprising any numerical value below the first threshold value TVi(QPl), may correspond to a first individual numerical quality assessment value IAVI(QPI), wherein IAVI(QPI) = 0. A second sub-range of image quality values QV1, comprising any numerical value equal to, or above the first threshold value TVi(QPl), but below the second threshold value TV2(QP1), may correspond to a second individual numerical quality assessment value IAVI(QPI), wherein IAV2(QP1) = 1. A third sub-range of image quality values QV1, comprising any numerical value equal to, or above the second threshold value TV2(QP1), may correspond to a third individual numerical quality assessment value lAVTQP l ). wherein IA V,(QP 1 ) = 2. Herein, each of the three sub-ranges of image quality values QV1, and hence, each of the three individual numerical quality assessment values IAVI(QPI), IAV2(QP1), and IAVTQP I ), may correspond to a different category of, or level of, quality in respect of the corresponding image quality parameter QP1. Thus far, the present example may be summarized as follows:

[0064] - a specific image quality parameter QP 1 is considered;

[0065] - the full range of image quality values QV1, obtainable for QP1, includes any numerical value in a range of from 0 to 10;

[0066] - two pre-determined threshold values are selected, or are determined, to be TVi(QPl) = 1, and TV2(QP1) = 5, respectively;

[0067] - if QV1 < 1, then a numerical value IAVI(QPI) of 0 is assigned to QP1;

[0068] - if 1 < QV1 < 5, then a numerical value IAV2(QP1) of 1 is assigned to QP1;

[0069] - if 5 < QV1 < 10, then a numerical value IAVSCQPI) of 2 is assigned to QP1. - the individual numerical quality assessment values IAVI(QPI), IAV2(QP1), and I A V,(QP 1 ). respectively, each correspond to a category of, or level of, insufficient quality (IAVI(QPI) = 0), acceptable quality (IAV2(QP1) = 1), and high quality (lAVs(QPl) = 2), respectively, in respect of QP1.

[0070] In step iv), from the individual numerical quality assessment values IAV(QPX) assigned in step (iii), an overall image quality assessment value OAV is derived. Herein, deriving the OAV may comprise combining, specifically by applying a mathematical function, two or more, specifically all, of the individual numerical quality assessment values IAV(QPX) assigned in step (iii). Specifically, the mathematical function may be, or may comprise, any basic or more complex type of calculation, including, but not being limited to, one or more of an addition, a subtraction, a multiplication, a division, and the like, and / or any combinations thereof. For illustration purposes only, the mathematical function may be a simple addition of the individual numerical quality assessment values IAV(QPX) assigned in step (iii); or the mathematical function may be an addition of the squares of the individual numerical quality assessment values IAV(QPX) assigned in step (iii); or the mathematical function may be a multiplication of said IAV(QPX) values. Other alternatives for the mathematical function may be conceived.

[0071] Specifically, deriving the OAV in step iv) may comprise combining, by applying a mathematical function, e.g. an addition of two or more, particularly all, of the individual numerical quality assessment values IAV(QPX) assigned in step (iii). In a situation wherein two image quality parameters QPx, such as a first image quality parameter QP1 and a second image quality parameter QP2, are considered, an image quality value QV1 and QV2 is determined, in step ii), from the image received in step (i), independently from one another, for each of QP1 and QP2, respectively. In step iii), each of QV1 and QV2 determined in step (ii) is compared, independently from one another, to one or more pre-determined threshold values TV(QPl) and TV(QP2), respectively. For example, for QP1, only one threshold value TV(QPl) may be selected, or determined, whereas for QP2, two TVi(QP2) and TV2(QP2) may be selected, or determined. Further, in step iii), based on the comparing in step iii), independently from one another, an individual numerical quality assessment value IAV(QPX) is assigned to each of QP1 and QP2 of step (ii), e.g. in this case lAV(QPl) and IAV(QP2). For QP1, one out of two numerical values of lAV(QPl) may be obtained, since there is only one threshold value TV(QPl). For QP2, since there are two threshold values, one out of three numerical values of IAV(QP2) may be obtained. Accordingly, an overall image quality assessment value OAV may be derived, by way of a mathematical addition, i.e. by adding one of the two numerical values of lAV(QPl) and one of the three numerical values of IAV(QP2). Assuming the two numerical values of lAV(QPl) obtainable to be either 0 or 1, and assuming the three numerical values of IAV(QP2) obtainable to be either one of 0, 1, or 2, it follows that OAV may be one of the following numerical values, i.e. a numerical value being a result of the addition of two numerical values: 0 (if lAV(QPl) = IAV(QP2) = 0), 1 (if lAV(QPl) = 0 and IAV(QP2) = 1, or vice versa), 2 (if lAV(QPl) = 0 and IAV(QP2) = 2, or if lAV(QPl) = IAV(QP2) = 1), or 3 (if lAV(QPl) = 1 and IAV(QP2) = 2).

[0072] Further, in step iv), the overall image quality assessment value OAV derived in step iv), is compared to at least one overall pre-determined threshold value OTV. The overall pre-determined threshold value OTV is a numerical value, e.g. 2, 3, 4 or 5, or any other numerical value appropriately selected, or determined, with regard to the individual numerical quality assessment values IAV(QPX). Specifically, since the OAV is derived from the individual numerical quality assessment values IAV(QPX), the OAV generally represents a combination of said individual numerical quality assessment values IAV(QPX). Therefore, each of the IAV(QPX) may contribute to the OAV. Specifically, the OAV thus is an indicator of an overall level of quality of an image, such as the image received in step i), said indicator taking into account each of the individual numerical quality assessment values IAV(QPX). Further, the overall pre -determined threshold value OTV may be selected, or determined, to be a numerical value which provides an assessment of an overall level of quality of an image, such as the image received in step i), in alignment with the individual numerical quality assessment values IAV(QPX) which have been initially selected, or determined, for each of the image quality parameters QPx.

[0073] In this regard, referring back to the example provided herein above, i.e. a setting wherein two image quality parameters QP 1 and QP2 are present, and the overall image quality assessment value OAV in this setting may be one of the following numerical values: 0, 1, 2 or 3; then in this setting, a numerical value of 0 or 1 for OAV may be representative of a category of, or level of, an insufficient quality in respect of the overall image quality assessment value OAV; whereas a numerical value of 2 or 3 for OAV may be representative of a category of, or level of, a sufficient quality in respect of the overall image quality assessment value OAV.

[0074] Particularly, the OAV thus is an indicator of the suitability of the image received in step (i) to be used in an analytical measurement method for detecting at least one analyte in a bodily fluid. Specifically, in step iv), if the overall image quality assessment value OAV is less than the at least one overall predetermined threshold value OTV, then the item of information about the quality of the image received in step (i) may be determined to be insufficient with respect to the suitability of the image to be used in an analytical measurement method for detecting at least one analyte in a bodily fluid. Otherwise, if the overall image quality assessment value OAV is equal to, or greater than, the at least one overall predetermined threshold value OTV, then the item of information about the quality of the image received in step (i) may be determined to be sufficient with respect to the suitability of the image to be used in an analytical measurement method for detecting at least one analyte in a bodily fluid. However, the skilled person will readily realize that other ways of applying a particular threshold may be used as well, depending inter alia on the ways used to assign, in step iii), an individual numerical quality assessment value IAV(QPX) to each of the image quality parameters QPx of step (ii), e.g. depending on whether a comparatively low numerical value (such as 0) is assigned for a poor or insufficient level of quality, or whether a comparatively high numerical value (such as 10) is assigned for a poor or insufficient level of quality; and depending on the way of deriving, in step iv), the overall image quality assessment value OAV, e.g. by way of addition, subtraction, multiplication, etc.

