Method for determining an item of information on the quality of an image

Abstract

The present invention relates to a computer-implemented method for determining information items about the quality of an image using a mobile device having at least one camera, wherein the information items about the quality of the image relate to the suitability of the image for use 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 area of ​​an optical testing element; the method comprising: (i) receiving at least one image captured using the camera, wherein the image includes at least a portion of the reagent test area of ​​the optical testing element to which a sample of the bodily fluid has been applied; (ii) from the image received in step (i), independently deriving 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 the at least two different image quality parameters QPx are independently configured to evaluate at least one of: the color characteristics of the image and the spatial characteristics of the image; (iii) taking the image from step (i) and then taking the image from step (i) and ... Each of the image quality values ​​QVx obtained in step (ii) is independently compared with one or more predetermined thresholds TV (QPx); and based on the comparison, individual numerical quality rating values ​​IAV (QPx) are independently assigned to each of the at least two different image quality parameters QPx in step (ii), wherein the individual numerical quality rating values ​​are selected from a predetermined set of individual numerical quality rating values; and (iv) an overall image quality rating value OAV is derived from the individual numerical quality rating values ​​IAV (QPx) assigned in step (iii); the overall image quality rating value OAV is compared with an overall predetermined threshold OTV; and based on the comparison, the information item regarding the quality of the image received in step (i) is determined, wherein step (iv) is performed only if none of the individual numerical quality rating values ​​IAV (QPx) assigned to the image quality parameter QPx in step (iii) in step (ii) corresponds to a predetermined category of insufficient quality associated with any of the image quality parameters QPx.

Description

Field of the Invention

[0001] The present invention relates to a method for determining an item of information about the quality of an image by using at least one mobile device having a camera; and a method for determining the concentration of an analyte in a body fluid, the method making use of the method for determining an item of information about the quality of an image. Further, the present invention relates to a mobile device having a camera for implementing these methods, to a kit comprising a mobile device having a camera, to a computer program and to a computer-readable storage medium. The method, the mobile device, the computer program and the storage medium are specifically useful for medical diagnosis, for example in order to qualitatively or quantitatively detect one or more analytes in a body fluid, such as for detecting glucose or viral load in blood or interstitial fluid. Background Art

[0002] In the field of medical diagnosis, in many cases, it is necessary to detect one or more analytes in a sample of a body fluid such as blood, interstitial fluid, urine, saliva or other types of body fluid. Examples of analytes to be detected are glucose, triglycerides, lactate, cholesterol or other types of analytes commonly present in these body fluids (such as one or more types of virus). If necessary, appropriate treatment may be selected based on the concentration and / or presence of the analyte.

[0003] Generally, devices and methods known to the person skilled in the art use test elements containing one or more test chemicals which are capable of performing one or more detectable detection reactions, such as optically detectable detection reactions, in the presence of the analyte to be detected. With regard to the test chemicals contained in the test elements, reference may be made, for example, 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 (for example for detecting viruses, in particular by using lateral flow assays) are known to the person skilled in the art.

[0004] In analytical measurements, specifically in analytical measurements based on color formation reactions, a technical challenge lies in the assessment of the color change caused by the detection reaction. In addition to using dedicated analytical devices such as handheld analytical meters (such as blood glucose meters), in recent years, the use of general-purpose electronic devices such as smart phones and portable computers or other mobile devices has become increasingly common.

[0005] Unlike laboratory measurements and measurements performed using dedicated analytical measuring devices, measurements using mobile computing devices such as smartphones require consideration of various additional effects, such as lighting conditions and positioning. Given the wide variety of situations in which such measurements can be performed, these additional effects can be quite difficult to account for. However, to improve the reliability of analyte detection results in these situations, further improvements in accuracy are desired when performing such measurements.

[0006] When performing color-forming response-based analytical measurements using mobile devices (where images of optical test elements are captured by the mobile device's camera), the objective quality of the captured images is a crucial factor. Many different aspects, including, in particular, ambient lighting conditions and camera settings during image capture, can affect the objective quality of such images. These factors can negatively impact image characteristics such as noise (also known as "image noise"), illumination uniformity, indistinct intensity speckles, color resolution, spatial resolution, and so on.

[0007] Furthermore, when performing analytical measurements by capturing images of optical test elements using the built-in camera of a mobile device, a color reference card (providing multiple reference colors) is typically used to meet accuracy requirements and to account for device-specific characteristics. Alternatively, such analytical measurements are limited to pre-calibrated or pre-tested mobile devices, such as a limited number of smartphone types or models, to ensure a minimum level of image quality. Without a color reference card, pre-testing for a specific smartphone model can ensure a minimum level of measurement performance, such as reliability or accuracy; however, such pre-testing cannot guarantee adequate functionality for each individual measurement. In particular, when considering influencing factors such as automatic image acquisition settings, various scenes to be captured, and different ambient lighting conditions, any effect of these factors on image compression can lead to variations in image quality and thus measurement performance.

[0008] Generally speaking, in methods that consider image quality, some known factors that affect image quality have been taken into account to some extent in the non-diagnostic domain.

[0009] For example, in the context of facial recognition of a moving person, US11475596B2 describes a method that specifically includes: receiving a first image from an image sequence from a camera device; determining a first portion of a target object detected in the first image; determining a confidence level for a second portion of the target object in the first image and adjusting the configuration of the camera device; processing the image sequence to determine a 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 the images and whether they pass a quality test can be determined; said quality test can assess whether the image includes a face of a moving person that contributes to its successful facial recognition, such as whether the image is overexposed or underexposed, or whether the moving person in the image is blurred or not blurred.

[0010] In the context of financial document transactions, another example is US10803431B2, which describes a method for presenting a graphical user interface on a portable device to guide a user through an image capture process to improve the visibility level of an image, thereby identifying magnetic ink character identification codes used for financial document transactions. This method specifically includes: capturing multiple images of a video feed depicting a financial document using an image sensor of the portable device; determining a visibility level of the financial document in the corresponding images based on alignment; extracting thresholds from a predefined array of thresholds corresponding to multiple image quality parameters, the extracted thresholds corresponding to the alignment; comparing the visibility level with the extracted thresholds; and calculating, based on the results of the analysis, at least one user action required to change the tilt orientation of the portable device to increase the visibility level. Image quality parameters may include brightness parameters, orientation parameters, and / or sharpness parameters; a sufficient visibility level may be evaluated based on matching with predefined thresholds; further, a sufficient visibility level may be evaluated based on matching with a set of thresholds, each threshold corresponding to another value among the image quality parameters, each value being set according to another image quality parameter among the image quality parameters.

[0011] US10171773B2 relates to a computer system for managing dynamic video images, wherein a set of dynamic image quality factors is collected relative to the dynamic video images; and wherein, based on the set of dynamic image quality factors, a set of display parameter values ​​is determined for a set of display parameters of a set of computational assets that benefit the set of dynamic image quality factors in relation to the dynamic video images. The set of dynamic image quality factors can be evaluated relative to a set of image quality factor benchmarks, which may include a quality threshold level. For example, a user may be a participant in a video conference call, and the user and their surroundings may appear unnatural with high-intensity colors; by taking into account the dynamic image quality factors (which may include white balance and saturation factors), the set of display parameters may allow the user's facial features to appear more natural with lower-intensity colors.

[0012] US2022 / 0317050A1 relates to a method for adjusting settings used in an analytical method for determining the concentration of an analyte in a body fluid based on a color-forming reaction in an optical test strip, the analytical method including the use of a mobile device with a camera, wherein the adjustment method includes: a) performing multiple analyte measurement attempts using the mobile device set in a standard measurement mode, the analyte measurement attempts including: capturing an image of the optical test strip, checking for the satisfaction of one or more measurement rejection criteria, and rejecting the measurement attempt and recording the rejection event in a memory when one or more measurement rejection criteria are satisfied; and b) analyzing the recorded rejection events in an error analysis and, based on the results, adjusting the settings by: (i) placing the device in a reduced measurement mode in which one or more measurement rejection criteria are disabled, and / or (ii) placing it in an enhanced measurement mode in which corrective feedback is provided to the user during the measurement attempts.

[0013] In summary, the factors affecting image quality (as far as they have been taken into account in some technical areas of the prior art) have been evaluated only individually (i.e. each factor separately) and usually independently of each other.

[0014] Despite the advantages involved in using mobile computing devices for analytical measurements, one of the remaining technical challenges remains properly evaluating the quality level of images captured by mobile computing devices.