[0075] Optionally, the item of information about the quality of the image received in step (i) may be displayed, e.g. on a display of the mobile device. Specifically, if one or more of the individual numerical quality assessment values IAV(QPX) assigned in step (iii) and / or the overall image quality assessment value OAV derived in step iv) indicate a level of, or a category of, a poor quality of an image with respect to one or more of the image quality parameters QPx, then additional information relating to such a poor quality of the image received in step i) may be useful, particularly in connection with the analytical measurement method of the present invention. Depending on an extent of such a poor quality, such additional information may comprise a remark or a warning. For example, if one, or more than one, of the individual numerical quality assessment values IAV(QPX) assigned in step (iii) correspond to an insufficient level of, or a category of, a quality of an image with respect to the image quality parameters QPx, then a warning may be displayed, e.g. on the display of the mobile device, that a result of a detection of an analyte, obtained by performing the analytical measurement method of the present invention, may be inaccurate; or that no detection of the analyte can be performed. Additionally or alternatively, if one or more of the individual numerical quality assessment values IAV(QPX) assigned in step (iii) correspond to a poor, but still sufficient, level of, or a category of, a quality of an image with respect to the image quality parameters QPx, then a remark may be displayed, e.g. on the display of the mobile device, that a result of detection of an analyte, obtained by performing the analytical measurement method of the present invention, may have a reduced accuracy. Furthermore, additionally or alternatively, one or more prompts may be displayed, e.g. on the display of the mobile device, providing guidance, specifically to a user of the method, in order to receive, or capture, another image, having an improved level of quality with respect to one or more specific image quality parameters QPx. For example, if the image initially received in step i) comprises a comparatively high level of illumination inhomogeneity, then such a prompt may comprise a recommendation to avoid any shadows, or any external light, within the camera’s field of view. As another example, if the image initially received in step i) comprises a comparatively high level of noise, then such a prompt may comprise a recommendation to repeat step i), e.g. by capturing another image, but in a brighter environment.

[0076] However, the method may further comprise, if at least one of the individual numerical quality assessment values IAV(QPX) assigned in step (iii) to the image quality parameters QPx of step (ii) corresponds to a pre-determined category of an insufficient quality in respect of the image quality parameter QPx affected, iii. 1) obtaining another image according to step i); or iii.2) aborting the method.

[0077] Particularly, herein, step iii.l) may be performed once, or more than once, before step iii.2) is performed.

[0078] As outlined above, step iv) is performed only if none of the individual numerical quality assessment values IAV(QPX) assigned in step (iii) to the image quality parameters QPx of step (ii) corresponds to a pre-determined category of an insufficient quality in respect of any of the image quality parameters QPx. As an example, if one or more of the individual numerical quality assessment values IAV(QPX) assigned in step (iii) corresponds to a pre-determined category of an insufficient quality in respect of one or more of the image quality parameters QPx of step ii), e.g. if a numerical value of 0 is obtained for at least one of the IAV(QPX) assigned in step (iii), for at least one of the image quality parameters QPx, and said numerical value of 0 represents a category of an insufficient quality in respect of said at least one of the image quality parameters QPx, then step iv) will not be performed; instead, either of steps iv. 1) or iv.2) may be performed.

[0079] Additionally or alternatively, the method may further comprise, if the overall image quality assessment value OAV derived in step iv) corresponds to a pre-determined category of an insufficient quality in respect of the at least one overall pre-determined threshold value OTV, iv.l) obtaining another image according to step i); or iv.2) aborting the method.

[0080] Specifically, herein, step iv.l) may be performed once, or more than once, before step iv.2) is performed.

[0081] More specifically, steps iii.l), iii.2), iv.l) and / or iv.2) may be performed only if at least one, specifically both, of the following criteria is met:

[0082] - a pre-determined period of time, starting with obtaining, for the first time, the at least one image in step i), has not yet elapsed;

[0083] - a pre -determined maximum number of attempts to perform the computer-implemented method has not yet been exceeded.

[0084] In a second aspect of the present invention, an analytical method, specifically a computer- implemented analytical method and / or specifically an in-vitro analytical method, for determining a concentration of an analyte in a bodily fluid is disclosed, the method comprising using a mobile device having at least one camera and, specifically, at least one processor. Herein, the at least one analyte is detected from at least one reagent test region of an optical test element. The at least one reagent test region is configured for performing at least one optical detection reaction in the presence of the analyte.

[0085] The method comprises the following steps which, as an example, may be performed in the given order. It shall be noted, however, that a different order may also be possible. Further, it may also be possible to perform one or more of the method steps once or repeatedly. Further, it may be possible to perform two or more of the method steps simultaneously or in a timely overlapping fashion. The method may comprise further method steps which are not listed.

[0086] The analytical measurement method comprises, in a first step I), performing the method for determining an item of information about a quality of the image received in step (i), as described herein above.

[0087] The analytical measurement method further comprises, if the item of information about the quality of the image captured in step (i) is determined, in step iv), to indicate that the quality of the image is sufficient for use of the image in the analytical measurement method for detecting the at least one analyte in the bodily fluid, detecting, by evaluating the image received in step (i), specifically by the processor of the mobile device, the at least one analyte from the at least one optical detection reaction at the at least one reagent test region of the optical test element.

[0088] Accordingly, the present invention, in the second aspect, particularly relates to an analytical measurement method, specifically a computer-implemented analytical measurement method and / or specifically an in-vitro analytical measurement method, for detecting at least one analyte in a sample of a bodily fluid by using a mobile device having at least one camera and, specifically, at least one processor, wherein the at least one analyte is detected from at least one reagent test region of an optical test element, and wherein the at least one reagent test region is configured for performing at least one optical detection reaction in the presence of the analyte, the method comprising:

[0089] I) performing the method for determining an item of information about a quality of the image received in step (i), as described herein above;

[0090] II) if the item of information about the quality of the image captured in step (i) is determined, in step iv), to indicate that the quality of the image is sufficient for use of the image in the analytical measurement method for detecting the at least one analyte in the bodily fluid, detecting, by evaluating the image received in step (i), specifically by the processor of the mobile device, the at least one analyte from the at least one optical detection reaction at the at least one reagent test region of the optical test element.

[0091] In step II), detecting the at least one analyte in the bodily fluid may comprise determining a presence or absence of the analyte in the bodily fluid. Further, in step II), detecting the at least one analyte in the bodily fluid may comprise determining a concentration of the analyte in the bodily fluid.

[0092] In the analytical measurement method, the optical test element may be selected from the group comprising a test strip, a test assay, specifically a lateral flow assay, a test stick, specifically a test dipstick, a test cassette, a test tape, a test paper, and a test chip. Specifically, a test strip and / or a lateral flow assay may be used.

[0093] The analytical measurement method further may comprise providing the optical test element.

[0094] The analytical measurement method may further comprise a step of displaying a result of the analytical measurement, such as an indication of a presence or an absence of an analyte, or an indication of an analyte concentration value, e.g. on a display of the mobile device. Optionally, in addition to dis- playing a result of the analytical measurement, a notification and / or a warning may be displayed, depending on one or more of the individual numerical quality assessment values IAV(QPX) and the overall image quality assessment value OAV. For example, if one or more of the individual numerical quality assessment values IAV(QPX) assigned in step iii) indicate a category of, or a level of, a poor quality with respect to one or more of the image quality parameters QPx, then a warning may be displayed, indicating that the result of the analytical measurement may be flawed or faulty; or a notification may be displayed, prompting a user to repeat the analytical measurement. Additionally or alternatively, the method may comprise storing a result, such as an analyte concentration value, in at least one data storage device of the mobile device. Again additionally and alternatively, the method may further comprise transmitting a result, e.g. an analyte concentration value, via at least one interface and / or via at least one data transmission network, such as to another computer, e.g. for further evaluation or processing.