[0015] Problems to be solved Therefore, it is desirable to provide apparatus and methods that at least partially address the aforementioned technical challenges. Specifically, it is desirable to provide apparatus and methods that allow for reliable assessment of the quality level of images captured by a mobile device, enabling accurate measurements to be obtained through mobile-based detection of analytes in bodily fluids. Furthermore, the provided apparatus and methods should reduce the number of failed attempts at mobile-based detection of analytes in bodily fluids, where such failed attempts are at least partially attributable to poor image quality levels. Summary of the Invention

[0016] This problem is addressed by the following features of the independent claims: a method for determining information items regarding the quality of an image using at least one mobile device having at least one camera and specifically at least one processor, specifically a computer-implemented method; further, an analytical measurement method for detecting at least one analyte in a sample of bodily fluid using a mobile device having at least one camera and specifically at least one processor, specifically a computer-implemented analytical measurement method; even further, a mobile device having at least one camera and specifically at least one processor; a kit comprising a mobile device and an optical testing element; and a computer program and a computer-readable storage medium. Advantageous embodiments that may be implemented individually or in any arbitrary combination are set forth in the dependent claims.

[0017] As used below, the terms “have,” “contain,” or “include,” or any grammatical variations thereof, are used in a non-exclusive manner. Thus, these terms can refer both to a situation where no other features exist in the entity described in the context besides those introduced by these terms, and to a situation where one or more other features exist. For example, the statements “A has B,” “A contains B,” and “A includes B” can all refer to a situation where no other elements exist in A besides B (i.e., where A is composed solely and exclusively of B), and to a situation where one or more other elements (such as elements C, C and D, or even other elements) exist in entity A besides B.

[0018] Furthermore, it should be noted that the terms "at least one," "one or more," or similar expressions indicating that a feature or element may exist once or multiple times are generally used only once when the corresponding feature or element is introduced. In the following text, in many cases, when referring to the corresponding feature or element, the expressions "at least one" or "one or more" will not be used repeatedly, even though the corresponding feature or element may exist only once or multiple times.

[0019] Furthermore, as used below, the terms “preferredly,” “more preferably,” “particularly,” “more particularly,” “specifically,” “more specifically,” or similar terms are used in combination with optional features without limiting the possibility of substitution. Therefore, features introduced by these terms are optional features and are not intended to limit the scope of the claims in any way. As those skilled in the art will recognize, the invention can be practiced by using alternative features. Similarly, features introduced by phrases such as “in embodiments of the invention” or similar expressions are intended to be optional features without any limitation on alternative embodiments of the invention, without any limitation on the scope of the invention, and without any limitation on the possibility of combining features introduced in this manner with other optional or non-optional features of the invention.

[0020] In a first aspect, the present invention relates to a method for determining information items concerning the quality of an image using a mobile device having at least one camera and, in particular, at least one processor, specifically a computer-implemented method. Here, the information items concerning the quality of the image relate to the suitability of the image for use in an analytical measurement method for detecting at least one analyte in bodily fluids. The at least one analyte is detected from at least one reagent testing area of ​​an optical testing element. The at least one reagent testing area is configured to perform at least one optical detection reaction in the presence of the analyte.

[0021] This method includes the following steps, which, as an example, can be performed in a given order. However, it should be noted that different orders are also possible. Furthermore, one or more method steps may be performed once or repeatedly. Further, two or more method steps may be performed simultaneously or in a manner that overlaps as appropriate. This method may include other method steps not listed.

[0022] The method includes, in a first step i), specifically by a processor of a mobile device, receiving at least one image captured using a camera of the mobile device. The image includes at least a portion, specifically the entirety, of a reagent testing area of ​​an optical testing element on which a body fluid has been applied.

[0023] The method further includes, in step ii), specifically, the processor of the mobile device independently determining, from the image received in step (i), 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 predetermined set of image quality parameters QP1 to QPn, where x is a value from 1 to n, and where n is the total number of image quality parameters contained in the predetermined set of image quality parameters. Here, the at least two different image quality parameters QPx are independently configured to evaluate at least one of: the color characteristics of the image, such as the color characteristics of the image received in step i); and the spatial characteristics of the image, such as the spatial characteristics of the image received in step i).

[0024] The method further includes, in step iii), specifically by the processor of the mobile device, comparing each of the image quality values ​​QVx obtained in step (ii) independently with one or more predetermined thresholds TV(QPx).

[0025] Step iii) further includes, based on comparison, specifically by the processor of the mobile device, assigning individual numerical quality rating values ​​IAV(QPx) independently to each of the at least two different image quality parameters QPx from step (ii). Herein, the individual numerical quality rating values ​​are selected from a predetermined set of individual numerical quality rating values.

[0026] The method further includes, in step iv), specifically, the processor of the mobile device derives the overall image quality rating OAV from the individual numerical quality rating IAV(QPx) assigned in step (iii).

[0027] Step iv) further includes: specifically, the processor of the mobile device compares the overall image quality rating value OAV with an overall predetermined threshold OTV.

[0028] Step iv) further includes: based on comparison, specifically by the processor of the mobile device, determining information items regarding the quality of the image received in step (i).

[0029] Step iv of the method is performed only if none of the individual numerical quality ratings IAV(QPx) assigned to step (ii) of the image quality parameter QPx in step (iii) corresponds to a predetermined category of insufficient quality associated with any of the image quality parameters QPx.

[0030] As used herein, the term "information item concerning image quality" is a broad term and is given a common and customary meaning to those skilled in the art, and is not limited to a specific or customary meaning. Specifically, the term may refer to, but is not limited to, any piece of information that can represent a given level or degree of quality of an image, specifically an electronic image (e.g., an image captured by a camera of a mobile device). In particular, "image quality" may refer to one or more physical parameters or properties specific to an image; for example, "image quality" may refer to a set (specifically predefined) of parameters, where the parameters may be quantifiable. More specifically, parameters may quantify the level or degree of image quality, for example, according to numerical values ​​indicating a particular level or degree of image quality. Further, physical parameters that can be used to specify "image quality" may specifically include, but are not limited to, any parameters related to the color characteristics and / or spatial characteristics of the image; more specifically, physical parameters may include one or more of the following: illuminance nonuniformity, maximum illuminance, noise, and sharpness. Other physical parameters are also conceivable in this regard. Here, "information item concerning image quality" relates to the suitability of an image for use in an analytical measurement method for detecting at least one analyte in bodily fluids.

[0031] As used herein, the term "suitability of an image for use in an analytical measurement method for detecting at least one analyte in a bodily fluid" is a broad term and is given a common and customary meaning to those skilled in the art, and is not limited to a specific or customary meaning. Specifically, the term may refer to, but is not limited to, an indication that an image can be suitably used in an analytical measurement method involving the detection of at least one analyte in a bodily fluid. In other words, if an image is suitable for use in such an analytical measurement method, then the image, specifically an electronic image, can be used as input to the analytical measurement method and can be further evaluated to obtain analytical measurement results. "Suitability of an image" can be represented by Boolean information, specifically by Boolean information indicating that the quality of the image, specifically the image, is sufficient for use in an analytical measurement method for detecting at least one analyte in a bodily fluid, or that the quality of the image, specifically the image, is insufficient for use in an analytical measurement method.

[0032] As used herein, the term "analytical measurement" (also known as "determination of the concentration of an analyte in body fluids") is a broad term and is given a common and customary meaning to those skilled in the art, and is not limited to a specific or customary meaning. Specifically, the term may refer to, but is not limited to, the quantitative and / or qualitative determination of at least one analyte in any sample or aliquot of body fluids. For example, body fluids may include one or more of blood, interstitial fluid, urine, saliva, or other types of body fluids, particularly blood. As an example, the result of an analytical measurement or determination of concentration may be the concentration of an analyte and / or the presence or absence of the analyte to be determined. In particular, as an example, an analytical measurement may be a blood glucose measurement, thus yielding, for example, a blood glucose concentration. Specifically, the result value of an analytical measurement can be determined by an analytical measurement.

[0033] Therefore, the term "analyte concentration value" (also commonly referred to as "analytical measurement result value") as used herein is a broad term and will be given a common and conventional meaning to those skilled in the art, and is not limited to a specific or customary meaning. The term may specifically refer to, but is not limited to, a numerical indication of the concentration of an analyte in a sample.

[0034] As an example, at least one analyte may be or may include one or more specific chemical compounds and / or other parameters. As an example, one or more analytes involved in metabolism, such as blood glucose, may be identified. Alternatively or additionally, other types of analytes or parameters may be identified, such as pH, viruses (e.g., those affecting type A or B viruses, SARS-CoV-2), etc. Without narrowing the scope, the invention can be specifically described in relation to blood glucose measurements. However, it should be noted that the invention can also be used for other types of analytical measurements using test elements, particularly for analytes other than blood glucose, specifically analytes to be detected by using lateral flow assays, such as various types of viruses.

[0035] In an analytical measurement method for detecting at least one analyte in a body fluid, the analyte is detected from a reagent testing area of ​​an optical testing element. The reagent testing area is configured to perform at least one optical detection reaction in the presence of the analyte. Specifically, the reagent testing area is adapted to apply a sample of the body fluid, and when the sample of the body fluid is applied to the reagent testing area, the reagent testing area is adapted to undergo at least a partial optical detection reaction. The reagent testing area may also be referred to herein as a “test area.” The optical detection reaction may specifically include a “color-forming reaction,” specifically involving a color change. More specifically, the color change involved in the optical detection reaction may result in a change in the color of the reagent testing area, for example, from yellow to green, from white to gray or black, or from white to red or purple. Alternatively or additionally, the optical detection reaction may specifically include the development of a color that is not present prior to the optical detection reaction; this type of optical detection reaction is typically observed using lateral flow measurement. In such lateral flow measurements, typically one or more bands or lines can be observed in the reagent testing area after sample application, and typically after a predetermined waiting period if the test result is positive. The terms “optical detection reaction” and “color formation reaction” can specifically refer to, but are not limited to, chemical, biological, or physical reactions in which the color (specifically, reflectance) of at least one element involved in the reaction changes as the reaction progresses.

[0036] As used herein, the term "optical test element" is a broad term and should be given its common and customary meaning to those skilled in the art, and should not be limited to a specific or customary meaning. The term can specifically refer to, but is not limited to, any element or device configured to perform an optical detection reaction (specifically involving a color change). An optical test element may also be referred to as a test strip or test element, where all three terms can refer to the same element. An optical test element has a reagent test area containing at least one test chemical for detecting at least one analyte. As an example, an optical test element may include at least one substrate, such as at least one carrier, wherein at least one reagent test area is applied thereto or integrated therein. In particular, an optical test element may further include one or more reference areas, such as white areas and / or black areas. Additionally or alternatively, the substrate or carrier itself may be or may include such reference areas. As an example, at least one carrier may be strip-shaped, thus making the test element a test strip. These test strips are commonly used and supplied. A test strip may carry a single test area or multiple test areas, in which the same or different test chemicals are included. Furthermore, lateral flow assays are widely used for a variety of analytes, such as viruses. These lateral flow assays typically consist of a housing carrying a membrane and reagent test areas integrated into or impregnated into the membrane. The construction and design of such lateral flow assays are generally known to those skilled in the art.

[0037] As used further herein, the term "reagent test area" (also referred to herein as "test area") is a broad term and will be given a meaning common and customary to those skilled in the art, and is not limited to a particular or customary meaning. The term may specifically refer to, but is not limited to, the coherence of a test chemical, such as a region or membrane, for example, a region or membrane having one or more layers of material (having a circular, polygonal, or rectangular shape), wherein the test chemical is included in at least one layer of the test area. As an example, regarding test chemicals included in optical test strips, refer to J. Hoenes et al., The Technology Behind Glucose Meters: Test Strips, Diabetes Technology & Therapeutics, Vol. 10, Supplement 1, 2008, pp. 10-26. Other types of test chemicals are also possible and can be used in carrying out this invention.

[0038] As outlined above, the method includes using at least one mobile device having at least one camera. As used herein, the term "mobile device" is a broad term and should be given the common and customary meaning to those skilled in the art, and is not limited to a specific or customary meaning. The term may specifically refer to, but is not limited to, mobile electronic devices, and more specifically, mobile communication devices such as mobile phones and / or smartphones. Alternatively or additionally, a mobile device may also refer to a notebook computer, tablet computer, or other type of portable computer having at least one camera. Thus, in general, a mobile device may be selected from the group consisting of: a mobile phone having at least one camera, specifically a smartphone; and a portable computer having at least one camera, specifically at least one of a notebook computer and a tablet computer. The mobile device may be used specifically in methods for determining information items regarding the quality of an image as described herein, particularly for performing said methods, and / or in analytical measurement methods, particularly for performing said analytical measurement methods, as will be described in further detail below. In addition to at least one camera, the mobile device may also include additional elements such as one or more processors and one or more displays or screens. As an example, a mobile device may include more than one camera, such as a front-facing camera (e.g., arranged on the same side as one or more displays or screens) and a rear-facing camera (e.g., arranged on the side opposite to the side where the front-facing camera is arranged).

[0039] As mentioned above, the mobile device may further include at least one processor. As used herein, the term "processor" is a broad term and is given a common and conventional meaning to those skilled in the art, and is not limited to a specific or customary meaning. Specifically, the term may refer to, but is not limited to, any logical circuit system configured to perform basic operations of a computer or system; and / or, generally, means configured to perform computation or logical operations. In particular, the processor may be configured to process basic instructions that drive the computer or system. As an example, the processor may include at least one arithmetic logic unit (ALU), at least one floating-point unit (FPU) (such as a math coprocessor or numerical coprocessor), multiple registers (specifically registers configured to provide operands to the ALU and store the results of operations), and a memory (such as L1 and L2 cache memories). In particular, the processor may be a multi-core processor. Specifically, the processor may be or may include a central processing unit (CPU). Additionally or alternatively, the processor may be or may include a microprocessor; therefore, specifically, the elements of the processor may be contained within a single integrated circuit (IC) chip. Alternatively or concurrently, the processor may be or may include one or more application-specific integrated circuits (ASICs) and / or one or more field-programmable gate arrays (FPGAs) and / or one or more tensor processing units (TPUs) and / or one or more chips, such as dedicated machine learning optimization chips. Specifically, the processor may be configured (e.g., via software programming) to perform one or more operations for determining image quality and / or detecting analytes, as will be described in further detail below.

[0040] As used herein, the term "camera" is a broad term and will be given a meaning common and customary to those skilled in the art, and is not limited to a particular or customary meaning. Specifically, the term may refer to, but is not limited to, a device having at least one imaging element configured to record or capture spatially resolved one-dimensional, two-dimensional, or even three-dimensional optical data or information. As an example, a camera may include at least one camera chip, such as at least one CCD chip and / or at least one CMOS chip, configured to record an image. As used herein, but not limited to, the term "image" may specifically refer to data recorded using a camera, such as multiple electronic readings from an imaging device, such as pixels of a camera chip.

[0041] In addition to at least one camera chip or imaging chip, the camera may further include additional elements, such as one or more optical elements, for example, one or more lenses. As an example, the camera may be a fixed-focus camera having at least one lens that is fixedly adjusted relative to the camera. Alternatively, however, the camera may also include one or more variable lenses that can be adjusted automatically or manually. This invention is particularly applicable to cameras commonly used in mobile applications, such as laptops, tablets, or especially cellular phones such as smartphones. Therefore, specifically, the camera may be part of a mobile device that, in addition to at least one camera, includes one or more data processing devices such as one or more data processors. However, other cameras are also feasible.

[0042] As outlined above, the method includes, in step i), receiving at least one image captured using a camera of a mobile device. The image includes at least a portion, specifically all, of a reagent testing area of ​​an optical testing element of a sample to which bodily fluid has been applied. As used herein, the term "receiving at least one image" is a broad term and is given a meaning common and customary to those skilled in the art, and is not limited to a specific or customary meaning. The term may specifically refer to, but is not limited to, one or more of imaging, image recording, image acquisition, and image capture. The term "receiving at least one image" may include receiving and / or capturing a single image and / or multiple images, such as a sequence of images. For example, receiving and / or capturing images may include continuously recording a sequence of images, such as video or film. Receiving and / or capturing at least one image may be initiated by a user action, or may be initiated automatically, for example, once at least one object is automatically detected within the field of view and / or within a predetermined sector of the camera's field of view. These automatic image acquisition techniques are known in, for example, the field of automatic barcode readers (such as automatic barcode reading applications). For example, images can be received and / or captured by utilizing a camera to acquire images in a stream or "live stream," wherein one or more images are automatically or through user interaction (such as pressing a button) and stored respectively as at least one first image or at least one second image. Image acquisition can be supported by the processor of the mobile device, and image storage can be performed in the data storage device of the mobile device.