[0095] In another aspect, the present invention relates to a mobile device having at least one camera and at least one processor, the mobile device being configured for

[0096] - determining an item of information about a quality of an image; wherein the mobile device further is configured for performing at least steps i) to iv) of the method for determining an item of information about a quality of an image, as described herein above; or

[0097] - detecting a concentration of an analyte in a bodily fluid, wherein the at least one analyte is detected from an optical detection reaction at a reagent test region; wherein the mobile device further is configured for performing at least steps I) to II) of the analytical measurement method, as described herein above.

[0098] In yet another aspect, the present invention relates to a kit, comprising the mobile device, as described herein above, and at least one optical test element having at least one reagent test region, wherein the at least one reagent test region is configured for performing at least one optical detection reaction in the presence of the analyte.

[0099] In yet another aspect, the present invention relates to a computer program comprising instructions which,

[0100] - when the program is executed by the mobile device, as described herein above, cause the mobile device to carry out at least steps i) to iv) of the method for determining an item of information about a quality of an image, as described herein above; or - when the program is executed by the mobile device, as described herein above, cause the mobile device to carry out at least steps I) to II) of the analytical measurement method, as described herein above.

[0101] In yet another aspect, the present invention relates to a computer-readable storage medium comprising instructions which,

[0102] - when executed by the mobile device, as described herein above, cause the mobile device to carry out at least steps i) to iv) of the method for determining an item of information about a quality of an image, as described herein above; or

[0103] - when executed by the mobile device, as described herein above, cause the mobile device to carry out at least steps I) to II) of the analytical measurement method, as described herein above.

[0104] The methods and devices proposed provide for a reliable evaluation of a level of quality of an image captured by a mobile device, for particular use in mobile-based analytical measurements demanding an accurate detection of an analyte in a bodily fluid by using a mobile device. By a combination of specifically selected parameters or quality attributes, an overall level of image quality can be derived, thereby enhancing an overall performance of a subsequent analytical measurement. Moreover, the devices and methods provided contribute to reducing a number of failed attempts to perform a mobilebased detection of an analyte in a bodily fluid, wherein the failed attempts are attributable, at least partially, to a poor level of image quality.

[0105] Short description of the figures

[0106] Fig. 1 A shows an example of an image quality parameter QPx, wherein QPx is an illumination inhomogeneity parameter, illustrating a very low illumination inhomogeneity.

[0107] Fig. 1 B shows an example of an image quality parameter QPx, wherein QPx is an illumination inhomogeneity parameter, illustrating a low illumination inhomogeneity.

[0108] Fig. 1 C shows an example of an image quality parameter QPx, wherein QPx is an illumination inhomogeneity parameter, illustrating a medium illumination inhomogeneity. Fig. 2 A shows another example of an image quality parameter QPx, wherein QPx is a maximum illumination parameter, illustrating a maximum illumination in an optimal range.

[0109] Fig. 2 B shows another example of an image quality parameter QPx, wherein QPx is a maximum illumination parameter, illustrating a maximum illumination in a suboptimal range.

[0110] Fig. 2 C shows another example of an image quality parameter QPx, wherein QPx is a maximum illumination parameter, illustrating a maximum illumination in a saturated range.

[0111] Fig. 3 A shows another example of an image quality parameter QPx, wherein QPx is a noise parameter, illustrating a very high signal-to-noise ratio.

[0112] Fig. 3 B shows another example of an image quality parameter QPx, wherein QPx is a noise parameter, illustrating a medium signal-to-noise ratio.

[0113] Fig. 3 C shows another example of an image quality parameter QPx, wherein QPx is a noise parameter, illustrating a low signal-to-noise ratio.

[0114] Fig. 4 A shows another example of an image quality parameter QPx, wherein QPx is a sharpness parameter, specifically illustrating an effect of image compression.

[0115] Fig. 4 B further illustrates the sharpness parameter from the example of Fig. 4 A.

[0116] Fig. 4 C shows another example of an image quality parameter QPx, wherein QPx is a sharpness parameter, specifically illustrating an effect of image smoothing.

[0117] Fig. 4 D further illustrates the sharpness parameter from the example of Fig. 4 C.

[0118] Fig. 5 shows an embodiment of a kit and a mobile device for performing an analytical measurement, illustrated in a perspective view. Fig. 6 illustrates a flow chart of an exemplary embodiment for carrying out the method for determining an item of information about a quality of an image, according to the present invention.

[0119] Fig. 7 illustrates a flow chart of an exemplary embodiment for carrying out the analytical measurement method for detecting at least one analyte in a sample of a bodily fluid, according to the present invention.

[0120] Detailed description of the figures

[0121] In the method for determining an item of information about a quality of an image, the at least two of the image quality parameters QPx, independently from one another, are configured to assess at least one of: a color characteristic of an image, and a spatial characteristic of an image. Specifically, the at least two of the image quality parameters QPx, independently from one another, may be configured to assess at least one of: illumination inhomogeneity, maximum illumination, noise, and sharpness. Hence, as an example, the pre-determined set of image quality parameters QPx may comprise 4 image quality parameters QPx, specifically illumination inhomogeneity, maximum illumination, noise, and sharpness. Fig. 1A to Fig. 4 D each illustrate an example of any of said image quality parameters QPx, in each case illustrating a specific level of quality and / or an impact of a specific level of quality, with regard to the specific image quality parameter QPx. Some different specific levels of quality may be represented by, or may correspond to, a pre -determined set of individual numerical quality assessment values. As an example only, the pre-determined set of individual numerical quality assessment values may comprise, or may consist of, the numerical values 0, 1, and 2, respectively. Specifically, the numerical values 0, 1, and 2, in this example, may represent an increasing level of quality with respect to a particular image quality parameter QPx.

[0122] Fig. 1 A to 1 C show an example of an image quality parameter QPx, such as QP1, wherein QP1 is an illumination inhomogeneity parameter. This illumination inhomogeneity parameter can be quantified by evaluating the distribution of a brightness across regions of the image that represent white regions of the test element. When the brightness profile in these regions is determined to comprise a wide range, then the image comprises a significant level of illumination inhomogeneity. Thus, an image quality value QVx, such as QV1, may be derived, from each of the images in Fig. 1 A, 1 B, and 1 C, respectively, independently from one another, by evaluating the brightness profile of each image. Spe- cifically, the image quality parameter QP1, as exemplified herein, relates to an inhomogeneity of brightness, i.e. to an inhomogeneity of intensity in a white region of an image, and thus, in other words, to a difference of the brightest and the darkest spots in an image.

[0123] By way of example, Fig. 1 A illustrates a very low illumination inhomogeneity. Thus, an image quality value QV1 derived from this image may, when compared to a first pre -determined threshold value TVi(QPl), readily pass said threshold value TVi(QPl), e.g. QV1 > TVi(QPl). Based on the comparing of QV1 with TVi(QPl), an individual numerical quality assessment value lAV(QPl) can be assigned to the image quality parameter QP1. For example, an lAV(QPl) = 2 may be assigned to QP1, which in this case reflects a very good level of quality with regard to the image quality parameter QP1.

[0124] Alternatively, Fig. 1 B illustrates a low illumination inhomogeneity. Thus, an image quality value QV1 derived from this image may, when compared to the pre-determined threshold value TVi(QPl), not pass said threshold value TVi(QPl), e.g. QV1 < TVi(QPl), but may still pass a second predetermined threshold value TV2(QP1), e.g. TV2(QP1) < QV1 < TVi(QPl). Based on the comparing of QV1 with TVi(QPl) and TV2(QP1), an individual numerical quality assessment value lAV(QPl), different to the one assigned before (with respect to Fig. 1 A), can be assigned to the image quality parameter QP1. For example, an lAV(QPl) = 1 may be assigned to QP1, which in this case reflects an acceptable level of quality with regard to the image quality parameter QP 1.