[0043] Receiving and / or capturing at least one image includes receiving and / or capturing at least one image while a sample of bodily fluid is applied to the test strip, and further and optionally, receiving and / or capturing at least one image without a sample of bodily fluid being applied to the test strip, such as capturing an image before the sample is applied to the test strip. The latter image is specifically used for comparative purposes and may also be referred to as a “blank image” or a “dry image.” For example, sample application can typically be performed directly or indirectly, for example, via at least one capillary element. At least one image received and / or captured after sample application may also be referred to as a “wet image,” even if the sample may have dried by the time the image is actually captured. A wet image can typically be received and / or captured after at least a predetermined waiting time (such as after five seconds or more) to allow a detection reaction to occur. Thus, as an example, the method may include waiting for at least a predetermined minimum amount of time between receiving and / or capturing at least one optional dry image and at least one wet image. This predetermined minimum amount of time may specifically be sufficient for a detection reaction to occur in the test strip. As an example, the minimum waiting time could be at least 5 seconds, or up to 15 minutes in the case of a SARS-CoV-2 rapid antigen LFA (lateral flow assay) self-test for patient use.

[0044] Analytical measurement methods for detecting analytes in samples of bodily fluids may include detecting the presence or absence of the analyte. Alternatively or additionally, the analytical measurement method may include determining the analyte concentration, particularly the analyte concentration value, from color formation in a reagent test area. Thus, the analytical measurement method may include a change in at least one optical property of an optical testing element (such as an optical test strip or lateral flow measurement), which can be visually measured or determined using a camera. Specifically, the analytical measurement may be or may include a color formation reaction in the presence of at least one analyte to be determined. As used herein, the term "color formation reaction" has been defined above. Color formation may be detected by a mobile device, such as by a processor of the mobile device, and may be qualitatively (and specifically quantitatively) evaluated, such as by deriving at least one parameter from at least one image that quantifies or characterizes the color formation of the test area due to the presence of the analyte in the bodily fluid. For this purpose, one or more specific color coordinates may be used. Thus, the mobile device, and specifically the processor of the mobile device, may be configured to determine a color change by determining a change in one or more color coordinates that occurs due to the detection reaction.

[0045] The presence or absence of an analyte, or the concentration of an analyte (specifically, the analyte concentration value), is determined from the color formation in the test area. For this purpose, at least one image is used. As an example, the analyte concentration value can be a numerical indicator of the result of an analytical measurement, such as indicating the concentration of at least one analyte in the sample, such as blood glucose concentration or viral load.

[0046] The method may further include the step of displaying information about the quality of the image, such as displaying it on a display of a mobile device. Alternatively or additionally, the method may include storing the information about the image quality in at least one data storage device of the mobile device. Also, additionally and additionally, the method may further include transmitting the information about the image quality via at least one interface and / or via at least one data transmission network, such as transmitting it to another computer, for example, for further evaluation or processing.

[0047] Therefore, in a first aspect, the present invention relates particularly to a method for determining information items concerning the quality of an image, specifically a computer-implemented method. -The information item regarding image quality relates to the suitability of the image for use in an analytical measurement method for detecting at least one analyte in bodily fluids, wherein the at least one analyte is detected from at least one reagent test area of ​​an optical test element, and wherein the at least one reagent test area is configured to perform at least one optical detection reaction in the presence of the analyte; This determination is made using a mobile device having at least one camera, and the method includes: (i) Receive at least one image captured by a camera using a mobile device, wherein the image includes at least a portion, specifically all, of a reagent testing area of ​​an optical testing element of a sample to which bodily fluid has been applied; (ii) From the image received in step (i), independently determine 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, where x is a value from 1 to n, and where n is the total number of image quality parameters contained in the predetermined set of image quality parameters, wherein at least two of the image quality parameters QPx (specifically the at least two different image quality parameters) are independently configured to evaluate at least one of the following: the color characteristics of the image and the spatial characteristics of the image; (iii) Each of the image quality values ​​QVx determined in step (ii) is independently compared with one or more predetermined thresholds TV(QPx); and based on the comparison, an individual numerical quality rating value IAV(QPx) is independently assigned to each of the at least two different image quality parameters QPx in step (ii), wherein the individual numerical quality rating value is selected from a predetermined set of individual numerical quality rating values; and (iv) From the individual numerical quality rating value IAV(QPx) assigned in step (iii), derive the overall image quality rating value OAV; compare the overall image quality rating value OAV with at least one overall predetermined threshold OTV; and based on the comparison, determine the information item regarding the quality of the image received in step (i). Step iv is performed only if any of the individual numerical quality rating values ​​IAV(QPx) assigned to the image quality parameter QPx in step (iii) in step (ii) does not correspond to a predetermined category of insufficient quality associated with any of the image quality parameters QPx.

[0048] The proposed method provides a reliable assessment of the quality level of images captured by mobile devices, an important aspect of mobile-based analytical measurements where analytes are to be accurately detected in bodily fluids using mobile consumer devices such as smartphones, tablets, or laptops. Specifically, the method considers a selected set of image quality attributes (such as the image quality parameter QPx) that can be quantified using simple algorithms. The “optimal” (i.e., high-quality) range of values ​​for each parameter or quality attribute is taken into account, where the optimal range contributes to improving the overall level of image quality. Through the combination of selected parameters or quality attributes, the overall level of image quality (such as an overall image quality rating) can be derived (particularly through calculation), thereby improving the overall performance of subsequent analytical measurements.

[0049] Furthermore, the selected set of parameters or quality attributes used in the method of this invention differs from other methods in that, most commonly, quantifying image quality aims to optimize the pleasing quality of an image to human perception, such as when photographs are taken in everyday situations (e.g., during holidays, celebrations, or events). However, in the field of analytical measurement, the goal is to achieve accurate and reliable measurement performance of algorithms implemented for the detection of analytes. In view of this goal, the set of physical parameters or quality attributes used to determine image quality is selected based on the measurement performance of such algorithms used to detect analytes, specifically, where the algorithms consider a broad set of data, particularly image data, in which a wide range of image aberrations (i.e., deviations from "high-quality" images, where "high-quality" refers to the objectively high and quantifiable quality of the image, such as having a high signal-to-noise ratio) may exist. Specifically, for analyte measurements involving the detection of analytes based on color-forming reactions, the measurement performance of the algorithm used to detect the analytes will depend particularly on the accurate and reliable determination of the colors formed during the color-forming reaction. Therefore, the set of parameters or quality attributes to be used in the method of this invention is specifically selected to achieve or support the accurate and reliable determination of the colors formed during the color-forming reaction. More specifically, since the parameters or quality attributes (i.e., image quality parameters QPx) are quantifiable, their evaluation (by assigning individual numerical quality rating values ​​IAV(QPx) to each of QPx) allows the analytical measurement method of the present invention to be performed with sufficient accuracy and reliability, even if the individual numerical quality rating value IAV(QPx) may indicate that the level or category of quality of the image received in step i) relative to one or more of the image quality parameters QPx is poor or even insufficient.

[0050] Furthermore, the provided apparatus and method help reduce the number of failed attempts at mobile-device-based detection of analytes in body fluids, where such failures are at least partly attributable to poor image quality. This additional advantage is achieved by appropriately balancing the influence of each of the selected parameters or quality attributes when deriving an overall image quality rating, so as to assess the overall image quality for the intended purpose: i.e., utilizing the image in the (subsequent) analytical detection of analytes in body fluids.

[0051] Specifically, information items regarding the quality of the image captured in step (i) may include Boolean information such as “sufficient” or “insufficient”; more specifically, Boolean information indicating that the quality of the image is sufficient for use in an analytical measurement method for detecting at least one analyte in body fluids, or that the quality of the image is insufficient for use in an analytical measurement method.

[0052] In this method, the image quality parameter QPx is selected from a predetermined set of image quality parameters QP1 to QPn, which may involve any physical parameter suitable for characterizing (specifically quantifying) aspects of the quality of the image received in step i). In step ii), at least two (specifically all) of the image quality parameters QPx are configured to evaluate at least one of the following: the color characteristics of the image, specifically the color characteristics of the image received in step i); and the spatial characteristics of the image, specifically the spatial characteristics of the image received in step i). More specifically, at least two (or even more specifically all) of the image quality parameters QPx may be configured independently of each other to evaluate at least one of the following: illuminance nonuniformity, maximum illuminance, noise, and sharpness. Advantageously, the predetermined set of image quality parameters QP1 to QPn includes, specifically, consists of: illuminance nonuniformity, maximum illuminance, noise, and sharpness.

[0053] In step ii), from the image received in step (i), an image quality value QVx is determined independently of each of at least two different image quality parameters QPx. The image quality parameter QPx is a physical parameter relating to aspects of image quality and is quantifiable (particularly by the image quality value QVx). The image quality value QVx can be a numerical value corresponding to each of the respective image quality parameters in the image quality parameter QPx, or can be represented by that numerical value. Thus, for example, if the image quality parameter QPx relates to the color characteristics of the image, such as an illuminance non-uniformity parameter, the image quality parameter QPx can be quantified, for example, by evaluating the distribution of luminance across a region of the image representing a white area of ​​a test element (such as a lateral flow measurement) or test box. As is known to those skilled in the art, luminance is an intensity that can be measured or determined in one or more color channels (depending on the color space used), for example, in a single color channel (such as any of the R, G, or B channels in the RGB color space, or any of the X, Y, or Z channels in the XYZ color space); any combination of two or more color channels is also possible to derive an intensity value. If a luminance curve, i.e., a luminance distribution along a specific direction in a two-dimensional image (specifically along the direction of lateral flow in lateral flow measurement), is found to have a wide range in the region, then a significant amount of illuminance non-uniformity exists in the image. In this regard, the range of the luminance curve can be derived by determining the difference between the minimum and maximum values ​​of the luminance curve, i.e., maximum (luminance curve) - minimum (luminance curve). In this document, the luminance curve should be smoothed to a certain extent to avoid any undesirable effects of noise or outliers.

[0054] The broad range of the brightness curve is reflected by the corresponding image quality value QVx, which can be a relatively high value, particularly higher than, for example, the image quality value QVx representing a very narrow range of the brightness curve of an image or a portion of an image. Similarly, the image quality value QVx can be different values ​​or can be represented by different values, each corresponding in each case to a corresponding one of other image quality parameters QPx, such as when the image quality parameter QPx is selected from one or more of maximum illuminance, noise, and sharpness. For example, the maximum illuminance of an image can be used to identify dark or saturated images or dark or saturated sub-regions within an image. In this regard, the distribution of brightness across regions representing white areas of a test element (such as lateral flow measurement) in the image can be further used to filter out outliers and / or to reliably detect maximum intensity. As another example, noise is typically the signal-to-noise ratio, or is represented by that signal-to-noise ratio. Another example is sharpness, where image power spectral density, in particular, reflects the effects of poor focusing during image capture or smoothing applied to the image; thus, image power spectral density provides an image quality measure independent of any reference card or smartphone-specific pre-test or pre-calibration. Especially in images containing sharp edges, smoothing or extensive image compression can be detected from a two-dimensional Fourier transform. The information contained in the two-dimensional Fourier transform can be radially averaged (especially when centered at the 0,0 frequency). Such radial averaging reduces dimensionality and facilitates the interpretation of the image's spectral characteristics. Lossy compression of an image (i.e., image compression in which relevant portions of the information contained in the uncompressed image are lost during the compression process) typically alters the image's power spectral density. Such alterations can be derived from the radial average of the two-dimensional Fourier transform. In general, any of the image quality parameters QPx envisioned herein (specifically, illumination nonuniformity, maximum illumination, noise, and sharpness), and the determination of such parameters (especially their quantification, e.g., by determining a value for the image quality value QVx), are generally known in the art. Typically, illuminance or intensity is measured in counts, for example, 0 to 255 for each color channel. If a 16-bit image is used, 0 to 65535 counts per color channel can be obtained. Specifically, for image noise, the standard deviation or variance of the counts within a uniform region of interest (ROI) (e.g., a white ROI, specifically a white ROI without specific features such as geometric elements like lines) can be used as the unit, for example, in counts or variances. 2 To measure.

[0055] Once an image quality value QVx (i.e., a numerical value) has been determined for each of at least two different image quality parameters QPx in step ii), then in step iii), each of the image quality values ​​QVx determined in step (ii) is independently compared to one or more predetermined thresholds TV(QPx). The predetermined thresholds TV(QPx) are numerical values. Therefore, the full range of numerical values ​​that can be obtained as the result of an evaluation (e.g., measurement, analysis, or mathematical calculation) of a particular image quality parameter QPx, in the form of image quality values ​​QVx, will be divided into two or more (e.g., two or three) numerical subranges. For example, if only one threshold TV(QPx) exists, then the threshold TV(QPx) will divide the theoretically achievable range of (numerical) image quality values ​​QVx for a specific image quality parameter QPx into two numerical subranges. One numerical subrange is below one threshold TV(QPx), and the other numerical subrange is above one threshold TV(QPx). One of the (numerical) thresholds TV(QPx) will be a part of only one of the two subranges. If more than one threshold TV(QPx) exists for a given image quality parameter QPx, such as two, then there will be at least three subranges of (numerical) image quality values ​​QVx.

[0056] Specifically, a predetermined threshold TV(QPx) can be independently selected for each of the image quality parameters QPx, such that the resulting subranges of image quality values ​​QVx can be categorized according to different levels of (objective) image quality. Therefore, a first subrange of image quality values ​​QVx can represent a category of insufficient quality related to the corresponding image quality parameter QPx, while a second subrange of image quality values ​​QVx can represent a category of acceptable quality related to the image quality parameter QPx. Having more than two subranges of image quality values ​​QVx allows for the differentiation of several acceptable levels of image quality. Therefore, in addition to the first subrange of image quality values ​​QVx representing a category of insufficient quality related to the corresponding image quality parameter QPx, the second and third subranges of image quality values ​​QVx can also represent categories of acceptable quality and high quality related to the image quality parameter QPx, respectively. Specifically, since in step iii), each of the image quality values ​​QVx determined in step (ii) is independently compared with one or more predetermined thresholds TV(QPx), the number of thresholds TV(QPx) selected for a given image quality parameter QPx can differ from the number of thresholds TV(QPx) selected for another image quality parameter QPx. Therefore, for the first image quality parameter QPx, only one threshold TV(QPx) can be selected, while for the second image quality parameter QPx, two thresholds TV(QPx) can be selected.

[0057] Specifically, a predetermined threshold TV (QPx) can be selected, determined, or defined to be suitable for a particular image quality parameter QPx. Therefore, the predetermined threshold TV (QPx) can be independently selected, determined, or defined for each of the image quality parameters QPx by considering one or more of the following: experimental data, research data, data from laboratory working examples, physical considerations, signal processing considerations, algorithmic considerations, and any combination thereof. Such physical considerations may include, for example: whether any pixel or a significant number of pixels are saturated or zero; and whether any smoothing applied to the image is too extensive and / or whether the sharpness is too low to obtain physically meaningful results. Further, such signal processing considerations may include, for example: whether a particular illumination non-uniformity present in the image can be corrected by signal processing; and whether a particular amount of image noise present in the image can be taken into account by signal processing. Furthermore, such algorithms may consider factors such as whether a particular image quality value QVx can prevent the algorithm from detecting geometric elements in the image, such as lines; and whether a particular QVx may induce flawed or erroneous detections, such as the detection of a particular color or the presence or absence of a geometric element, both of which can correspond to the presence or absence of the object to be detected.

[0058] Typically, any value chosen or determined for a predetermined threshold can be validated by testing a specific algorithm used to evaluate 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 typically includes images with different levels of quality relative to one or more image quality parameters QPx (such as different levels of image noise, applied smoothing, and / or intensity gradients). To create a suitable set of image test data, different levels of quality can be artificially introduced, such as by reducing (e.g., progressively) the signal-to-noise ratio of the image, by gradually applying smoothing to the image, or by adding an intensity gradient to the image. Thus, one or more series of test data can be generated with decreasing (e.g., continuously decreasing) image quality values ​​QVx. By testing the algorithm using such series of test data, any range of image quality values ​​QVx, such as insufficient, acceptable, or high-quality ranges, can be identified, depending on whether the algorithm can produce any physically meaningful results.

[0059] Further, in step iii), based on a comparison of the image quality value QVx determined in step (ii) with one or more predetermined thresholds TV(QPx), individual numerical quality rating values ​​IAV(QPx) are independently assigned to each of the at least two different image quality parameters QPx in step (ii). Hereinafter, the individual numerical quality rating values ​​are selected from a predetermined set of individual numerical quality rating values. The predetermined set of individual numerical quality rating values ​​may include or consist of the following: for example, the numbers 0 and 1; 0, 1 and 2; 0, 1, 2 and 3. While the inclusion of the number 0 in the predetermined set of individual numerical quality rating values ​​is not mandatory, in some cases including the number 0 may prove convenient and therefore efficient; particularly if the number 0 is used for a category of insufficient quality associated with the corresponding image quality parameter QPx.