[0125] Still alternatively, Fig. 1 C illustrates a medium illumination inhomogeneity. Thus, an image quality value QV1 derived from this image may, when compared to the pre-determined threshold value TV2(QP1), not pass said threshold value TV2(QP1), e.g. QV1 < TV2(QP1). Based on the comparing of QV1 with TV2(QP1), an individual numerical quality assessment value lAV(QPl), different to the ones assigned before (with respect to Fig. 1 A and Fig. 1 B, respectively), can be assigned to the image quality parameter QP1. For example, an lAV(QPl) = 0 may be assigned to QP1, which in this case reflects an insufficient level of quality with regard to the image quality parameter QP1.

[0126] Fig. 2 A to 2 C show another example of an image quality parameter QPx, such as QP2, wherein QP2 is a maximum illumination parameter. The maximum illumination of an image can be used to identify dark or saturated images. In particular, the distribution of a brightness across regions of an image that represent white regions of a test element can be used to filter out outliers in order to reliably identify any maxima of brightness. Specifically, the image quality parameter QP2, as exemplified herein, thus relates to a maximum of the brightness. Accordingly, an image quality value QVx, such as QV2, may be derived, from each of the images in Fig. 2 A, 2 B, and 2 C, respectively, independently from one another, by evaluating the maximum illumination of each image.

[0127] By way of example, Fig. 2 A illustrates a maximum illumination in an optimal range. Thus, an image quality value QV2 derived from this image may, when compared to a first pre -determined threshold value TVi(QP2), readily pass said threshold value TVi(QP2), e.g. QV2 > TVi(QP2). Based on the comparing of QV2 with TVi(QP2), an individual numerical quality assessment value IAV(QP2) can be assigned to the image quality parameter QP2. For example, an IAV(QP2) = 2 may be assigned to QP2, which in this case reflects a very good level of quality with regard to the image quality parameter QP2.

[0128] Alternatively, Fig. 2 B illustrates a maximum illumination in a suboptimal range. Thus, an image quality value QV2 derived from this image may, when compared to the pre-determined threshold value TVi(QP2), not pass said threshold value TVi(QP2), e.g. QV2 < TVi(QP2), but may still pass a second pre-determined threshold value TV2(QP2), e.g. TV2(QP2) < QV2 < TVi(QP2). Based on the comparing of QV2 with TVi(QP2) and TV2(QP2), an individual numerical quality assessment value IAV(QP2), different to the one assigned before (with respect to Fig. 2 A), can be assigned to the image quality parameter QP2. For example, an IAV(QP2) = 1 may be assigned to QP2, which in this case reflects an acceptable level of quality with regard to the image quality parameter QP2.

[0129] Still alternatively, Fig. 2 C illustrates a maximum illumination in a saturated range. Thus, an image quality value QV2 derived from this image may, when compared to the pre-determined threshold value TV2(QP2), not pass said threshold value TV2(QP2), e.g. QV2 < TV2(QP2). Based on the comparing of QV2 with TV2(QP2), an individual numerical quality assessment value IAV(QP2), different to the ones assigned before (with respect to Fig. 2 A and Fig. 2 B, respectively), can be assigned to the image quality parameter QP2. For example, an IAV(QP2) = 0 may be assigned to QP2, which in this case reflects a poor, and thus insufficient, level of quality with regard to the image quality parameter QP2.

[0130] Fig. 3 A to 3 C show another example of an image quality parameter QPx, such as QP3, wherein QP3 is a noise parameter. The noise parameter (also referred to as “image noise” parameter) can be quantified by evaluating a signal-to-noise ratio of an image. Thus, an image quality value QVx, such as QV3, may be derived, from each of the images in Fig. 3 A, 3 B, and 3 C, respectively, independently from one another, by evaluating the signal -to-noise ratio of each image.

[0131] By way of example, Fig. 3 A illustrates a very high signal-to-noise ratio. Thus, an image quality value QV3 derived from this image may, when compared to a first pre-determined threshold value TVi(QP3), readily pass said threshold value TVi(QP3), e.g. QV3 > TVi(QP3). Based on the comparing of QV3 with TVi(QP3), an individual numerical quality assessment value IAV(QP3) can be assigned to the image quality parameter QP3. For example, an IAV(QP3) = 2 may be assigned to QP3, which in this case reflects a very good level of quality with regard to the image quality parameter QP3.

[0132] Alternatively, Fig. 3 B illustrates a medium signal-to-noise ratio. Thus, an image quality value QV3 derived from this image may, when compared to the pre-determined threshold value TVi(QP3), not pass said threshold value TVi(QP3), e.g. QV3 < TVi(QP3), but may still pass a second pre-determined threshold value TV2(QP3), e.g. TV2(QP3) < QV3 < TVi(QP3). Based on the comparing of QV3 with TVi(QP3) and TV2(QP3), an individual numerical quality assessment value IAV(QP3), different to the one assigned before (with respect to Fig. 3 A), can be assigned to the image quality parameter QP3. For example, an IAV(QP3) = 1 may be assigned to QP3, which in this case reflects an acceptable level of quality with regard to the image quality parameter QP3.

[0133] Still alternatively, Fig. 3 C illustrates a low signal-to-noise ratio. Thus, an image quality value QV3 derived from this image may, when compared to the pre-determined threshold value TV2(QP3), not pass said threshold value TV2(QP3), e.g. QV3 < TV2(QP3). Based on the comparing of QV3 with TV2(QP3), an individual numerical quality assessment value IAV(QP3), different to the ones assigned before (with respect to Fig. 3 A and Fig. 3 B, respectively), can be assigned to the image quality parameter QP3. For example, an IAV(QP3) = 0 may be assigned to QP3, which in this case reflects a poor, and thus insufficient, level of quality with regard to the image quality parameter QP3.

[0134] Fig. 4 A to 4 D show another example of an image quality parameter QPx, such as QP4, wherein QP4 is a sharpness parameter. Determining a sharpness can be achieved by e.g., but not limited to, an evaluation of an image power spectral density. Such an evaluation of the image power spectral density usually reflects any smoothing applied and, therefore, provides a measure of a level of quality of an image, independent of any smartphone-specific pre-testing or pre-calibration, and without a need for using a color reference card. Particularly in images which contain one or more sharp edges, smooth- ing or extensive image compression can be detected from a two-dimensional Fourier transform. The information contained in the two-dimensional Fourier transform can be averaged radially (specifically with the center located at 0,0 frequency). Such radial averaging reduces the dimensionality. Furthermore, it facilitates an interpretation of the spectral characteristics of an image. Lossy compression of images, i.e. with wherein a certain amount of information is lost upon compression of an image, alters the power spectral density of images. Such an alteration can be read from the radial average of the two-dimensional Fourier transform. Specifically, the radial average of the two-dimensional Fourier transform may be compared for one and the same image before and after compression of said image, thereby illustrating an effect of such image compression. Additionally or alternatively, the radial average of the two-dimensional Fourier transform may be compared for one and the same image before and after smoothing, or filtering, of said image, thereby illustrating an effect of such image smoothing.

[0135] Fig. 4 A specifically illustrates an effect of image compression. In the top left part of Fig. 4 A, an image of a test element, such as a lateral flow assay, e.g. for virus detection, comprising a sharp edge and a non-insignificant level of noise, i.e. signal-to-noise ratio, is represented. In the top right part of Fig. 4 A, the same image, but after being compressed with an algorithm from the Joint Photographic Experts Group (standard JPEG, 8bit) with a low quality factor of 25%, is represented. The bottom part of Fig. 4 A shows a signal, i.e. pixel values, of a single row from the green channel of the image before and after compression.