[0060] Typically, individual numerical quality rating values ​​IAV(QPx) are assigned independently to each other, corresponding to each of several sub-ranges of the image quality value QVx, as envisioned above. Alternatively, a particular individual numerical quality rating value IAV(QPx) (such as 0, 1, or 2) may represent a specific category or level of quality associated with the corresponding image quality parameter QPx. For example, a particular individual numerical quality rating value IAV(QPx) 0 may represent a category or level of insufficient quality associated with the corresponding image quality parameter QPx. Similarly, a particular individual numerical quality rating value IAV(QPx) (such as 1 or 2) may respectively represent different specific categories or levels of acceptable quality (e.g., IAV(QPx) = 1) or high quality (e.g., IAV(QPx) = 2) associated with the corresponding image quality parameter QPx.

[0061] In one aspect of the present invention, a set of individual numerical quality assessment values for step (iii) may include at least a first individual numerical quality assessment value IAV1(QPx) and a second individual numerical quality assessment value IAV2(QPx) independently of each other for at least two (specifically all) of the image quality parameters QPx, where IAV1(QPx) < IAV2(QPx). Here, the first IAV1(QPx) corresponds to a predetermined category of insufficient quality related to the corresponding image quality parameter QPx. In addition, the set of individual numerical quality assessment values may independently of each other include one or more additional individual numerical quality assessment values, such as a third individual numerical quality assessment value IAV3(QPx), for at least one of the at least two image quality parameters QPx (e.g., for one or both, or for all), where IAV1(QPx) < IAV2(QPx) < IAV3(QPx). Generally, the better the image quality related to the image quality parameter QPx can be evaluated, the higher the actual value of a specific individual numerical quality assessment value (such as IAV1(QPx), IAV2(QPx), and IAV3(QPx)). It will be apparent to those skilled in the art that other ways can be conceived to assign the individual numerical quality assessment value IAV(QPx) to a predetermined category of the quality of an image, such as the category of "insufficient quality" related to the corresponding image quality parameter QPx; wherein, the assessment of the image quality related to the image quality parameter QPx can be evaluated differently, for example, the better the evaluation, the lower the actual value of a specific individual numerical quality assessment value, as in the case of assigning negative numerical values to the individual numerical quality assessment value IAV(QPx).

[0062] For illustrative purposes only, some examples of image quality values ​​QVx (e.g., QV1), predetermined thresholds TV(QPx) (e.g., TV(QP1)), and individual numerical quality ratings IAV(QPx) (e.g., IAV(QP1)) can be considered and illustrated using arbitrary numbers, relative to one of the image quality parameters QPx (e.g., the first image quality parameter QP1). Thus, two thresholds TV(QP1) can be chosen relative to QP1, for example, a first threshold TV1(QP1) = 1 and a second threshold TV2(QP1) = 5. The full range of image quality values ​​QV1 obtainable for QP1 can include any value in the range of 0 to 10. Therefore, thresholds TV1(QP1) and TV2(QP1) divide 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 the image quality value QV1 can correspond to a different one of the three individual numerical quality assessment values ​​IAV(QP1). Therefore, the first sub-range of the image quality value QV1, which includes any value below the first threshold TV1(QP1), can correspond to the first individual numerical quality assessment value IAV1(QP1), where IAV1(QP1) = 0. The second sub-range of the image quality value QV1, which includes any value equal to or higher than the first threshold TV1(QP1) but lower than the second threshold TV2(QP1), can correspond to the second individual numerical quality assessment value IAV1(QP1), where IAV2(QP1) = 1. The third sub-range of the image quality value QV1, which includes any value equal to or higher than the second threshold TV2(QP1), can correspond to the third individual numerical quality assessment value IAV3(QP1), where IAV3(QP1) = 2. In this paper, each of the three subranges of the image quality value QV1, and therefore each of the three individual numerical quality ratings IAV1(QP1), IAV2(QP1), and IAV3(QP1), can correspond to a different category or level of quality associated with the corresponding image quality parameter QP1. This example can be summarized as follows: -Consider the specific image quality parameter QP1; - The full range of image quality values ​​QV1 available for QP1 includes any value in the range of 0 to 10; - Select or determine the two predetermined thresholds as TV1(QP1) = 1 and TV2(QP1) = 5 respectively; - If QV1 < 1, then assign the value IAV1(QP1) 0 to QP1; -If 1 ≤ QV1 < 5, then assign the value IAV2(QP1) 1 to QP1; - If 5 ≤ QV1 < 10, then assign the value IAV3(QP1) 2 to QP1.

[0063] - Individual numerical quality ratings IAV1(QP1), IAV2(QP1) and IAV3(QP1) correspond to the categories or levels of inadequate quality (IAV1(QP1) = 0), acceptable quality (IAV2(QP1) = 1), and high quality (IAV3(QP1) = 2) associated with QP1, respectively.

[0064] In step iv), the overall image quality rating (OAV) is derived from the individual numerical quality ratings (IAV) (QPx) assigned in step (iii). Herein, deriving OAV may include, specifically, combining two or more (specifically all) of the individual numerical quality ratings (IAV) (QPx) assigned in step (iii) by applying a mathematical function. Specifically, the mathematical function may be, or may include, any basic or more complex type of calculation, including but not limited to one or more of the following: addition, subtraction, multiplication, division, etc., and / or any combination thereof. For illustrative purposes only, the mathematical function may be a simple addition of the individual numerical quality ratings (IAV) (QPx) assigned in step (iii); or the mathematical function may be an addition of the squares of the individual numerical quality ratings (IAV) (QPx) assigned in step (iii); or the mathematical function may be a multiplication of the IAV (QPx) values. Other alternatives for the mathematical function may be conceived.

[0065] Specifically, deriving OAV in step iv) may include combining two or more (especially all) of the individual numerical quality rating values ​​IAV(QPx) assigned in step (iii) by applying a mathematical function (e.g., addition). In cases where two image quality parameters QPx are considered (such as a first image quality parameter QP1 and a second image quality parameter QP2), in step ii), image quality values ​​QV1 and QV2 are determined independently from the image received in step (i) for each of QP1 and QP2. In step iii), each of QV1 and QV2 determined in step (ii) is compared independently to one or more predetermined thresholds TV(QP1) and TV(QP2). For example, for QP1, only one threshold TV(QP1) may be selected or determined, while for QP2, two TV1(QP2) and TV2(QP2) may be selected or determined. Further, in step iii), based on the comparison in step iii), the individual numerical quality rating values ​​IAV(QPx) are independently assigned to each of QP1 and QP2 in step (ii), for example, in this case, the individual numerical quality rating values ​​are IAV(QP1) and IAV(QP2). For QP1, one of the two IAV(QP1) values ​​can be obtained because there is only one threshold TV(QP1). For QP2, since there are two thresholds, one of the three IAV(QP2) values ​​can be obtained. Therefore, the overall image quality rating value OAV can be obtained by mathematical addition, i.e., by adding one of the two IAV(QP1) values ​​to one of the three IAV(QP2) values. Assuming that two IAV(QP1) values ​​can be 0 or 1, and that three IAV(QP2) values ​​can be any of 0, 1, or 2, OAV can be one of the following values, i.e., the result of adding two values: 0 (if IAV(QP1) = IAV(QP2) = 0), 1 (if IAV(QP1) = 0 and IAV(QP2) = 1, or vice versa), 2 (if IAV(QP1) = 0 and IAV(QP2) = 2, or if IAV(QP1) = IAV(QP2) = 1), or 3 (if IAV(QP1) = 1 and IAV(QP2) = 2).

[0066] Further, in step iv), the overall image quality rating value OAV obtained in step iv) is compared with at least one overall predetermined threshold OTV. The overall predetermined threshold OTV is a numerical value, such as 2, 3, 4, or 5, or any other numerical value appropriately selected or determined relative to the individual numerical quality rating values ​​IAV(QPx). Specifically, since OAV is derived from the individual numerical quality rating values ​​IAV(QPx), OAV typically represents a combination of the individual numerical quality rating values ​​IAV(QPx). Therefore, each of the IAV(QPx) contributes to OAV. Specifically, OAV is thus an indicator of the overall level of quality of an image (such as the image received in step i), which takes into account each of the individual numerical quality rating values ​​IAV(QPx). Furthermore, the overall predetermined threshold OTV can be selected or determined as a numerical value that provides an assessment of the overall level of quality of the image (such as the image received in step i), consistent with the individual numerical quality assessment value IAV(QPx) that has been initially selected or determined for each of the image quality parameters QPx.