[0136] The image before compression (top left-hand side of Fig. 4 A) is compared to the same image after being compressed with the JPEG algorithm (top right-hand side of Fig. 4 A). Fig. 4 B shows a comparison of the radial average of the two-dimensional Fourier transform of the images before and after compression. From Fig. 4 B, it can be deduced that, after compression, some information in the higher spatial frequencies is lost from the original image. This is because the intensity of the Fourier transform depicted for the compressed image is found to be at higher spatial frequencies below the intensity of the Fourier transform depicted for the non-compressed image, indicating that less information is contained at said higher spatial frequencies.

[0137] Fig. 4 C specifically illustrates an effect of image smoothing. In the top left part of Fig. 4 C, an image of a test element, such as a lateral flow assay, e.g. for virus detection, comprising a sharp edge and a non-insignificant level of noise, i.e. signal-to-noise ratio, is represented. In the top right part of Fig. 4 C, the same image, but after being smoothed with a Gaussian filter (sigma = 2), resulting in a blurred image, is represented. The bottom part of Fig. 4 C shows a signal, i.e. pixel values, of a single row from the green channel of the image before and after compression.

[0138] The image before compression (top left-hand side of Fig. 4 C) is compared to the same image after being smoothed with the Gaussian filter (top right-hand side of Fig. 4 C). Fig. 4 D shows a comparison of the radial average of the two-dimensional Fourier transform of the images before and after compression. From Fig. 4 D, it can be deduced that, after compression, some information in the higher spatial frequencies is lost from the original image. This is because the intensity of the Fourier transform depicted for the smoothed image is found to be at higher spatial frequencies below the intensity of the Fourier transform depicted for the non-smoothed image, indicating that less information is contained at said higher spatial frequencies.

[0139] In conclusion, it may be noted that the exponent that best describes the radial average decay of the two-dimensional Fourier transform is a suitable measure in order to identify optimal and non-optimal, or suboptimal, sharpness. In this example using a lateral flow assay (LFA) for virus detection, particularly, the sharpness of a control line of the LFA can be employed for this purpose. Hence, the radial average of the two-dimensional Fourier transform of an image may be used to derive an image quality value QV4 for the image quality parameter QP4, which can be compared to a pre -determined threshold value TV(QP4). Based on the comparison, an individual numerical quality assessment value IAV(QP4) may be assigned to QP4.

[0140] In Fig. 5, an embodiment of a kit 148 and a mobile device, specifically configured for performing an analytical measurement, is illustrated in a perspective view. The kit 148 comprises a mobile device 112 and at least one optical test element 118. The mobile device 112, having a camera 114, may further comprise a processor 149. The mobile device 112, specifically by using a processor 149, may be configured for performing the methods described herein. The optical test element 118 may be an optical test strip. In particular, the optical test element 118 specifically has at least one reagent test region 120, wherein the at least one reagent test region 120 is configured for performing at least one optical detection reaction in the presence of the analyte. To this end, the reagent test region 120 may contain at least one test chemical for detecting at least one analyte in the sample of bodily fluid. The optical test element 118 further may contain a reference field 121. The mobile device 112, as illustrated in Fig. 5, may capture the at least one image 124 received in step (i), by using the camera 114, wherein the image 124 comprises at least a part of the reagent test region 120 of the optical test element 118 having the sample of the bodily fluid applied thereto.

[0141] Fig. 6 illustrates a flow chart of an exemplary embodiment for carrying out the method, specifically the computer-implemented method, for determining an item of information about a quality of an image according to the present invention, e.g. by making use of the kit as shown in Fig. 5.

[0142] Herein, the item of information about the quality of the image relates to the suitability of the image to be used in an analytical measurement method for detecting at least one analyte in a bodily fluid. In such an analytical measurement method, the at least one analyte is detected from the at least one reagent test region 120 of the optical test element 120. The at least one reagent test region 120 is configured for performing at least one optical detection reaction in the presence of the analyte. Specifically, the item of information about the quality of the image received in step (i) may comprise Boolean information. More specifically, said Boolean information may indicate that the quality of the image is sufficient for use of the image in the analytical measurement method for detecting the at least one analyte in the bodily fluid. Alternatively, said Boolean information may indicate that the quality of the image is not sufficient for use of the image in the analytical measurement method.

[0143] The method for determining an item of information about a quality of an image is performed by using the mobile device 112 having at least one camera 114. Specifically, the mobile device 112 has at least one processor 149. The method comprises, in a first step i), depicted with reference numeral 100 in Fig. 6, receiving, specifically by the processor 149 of the mobile device 112, the at least one image 124 captured by the camera 114 of the mobile device 112. Herein, the image 124 comprises at least a part of the reagent test region 120 of the optical test element 118. The reagent test region 120 has a sample of the bodily fluid applied thereto.

[0144] The method further comprises, in step ii), depicted with reference numeral 200 in Fig. 6, deriving, from the image received in step (i), independently from one another, an image quality value QVx for each of at least two different image quality parameters QPx.

[0145] The at least two different image quality parameters QPx are selected from a pre -determined set of image quality parameters QP 1 to QPn, wherein x is a numerical value from 1 to n, and wherein n is the total number of image quality parameters contained in the pre -determined set of image quality parame- ters. For example, if n = 3, then the pre-determined set of image quality parameters comprises, specifically consists of, 3 image quality parameters, namely QP1, QP2, and QP3. Further, in step ii), the at least two of the image quality parameters QPx, independently from one another, are configured to assess at least one of: a color characteristic of an image, and a spatial characteristic of an image. Specifically, the at least two of the image quality parameters QPx, independently from one another, are configured to assess at least one of: illumination inhomogeneity, maximum illumination, noise, and sharpness.

[0146] In general, the image quality parameters QPx, specifically illumination inhomogeneity, maximum illumination, noise, and sharpness, are quantifiable. In particular, they are quantified by the image quality values QVx derived in step ii).

[0147] The method further comprises, in step iii), depicted with reference numeral 300 in Fig. 6, comparing each of the image quality values QVx derived in step (ii), independently from one another, to one or more pre-determined threshold values TV(QPx). The pre-determined threshold values TV(QPx) are numerical values, which may be selected, or determined, by a series of experimental data, e.g. from a calibration curve, prior to carrying out the method of the present invention. Each of the pre-determined threshold values TV(QPx) is adapted to the specific image quality value QVx. As has been demonstrated in the examples herein above, e.g. with respect to Fig. 1 A to 1 C, Fig. 2 A to 2 C, and Fig. 3 A to 3 C, more than one pre-determined threshold value TV(QPx) may be used for one, or more, e.g. 2, or 3, or all, of the image quality parameters QPx.

[0148] Further, in step iii), based on the comparing of the image quality values QVx derived in step (ii) to the one or more pre-determined threshold values TV(QPx), an individual numerical quality assessment value IAV(QPX) is assigned, independently from one another, to each of the at least two different image quality parameters QPx of step (ii). Herein, the individual numerical quality assessment values are selected from a pre-determined set of individual numerical quality assessment values. As an example only, the pre-determined set of individual numerical quality assessment values may comprise, or consist of, e.g. the numbers 0 and 1; 0, 1, and 2; 0, 1, 2, and 3. Specifically, any specific individual numerical quality assessment value IAV(QPX), such as 0, 1 or 2, may represent a specific category of, or level of, a quality in respect of a corresponding image quality parameter QPx. For example, a specific individual numerical quality assessment value IAV(QPX) of 0 may represent a category of, or a level of, an insufficient quality in respect of a corresponding image quality parameter QPx. Likewise, an IAV(QPX) of 1 or 2, may represent different specific categories of, or levels of, e.g. a high quality (e.g. IAV(QPX) = 1), or a very high quality (e.g. IAV(QPX) = 2), respectively, in respect of a corresponding image quality parameter QPx.

[0149] Optionally, the method may further comprise, if at least one, e.g. 1, 2, 3, or 4, or all, of the individual numerical quality assessment values IAV(QPX) assigned in step (iii) to one or more of the image quality parameters QPx of step (ii) corresponds to a pre-determined category of an insufficient quality in respect of the image quality parameter QPx affected, an additional step iii. l), and / or an additional step iii.2), as follows: iii. 1) receiving another image according to step i); or iii.2) aborting the method.

[0150] Specifically, herein, step iii. l) may be performed once, or more than once, before step iii.2) is performed. Additionally or alternatively, step iv) of the method may be performed only if none of the individual numerical quality assessment values IAV(QPX) assigned in step (iii) to the image quality parameters QPx of step (ii) corresponds to a pre-determined category of an insufficient quality in respect of any of the image quality parameters QPx.

[0151] The method further comprises, in step iv), depicted with reference numeral 400 in Fig. 6, deriving, from the individual numerical quality assessment values IAV(QPX) assigned in step (iii), an overall image quality assessment value OAV. In step (iv), deriving the overall image quality assessment value OAV may comprise combining, specifically by applying a mathematical function, such as an addition, two or more, e.g. all, of the individual numerical quality assessment values IAV(QPX) assigned in step (iii). For example, in step ii), for three image quality parameters QP1, QP2, and QP3, image quality values QV1, QV2, and QV3 may have been derived; and, based on the comparing of each of said image quality values QV1, QV2, and QV3 to one or more pre-determined threshold values TV(QPl), TV(QP2), and TV(QP3), an individual numerical quality assessment value lAV(QPl), IAV(QP2), and IAV(QP3), respectively, may have been assigned to each of the image quality parameters QP1, QP2, and QP3, respectively. Thus, if lAV(QPl) = 1, IAV(QP2) = 1, and IAV(QP3) = 2, then an overall image quality assessment value OAV = 4 may be derived in step iv), e.g. by addition of the individual numerical values of lAV(QPl), IAV(QP2), and IAV(QP3). Or, if lAV(QPl) = 1, IAV(QP2) = 0, and IAV(QP3) = 1, then an overall image quality assessment value OAV = 2 may be derived in step iv) by way of addition. Further, in step iv), the method comprises comparing the overall image quality assessment value OAV derived in step iv) to at least one overall pre-determined threshold value OTV. The overall predetermined threshold value OTV is a numerical value, which may be selected, or determined, by a series of experimental data, prior to carrying out the method of the present invention. Still further, in step iv), the method comprises, based on the comparing of OAV with OTV, determining the item of information about the quality of the image received in step (i).

[0152] As a first example in this regard, an overall image quality assessment value OAV = 4 may have been derived in step iv), as exemplified herein above, and an overall pre-determined threshold value OTV may be a numerical value of OTV = 3; then comparing OAV to OTV results in OTV < OAV (i.e. 3 < 4). Since the overall image quality assessment value OAV, in this example, has passed the overall predetermined threshold value OTV, this may indicate that the level, or category, of quality of the image received in step ii) is high (or good, or acceptable; whatever name, or label, may be applied to this specific quality level) and thus, particularly sufficient with respect to the suitability of the image to be used in an analytical measurement method for detecting at least one analyte in a bodily fluid. Thus, specifically, in this first example in this regard, the item of information about the quality of the image received in step (i) may be determined, in step iv), to be sufficient with respect to the suitability of the image to be used in an analytical measurement method for detecting at least one analyte in a bodily fluid.

[0153] As a second example in this regard, an overall image quality assessment value OAV = 2 may have been derived in step iv), as exemplified herein above as well; with a given overall pre-determined threshold value OTV of 3 (i.e. OTV = 3), the comparing of OAV to OTV results in OAV < OTV (i.e. 2 < 3). ). Since the overall image quality assessment value OAV, in this example, has not passed the overall pre-determined threshold value OTV, this may indicate that the level, or category, of quality of the image received in step ii) is poor, and thus insufficient, particularly insufficient with respect to the suitability of the image to be used in an analytical measurement method for detecting at least one analyte in a bodily fluid. Thus, specifically, in this second example in this regard, the item of information about the quality of the image received in step (i) may be determined, in step iv), to be insufficient with respect to the suitability of the image to be used in an analytical measurement method for detecting at least one analyte in a bodily fluid. Optionally, the method may further comprise, if the overall image quality assessment value OAV derived in step iv) corresponds to a pre-determined category of an insufficient quality in respect of the at least one overall pre-determined threshold value OTV, an additional step iv.l), and / or an additional step iv.2), as follows: iv.l) obtaining another image according to step i); or iv.2) aborting the method.

[0154] Specifically, herein, step iii. l) may be performed once, or more than once, before step iii.2) is performed.

[0155] Fig. 7 illustrates a flow chart of an exemplary embodiment for carrying out the analytical measurement method, specifically a computer-implemented method and / or an in-vitro method, for detecting at least one analyte in a sample of a bodily fluid according to the present invention, e.g. by making use of the kit 148 as shown in Fig. 5.

[0156] The analytical measurement method for detecting at least one analyte in a sample of a bodily fluid is performed by using the mobile device 112 having at least one camera 114, and specifically at least one processor 149. Herein, the at least one analyte is detected from at least one reagent test region 120 of an optical test element 118. The at least one reagent test region 120 is configured for performing at least one optical detection reaction in the presence of the analyte.

[0157] The analytical measurement method comprises, in a first step I), depicted by reference numeral 500, performing the method for determining an item of information about a quality of the image 124 received in step (i).

[0158] The analytical measurement method further comprises, in step II), depicted by reference numeral 600, determining, in step iv), whether or not the item of information about the quality of the image captured in step (i) indicates that the quality of the image, received in step i), is sufficient for use of the image in the analytical measurement method for detecting the at least one analyte in the bodily fluid.

[0159] If, as a result of said determination, the item of information about the quality of the image captured in step (i) indicates that the quality of the image, received in step i), is not sufficient for use of the image in the analytical measurement method, then either another image may be received, e.g. by repeating step i), or the method may be aborted. Specifically, if another image should be received, a prompt message indicating so may be displayed, e.g. on a display of the mobile device 112. Otherwise, if the method is aborted, a message may be displayed, e.g. on the display of the mobile device 112, informing about a poor quality of the image received in step i).

[0160] Alternatively, if, as a result of said determination in step iv), the item of information about the quality of the image captured in step (i) indicates that the quality of the image, received in step i), is sufficient for use of the image in the analytical measurement method, then, in step II), the at least one analyte is detected. To this end, in step II), the image received in step (i) is evaluated, and the analyte is detected from the at least one optical detection reaction at the at least one reagent test region 120 of the optical test element 118. Specifically, the detecting the at least one analyte in the bodily fluid may comprise determining a concentration of the analyte in the bodily fluid.