[0067] In this regard, referring back to the example provided above, where there are two image quality parameters, QP1 and QP2, and the overall image quality rating (OAV) in this setting can be one of the following values: 0, 1, 2, or 3; then in this setting, a value of 0 or 1 for OAV can represent the category or level of inadequate quality related to the overall image quality rating (OAV); while a value of 2 or 3 for OAV can represent the category or level of inadequate quality related to the overall image quality rating (OAV).

[0068] Specifically, OAV is therefore an indication of the suitability of the image received in step (i) for use in the analytical measurement method for detecting at least one analyte in body fluids. Specifically, in step iv), if the overall image quality assessment value OAV is less than at least one overall predetermined threshold OTV, it can be determined that the information item regarding the quality of the image received in step (i) is insufficient for use in the analytical measurement method for detecting at least one analyte in body fluids. Otherwise, if the overall image quality assessment value OAV is equal to or greater than at least one overall predetermined threshold OTV, it can be determined that the information item regarding the quality of the image received in step (i) is sufficient for use in the analytical measurement method for detecting at least one analyte in body fluids. However, those skilled in the art will readily recognize that other methods of applying specific thresholds may also be used, depending in particular on the method used to assign the individual numerical quality rating values ​​IAV(QPx) to each of the image quality parameters QPx in step (ii) in step iii), for example, depending on whether a relatively low value (such as 0) is assigned to a poor or inadequate level of quality, or whether a relatively high value (such as 10) is assigned to a poor or inadequate level of quality; and depending on the method used to derive the overall image quality rating value OAV in step iv) (e.g., by addition, subtraction, multiplication, etc.).

[0069] Optionally, information items regarding the quality of the image received in step (i) can be displayed, for example, on the display of a mobile device. Specifically, if one or more of the individual numerical quality rating values ​​IAV(QPx) assigned in step (iii) and / or the overall image quality rating value OAV obtained in step (iv) indicate the level or category of poor quality of the image relative to one or more of the image quality parameters QPx, then additional information relating to such poor quality of the image received in step (i) may be useful, particularly in conjunction with the analytical measurement method of the present invention. Depending on the degree of such poor quality, such additional information may include notes or warnings. For example, if one or more of the individual numerical quality rating values ​​IAV(QPx) assigned in step (iii) correspond to an insufficient level or category of quality of the image relative to the image quality parameter QPx, then, for example, the following warning may be displayed on the display of a mobile device: the results of detection of the analyte obtained by performing the analytical measurement method of the present invention may be inaccurate; or the detection of the analyte cannot be performed. Alternatively or additionally, if one or more of the individual numerical quality rating values ​​(IAV) (QPx) assigned in step (iii) correspond to a poor but still adequate level or category of image quality relative to the image quality parameter QPx, the following note may be displayed, for example, on the display of the mobile device: The results of the detection of the analyte obtained by performing the analytical measurement method of the present invention may have reduced accuracy. Furthermore, alternatively or additionally, one or more prompts may be displayed, for example, on the display of the mobile device, specifically instructing the user of the method to receive or capture another image with improved quality relative to one or more specific image quality parameters QPx. For example, if the image initially received in step i) includes a relatively high level of illuminance inhomogeneity, such a prompt may include a suggestion to avoid any shadows or external light within the camera's field of view. As another example, if the image initially received in step i) includes a relatively high level of noise, such a prompt may include, for example, a suggestion to capture another image but repeat step i) in a brighter environment.

[0070] However, the method may further include if at least one of the individual numerical quality rating values ​​IAV(QPx) assigned to the image quality parameter QPx in step (iii) in step (ii) corresponds to a predetermined category of insufficient quality related to the affected image quality parameter QPx, then iii.1) Obtain another image according to step i); or iii.2) Abort the method.

[0071] In particular, in this document, step iii.1) may be performed once or more before step iii.2).

[0072] As outlined above, step iv) is performed only if none of the individual numerical quality rating values ​​IAV(QPx) assigned to the image quality parameter QPx in step (iii) in step (ii) corresponds to a predetermined category of insufficient quality associated with any of the image quality parameters QPx. As an example, if one or more of the individual numerical quality rating values ​​IAV(QPx) assigned in step (iii) correspond to a predetermined category of insufficient quality associated with one or more of the image quality parameters QPx in step ii), for example, if at least one of the IAV(QPx) assigned for the image quality parameter QPx in step (iii) yields a value of 0, and said value 0 represents a category of insufficient quality associated with said at least one of the image quality parameters QPx, then step iv) will not be performed; instead, either step iv.1) or iv.2) may be performed.

[0073] Alternatively, the method may further include if the overall image quality rating OAV obtained in step iv) corresponds to a predetermined category of insufficient quality related to at least one overall predetermined threshold OTV, then iv.1) Obtain another image according to step i); or iv.2) Abort the method.

[0074] Specifically, in this article, step iv.1) may be performed once or more before step iv.2).

[0075] More specifically, steps iii.1), iii.2), iv.1), and / or iv.2) may be performed only if one (or both) of the following criteria are met: - The predetermined time period has not elapsed since the first acquisition of the at least one image in step i); - The predetermined maximum number of attempts to implement this computer-based method has not yet been exceeded.

[0076] In a second aspect of the invention, an analytical method for determining the concentration of an analyte in bodily fluids is disclosed, specifically a computer-implemented analytical method and / or specifically an in vitro analytical method, the method comprising using a mobile device having at least one camera (specifically at least one processor). Hereinafter, at least one analyte is detected from at least one reagent testing area of ​​an optical testing element. The at least one reagent testing area is configured to perform at least one optical detection reaction in the presence of the analyte.

[0077] This method includes the following steps, which, as an example, can be performed in a given order. However, it should be noted that different orders are also possible. Furthermore, one or more method steps may be performed once or repeatedly. Further, two or more method steps may be performed simultaneously or in a manner that overlaps as appropriate. This method may include other method steps not listed.

[0078] The analytical measurement method includes, in the first step (i), performing the method described above for determining information items regarding the quality of the image received in step (i).

[0079] The analytical measurement method further includes: if information regarding 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 in the analytical measurement method for detecting at least one analyte in body fluids, then at least one analyte is detected from at least one optical detection reaction at at least one reagent test area of ​​an optical test element by evaluating the image captured in step (i), specifically by the processor of the mobile device.

[0080] Therefore, in a second aspect, the present invention particularly relates to an analytical measurement method for detecting at least one analyte in a sample of bodily fluid using a mobile device having at least one camera and specifically at least one processor, specifically a computer-implemented analytical measurement method and / or specifically an in vitro analytical measurement method, wherein at least one analyte is detected from at least one reagent testing area of ​​an optical testing element, and wherein at least one reagent testing area is configured to perform at least one optical detection reaction in the presence of the analyte, the method comprising: I) Perform the method described above for determining information items regarding the quality of the image received in step (i); II) If the information item regarding the quality of the image captured in step (i) is determined in step (iv) to be sufficient to indicate that the quality of the image is adequate for use in an analytical measurement method for detecting at least one analyte in body fluids, then at least one analyte is detected from at least one optical detection reaction at at least one reagent test area of ​​the optical test element by evaluating the image captured in step (i), specifically by the processor of the mobile device.

[0081] In step II), detecting at least one analyte in the body fluid may include determining the presence or absence of the analyte in the body fluid. Further, in step II), detecting at least one analyte in the body fluid may include determining the concentration of the analyte in the body fluid.

[0082] In analytical measurement methods, optical test elements can be selected from the group consisting of: test strips, test measurements (specifically lateral flow measurements), test bars (specifically test dip bars), test boxes, test strips, test papers, and test chips. Specifically, test strips and / or lateral flow measurements can be used.

[0083] The analytical measurement method may further include providing optical test elements.

[0084] The analytical measurement method may further include the following steps: for example, displaying the results of the analytical measurement on a display of the mobile device, such as an indication of the presence or absence of an analyte or an indication of an analyte concentration value. Optionally, in addition to displaying the results of the analytical measurement, notifications and / or warnings may be displayed depending on one or more of the individual numerical quality assessment values ​​(IAVs) (QPx) and the overall image quality assessment value (OAV). For example, if one or more of the individual numerical quality assessment values ​​(IAVs) (QPx) assigned in step iii) indicate a category or level of poor quality related to one or more of the image quality parameters (QPx), a warning may be displayed indicating that the results of the analytical measurement may be flawed or erroneous; or a notification may be displayed prompting the user to repeat the analytical measurement. Alternatively or additionally, the method may include storing the results (such as analyte concentration values) in at least one data storage device of the mobile device. Also, additionally and additionally, the method may further include transmitting the results (e.g., analyte concentration values) via at least one interface and / or via at least one data transmission network, such as to another computer, for example, for further evaluation or processing.