[0161] Furthermore, the present invention comprises the following embodiments:

[0162] Embodiment 1 : A method, specifically a computer-implemented method, for determining an item of information about a quality of an image, wherein the item of information about the quality of the image relates to a suitability of the image to be used in an analytical measurement method for detecting at least one analyte in a bodily fluid, wherein the at least one analyte is detected from at least one reagent test region of an optical test element, and wherein the at least one reagent test region is configured for performing at least one optical detection reaction in the presence of the analyte; by using a mobile device having at least one camera, the method comprising:

[0163] (i) receiving at least one image captured by using the camera, wherein the image comprises at least a part of the reagent test region of the optical test element having the sample of the bodily fluid applied thereto;

[0164] (ii) deriving, from the image received in step (i), independently from one another, an image quality value QVx for each of at least two different image quality parameters QPx, selected from a predetermined set of image quality parameters QP1 to QPn, wherein x is a numerical value from 1 to n, and wherein n is the total number of image quality parameters contained in the pre-determined set of image quality parameters, wherein the at least two of the image quality parameters QPx, independently from one another, are configured to assess at least one of: a color characteristic of an image, and a spatial characteristic of an image; (iii) comparing each of the image quality values QVx derived in step (ii), independently from one another, to one or more pre-determined threshold values TV(QPx); and, based on the comparing, assigning, independently from one another, an individual numerical quality assessment value IAV(QPX) to each of the at least two different image quality parameters QPx of step (ii), wherein the individual numerical quality assessment values are selected from a pre -determined set of individual numerical quality assessment values; and

[0165] (iv) deriving, from the individual numerical quality assessment values IAV(QPX) assigned in step (iii), an overall image quality assessment value OAV; comparing said overall image quality assessment value OAV to at least one overall pre -determined threshold value OTV; and, based on the comparing, determining the item of information about the quality of the image received in step (i).

[0166] Embodiment 2: The computer-implemented method according to embodiment 1, wherein the item of information about the quality of the image received in step (i) comprises Boolean information, specifically Boolean information indicating that the quality of the image is either sufficient for use of the image in the analytical measurement method for detecting the at least one analyte in the bodily fluid, or that the quality of the image is not sufficient for use of the image in the analytical measurement method.

[0167] Embodiment 3: The computer-implemented method according to embodiment 1 or 2, wherein the at least two of the image quality parameters QPx, independently from one another, are configured to assess at least one of: illumination inhomogeneity, maximum illumination, noise, and sharpness.

[0168] Embodiment 4: The computer-implemented method according to any one of the preceding embodiments, wherein, the set of individual numerical quality assessment values of step (iii) comprises, for at least two of the image quality parameters QPx, independently from one another, at least a first individual numerical quality assessment value LAVI (QPx) and a second individual numerical quality assessment value IAV2(QPx), wherein lAVl(QPx) < IAV2(QPx), and wherein the first lAVl(QPx) corresponds to a pre-determined category of an insufficient quality in respect of an image quality parameter QPx; wherein the set of individual numerical quality assessment values further may comprise, for the at least two image quality parameters QPx, independently from one another, one or more additional individual numerical quality assessment values, e.g. a third individual numerical quality assessment value IAV3(QPx), wherein lAVl(QPx) < IAV2(QPx) < IAV3(QPx); wherein the image quality in respect of an image quality parameter QPx is evaluated the better the higher the actual numerical value of a specific individual numerical quality assessment value, such as lAVl(QPx), IAV2(QPx) and IAV3(QPx), is.

[0169] Embodiment 5: The computer-implemented method according to any one of the preceding embodiments, further comprising, if at least one of the individual numerical quality assessment values IAV(QPX) assigned in step (iii) to the image quality parameters QPx of step (ii) corresponds to a predetermined category of an insufficient quality in respect of the image quality parameter QPx affected,

[0170] 111.1) receiving another image according to step i); or

[0171] 111.2) aborting the method.

[0172] Embodiment 6: The computer-implemented method according to any one of the preceding embodiments, further comprising, if the overall image quality assessment value OAV derived in step iv) corresponds to a pre -determined category of an insufficient quality in respect of the at least one overall pre-determined threshold value OTV, iv. 1) receiving another image according to step i); or iv.2) aborting the method.

[0173] Embodiment 7: The computer-implemented method according to any one of the two preceding embodiments, wherein steps iii. l), iii.2), iv.l) and / or iv.2) are performed only if at least one, specifically both, of the following criteria is met:

[0174] - a pre-determined period of time, starting with obtaining, for the first time, the at least one image in step i), has not yet elapsed;

[0175] - a pre -determined maximum number of attempts to perform the computer-implemented method has not yet been exceeded.

[0176] Embodiment 8: The computer-implemented method according to any one of the preceding embodiments, wherein step iv) is performed only if none of the individual numerical quality assessment values IAV(QPX) assigned in step (iii) to the image quality parameters QPx of step (ii) corresponds to a pre-determined category of an insufficient quality in respect of any of the image quality parameters QPx. Embodiment 9: The computer-implemented method according to any one of the preceding embodiments, wherein, in step (iv), deriving the overall image quality assessment value OAV comprises combining, by applying a mathematical function, two or more, specifically all, of the individual numerical quality assessment values IAV(QPX) assigned in step (iii).

[0177] Embodiment 10: A computer-implemented analytical measurement method for detecting at least one analyte in a sample of a bodily fluid by using a mobile device having at least one camera, wherein the at least one analyte is detected from at least one reagent test region of an optical test element, and wherein the at least one reagent test region is configured for performing at least one optical detection reaction in the presence of the analyte, the method comprising:

[0178] I) performing the computer-implemented method of any one of embodiments 1 to 9 for determining an item of information about a quality of the image received in step (i);

[0179] II) if the item of information about the quality of the image captured in step (i) is determined, in step iv), to indicate that the quality of the image is sufficient for use of the image in the analytical measurement method for detecting the at least one analyte in the bodily fluid, detecting, by evaluating the image captured in step (i), the at least one analyte from the at least one optical detection reaction at the at least one reagent test region of the optical test element.

[0180] Embodiment 11 : The computer-implemented analytical measurement method according to any one of the preceding embodiments referring to an analytical measurement method, wherein detecting the at least one analyte in the bodily fluid comprises determining a concentration of the analyte in the bodily fluid.

[0181] Embodiment 12: The computer-implemented analytical measurement method according to any one of the preceding embodiments referring to an analytical measurement method, wherein the optical test element is selected from the group comprising a test strip, a test assay, specifically a lateral flow assay, a test stick, specifically a test dipstick, a test cassette, a test tape, a test paper, and a test chip.

[0182] Embodiment 13: The computer-implemented analytical measurement method according to any one of the preceding embodiments referring to an analytical measurement method, wherein the method further comprises providing the optical test element. Embodiment 14: A mobile device having at least one camera and at least one processor, the mobile device being configured for

[0183] - determining an item of information about a quality of an image; wherein the mobile device further is configured for performing at least steps i) to iv) of the computer-implemented method according to any one of embodiments 1 to 9; or

[0184] - detecting a concentration of an analyte in a bodily fluid, wherein the at least one analyte is detected from an optical detection reaction at a reagent test region; wherein the mobile device further is configured for performing at least steps I) to II) of the computer-implemented analytical measurement method according to any one of embodiments 10 to 13.

[0185] Embodiment 15: A kit, comprising the mobile device according to embodiment 14, and at least one optical test element having at least one reagent test region, wherein the at least one reagent test region is configured for performing at least one optical detection reaction in the presence of the analyte.

[0186] Embodiment 16: A computer program comprising instructions which, when the program is executed by the mobile device according to embodiment 14, cause the mobile device to carry out

[0187] - at least steps i) to iv) of the computer-implemented method according to any one of embodiments 1 to 9; or

[0188] - at least steps I) to II) of the computer-implemented analytical measurement method according to any one of embodiments 10 to 13.

[0189] Embodiment 17: A computer-readable storage medium comprising instructions which, when executed by the mobile device according to embodiment 14, cause the mobile device to carry out

[0190] - at least steps i) to iv) of the computer-implemented method according to any one of embodiments 1 to 9; or

[0191] - at least steps I) to II) of the computer-implemented analytical measurement method according to any one of embodiments 10 to 13. List of reference numbers

[0192] 112 mobile device

[0193] 114 camera

[0194] 118 optical test element

[0195] 120 reagent test region

[0196] 121 reference field

[0197] 124 image

[0198] 148 kit

[0199] 149 processor

[0200] 100 step i) of the method for determining an item of information about a quality of the image received in step (i)

[0201] 200 step ii) of the method for determining an item of information about a quality of the image received in step (i)

[0202] 300 step iii) of the method for determining an item of information about a quality of the image received in step (i)

[0203] 400 step iv) of the method for determining an item of information about a quality of the image received in step (i)

[0204] 500 step I) of the analytical measurement method

[0205] 600 step II) of the analytical measurement method

Claims

Claims1. A computer-implemented method for determining an item of information about a quality of an image,- wherein the item of information about the quality of the image relates to a suitability of the image to be used in an analytical measurement method for detecting at least one analyte in a bodily fluid, wherein the at least one analyte is detected from at least one reagent test region of an optical test element, and wherein the at least one reagent test region is configured for performing at least one optical detection reaction in the presence of the analyte; by using a mobile device having at least one camera, the method comprising:(i) receiving at least one image captured by using the camera, wherein the image comprises at least a part of the reagent test region of the optical test element having the sample of the bodily fluid applied thereto;(ii) determining, from the image received in step (i), independently from one another, an image quality value QVx for each of at least two different image quality parameters QPx, selected from a pre-determined set of image quality parameters QP1 to QPn, wherein x is a numerical value from 1 to n, and wherein n is the total number of image quality parameters contained in the pre-determined set of image quality parameters, wherein the at least two of the image quality parameters QPx, independently from one another, are configured to assess at least one of: a color characteristic of an image, and a spatial characteristic of an image;(iii) comparing each of the image quality values QVx determined in step (ii), independently from one another, to one or more pre -determined threshold values TV(QPx); and, based on the comparing, assigning, independently from one another, an individual numerical quality assessment value IAV(QPX) to each of the at least two different image quality parameters QPx of step (ii), wherein the individual numerical quality assessment values are selected from a pre-determined set of individual numerical quality assessment values; and(iv) deriving, from the individual numerical quality assessment values IAV(QPX) assigned in step (iii), an overall image quality assessment value OAV; comparing said overall image quality assessment value OAV to at least one overall pre-determined threshold value OTV; and, based on the comparing, determining the item of information about the quality of the image received in step (i), wherein step iv) is performed only if none of the individual numerical quality assessment values IAV(QPX) assigned in step (iii) to the image quality parameters QPx of step (ii) corresponds to apre -determined category of an insufficient quality in respect of any of the image quality parameters QPx.

2. The computer-implemented method according to claim 1, wherein the item of information about the quality of the image received in step (i) comprises Boolean information, specifically Boolean information indicating that the quality of the image is either sufficient for use of the image in the analytical measurement method for detecting the at least one analyte in the bodily fluid, or that the quality of the image is not sufficient for use of the image in the analytical measurement method.

3. The computer-implemented method according to claim 1 or 2, wherein the at least two of the image quality parameters QPx, independently from one another, are configured to assess at least one of: illumination inhomogeneity, maximum illumination, noise, and sharpness.

4. The computer-implemented method according to any one of the preceding claims, wherein, the set of individual numerical quality assessment values of step (iii) comprises, for at least two of the image quality parameters QPx, independently from one another, at least a first individual numerical quality assessment value lAVl(QPx) and a second individual numerical quality assessment value IAV2(QPx), wherein lAVl(QPx) < IAV2(QPx), and wherein the first lAVl(QPx) corresponds to a pre-determined category of an insufficient quality in respect of an image quality parameter QPx; wherein the set of individual numerical quality assessment values further may comprise, for the at least two image quality parameters QPx, independently from one another, one or more additional individual numerical quality assessment values, e.g. a third individual numerical quality assessment value IAV3(QPx), wherein lAVl(QPx) < IAV2(QPx) < IAV3(QPx); wherein the image quality in respect of an image quality parameter QPx is evaluated the better the higher the actual numerical value of a specific individual numerical quality assessment value, such as lAVl(QPx), IAV2(QPx) and IAV3(QPx), is.

5. The computer-implemented method according to any one of the preceding claims, further comprising, if at least one of the individual numerical quality assessment values IAV(QPX) assigned in step (iii) to the image quality parameters QPx of step (ii) corresponds to a pre-determined category of an insufficient quality in respect of the image quality parameter QPx affected, iii.l) receiving another image according to step i); oriii.2) aborting the method.

6. The computer-implemented method according to any one of the preceding claims, further comprising, if the overall image quality assessment value OAV derived in step iv) corresponds to a pre -determined category of an insufficient quality in respect of the at least one overall predetermined threshold value OTV, iv.l) receiving another image according to step i); or iv.2) aborting the method.

7. The computer-implemented method according to any one of the two preceding claims, wherein steps iii.1), iii.2), iv. 1) and / or iv.2) are performed only if at least one, specifically both, of the following criteria is met: a) a pre-determined period of time, starting with obtaining, for the first time, the at least one image in step i), has not yet elapsed; b) a pre-determined maximum number of attempts to perform the computer-implemented method has not yet been exceeded.

8. The computer-implemented method according to any one of the preceding claims, wherein, in step (iv), deriving the overall image quality assessment value OAV comprises combining, by applying a mathematical function, two or more, specifically all, of the individual numerical quality assessment values IAV(QPX) assigned in step (iii).

9. A computer-implemented analytical measurement method for detecting at least one analyte in a sample of a bodily fluid, wherein the detecting may comprise determining a concentration of the analyte in the bodily fluid, by using a mobile device having at least one camera, wherein the at least one analyte is detected from at least one reagent test region of an optical test element, and wherein the at least one reagent test region is configured for performing at least one optical detection reaction in the presence of the analyte, the method comprising:I) performing the computer-implemented method of any one of claims 1 to 8 for determining an item of information about a quality of the image received in step (i);II) if the item of information about the quality of the image captured in step (i) is determined, in step iv), to indicate that the quality of the image is sufficient for use of the image in the ana-lytical measurement method for detecting the at least one analyte in the bodily fluid, detecting, by evaluating the image captured in step (i), the at least one analyte from the at least one optical detection reaction at the at least one reagent test region of the optical test element.

10. The computer-implemented analytical measurement method according to claim 9, wherein the method further comprises providing the optical test element.

11. A mobile device having at least one camera and at least one processor, the mobile device being configured for- determining an item of information about a quality of an image; wherein the mobile device further is configured for performing at least steps i) to iv) of the computer-implemented method according to any one of claims 1 to 8; or- detecting a concentration of an analyte in a bodily fluid, wherein the at least one analyte is detected from an optical detection reaction at a reagent test region; wherein the mobile device further is configured for performing at least steps I) to II) of the computer-implemented analytical measurement method according to any one of claims 9 to 10.

12. A kit, comprising the mobile device according to claim 11, and at least one optical test element having at least one reagent test region, wherein the at least one reagent test region is configured for performing at least one optical detection reaction in the presence of the analyte.

13. A computer program comprising instructions which, when the program is executed by the mobile device according to claim 11, cause the mobile device to carry out- at least steps i) to iv) of the computer-implemented method according to any one of claims 1 to 8; or- at least steps I) to II) of the computer-implemented analytical measurement method according to any one of claims 9 to 10.

14. A computer-readable storage medium comprising instructions which, when executed by the mobile device according to claim 11, cause the mobile device to carry out at least steps i) to iv) of the computer-implemented method according to any one of claims 1 to 8; orat least steps I) to II) of the computer-implemented analytical measurement method according to any one of claims 9 to 10.

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