[0085] 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 to: - Determine information items regarding the quality of the image; wherein the moving device is further configured to perform at least steps i) to iv) of the method for determining information items regarding the quality of the image as described above; or - Detecting the concentration of an analyte in a body fluid, wherein at least one analyte is detected by an optical detection reaction at a reagent test area; wherein the moving device is further configured to perform at least steps I) to II) of the analytical measurement method as described above.

[0086] In another aspect, the present invention relates to a kit comprising a mobile device as described above and at least one optical testing element having at least one reagent testing area, wherein the at least one reagent testing area is configured to perform at least one optical detection reaction in the presence of an analyte.

[0087] In another aspect, the present invention relates to a computer program comprising instructions. - When the procedure is executed by a mobile device as described above, the instructions cause the mobile device to perform at least steps i) to iv) of the method for determining information items regarding the quality of an image as described above; or - When the procedure is executed by the mobile device as described above, the instruction causes the mobile device to perform at least steps I) to II) of the analytical measurement method as described above.

[0088] In another aspect, the present invention relates to a computer-readable storage medium including instructions. - When executed by the mobile device as described above, this instruction causes the mobile device to perform at least steps i) to iv) of the method described above for determining information items regarding the quality of an image; or - When executed by the mobile device as described above, this instruction causes the mobile device to perform at least steps I) to II) of the analytical measurement method as described above.

[0089] The proposed method and apparatus provide a reliable assessment of the quality level of images captured by a mobile device, particularly for mobile-based analytical measurements requiring accurate detection of analytes in bodily fluids using a mobile device. By specifically selecting combinations of parameters or quality attributes, the overall level of image quality can be derived, thereby improving the overall performance of subsequent analytical measurements. Furthermore, the provided apparatus and method help reduce the number of failed attempts at mobile-based detection of analytes in bodily fluids, where these failures are at least partially attributable to poor image quality. Attached Figure Description

[0090] Figure 1AThe image quality parameter QPx is shown as an example where QPx is an illumination non-uniformity parameter, demonstrating extremely low illumination non-uniformity.

[0091] Figure 1B The image quality parameter QPx is shown as an example where QPx is an illumination non-uniformity parameter, illustrating low illumination non-uniformity.

[0092] Figure 1C The image quality parameter QPx is shown as an example where QPx is an illumination non-uniformity parameter, illustrating moderate illumination non-uniformity.

[0093] Figure 2A The image quality parameter QPx is shown as another example where QPx is the maximum illumination parameter, demonstrating the maximum illumination within the optimal range.

[0094] Figure 2B The image quality parameter QPx is shown as another example where QPx is the maximum illumination parameter, demonstrating the maximum illumination within the suboptimal range.

[0095] Figure 2C The image quality parameter QPx is shown as another example where QPx is the maximum illumination parameter, showing the maximum illumination within the saturation range.

[0096] Figure 3A The image quality parameter QPx is shown as another example where QPx is a noise parameter, demonstrating an extremely high signal-to-noise ratio.

[0097] Figure 3B The image quality parameter QPx is shown as another example where QPx is a noise parameter, demonstrating a moderate signal-to-noise ratio.

[0098] Figure 3C The image quality parameter QPx is shown as another example where QPx is a noise parameter, demonstrating a low signal-to-noise ratio.

[0099] Figure 4A The image quality parameter QPx is shown as another example of a sharpness parameter, specifically demonstrating the effect of image compression.

[0100] Figure 4B The sharpness parameters from the example in Figure 4A are further shown.

[0101] Figure 4C The image quality parameter QPx is shown as another example of the sharpness parameter, specifically demonstrating the effect of image smoothing.

[0102] Figure 4D Further showcase from Figure 4CThe example's clarity parameter.

[0103] Figure 5 An embodiment of the kit and mobile device for performing analytical measurements is shown in perspective view.

[0104] Figure 6 A flowchart illustrating an exemplary embodiment of the method for implementing a method for determining information items about the quality of an image according to the present invention.

[0105] Figure 7 shows a flowchart of an exemplary embodiment of the analytical measurement method according to the present invention for implementing an analytical measurement method for detecting at least one analyte in a sample of bodily fluids.

[0106] Detailed description of the attached diagram In a method for determining information items related to image quality, at least two of the image quality parameters QPx are independently configured to evaluate at least one of the following: color characteristics of the image and spatial characteristics of the image. Specifically, at least two of the image quality parameters QPx may be independently configured to evaluate at least one of the following: illuminance nonuniformity, maximum illuminance, noise, and sharpness. Thus, as an example, a predetermined set of image quality parameters QPx may include four image quality parameters QPx, specifically illuminance nonuniformity, maximum illuminance, noise, and sharpness. Figures 1A to 4D each illustrate an example of any of the image quality parameters QPx, demonstrating in each case a specific level of quality and / or the effect of that specific level of quality relative to a particular image quality parameter QPx. Some different specific levels of quality may be represented by, or may correspond to, a predetermined set of individual numerical quality ratings. As an example only, the predetermined set of individual numerical quality ratings may include or may consist of the following values: 0, 1, and 2. Specifically, in this example, the values ​​0, 1, and 2 can represent incremental levels of quality relative to a specific image quality parameter QPx.

[0107] Figures 1A to 1C illustrate examples of an image quality parameter QPx, such as QP1, where QP1 is an illuminance non-uniformity parameter. This illuminance non-uniformity parameter can be quantified by evaluating the distribution of luminance across regions of an image representing the white area of a test element. When the luminance curve in these regions is determined to include a wide range, the image includes a significant level of illuminance non-uniformity. Thus, by evaluating the luminance curve of each image, an image quality value QVx, such as QV1, can be derived independently from each of the images in Figures 1A, 1B, and 1C. Specifically, the image quality parameter QP1 as illustrated herein relates to the non-uniformity of luminance, i.e., to the non-uniformity of intensity in the white area of the image, and thus, in other words, to the difference between the brightest and darkest spots in the image.

[0108] For example, Figure 1A shows very low illuminance non-uniformity. Thus, when compared with a first predetermined threshold TV1(QP1), the image quality value QV1 derived from this image can easily exceed the threshold TV1(QP1), e.g., QV1 ≥ TV1(QP1). Based on the comparison of QV1 with TV1(QP1), a separate numerical quality assessment value IAV(QP1) can be assigned to the image quality parameter QP1. For example, IAV(QP1) = 2 can be assigned to QP1, which in this case reflects an extremely good level of quality with respect to the image quality parameter QP1.

[0109] Alternatively, Figure 1B shows low illuminance non-uniformity. Thus, when compared with the predetermined threshold TV1(QP1), the image quality value QV1 derived from this image may not exceed the threshold TV1(QP1), e.g., QV1 < TV1(QP1), but may still exceed a second predetermined threshold TV2(QP1), e.g., TV2(QP1) ≤ QV1 < TV1(QP1). Based on the comparison of QV1 with TV1(QP1) and TV2(QP1), a separate numerical quality assessment value IAV(QP1) different from that assigned previously (with respect to Figure 1A) can be assigned to the image quality parameter QP1. For example, IAV(QP1) = 1 can be assigned to QP1, which in this case reflects an acceptable level of quality with respect to the image quality parameter QP1.

[0110] Still alternatively, FIG. 1C shows medium illuminance non-uniformity. Thus, when compared with a predetermined threshold TV2(QP1), the image quality value QV1 derived from this image may not exceed the threshold TV2(QP1), for example QV1 < TV2(QP1). Based on the comparison of QV1 with TV2(QP1), a separate numerical quality assessment value IAV(QP1) different from that previously assigned (respectively with respect to FIGS. 1A and 1B) may be assigned to the image quality parameter QP1. For example, IAV(QP1) = 0 may be assigned to QP1, which in this case reflects an insufficient level of quality with respect to the image quality parameter QP1. [[ID=I]]

[0111] FIGS. 2A to 2C illustrate another example of an image quality parameter QPx, such as QP2, where QP2 is the maximum illuminance parameter. The maximum illuminance of an image can be used to identify dark or saturated images. In particular, the distribution of luminance across the region of the image representing the white area of the test element can be used to filter out outliers in order to reliably identify any maximum value of luminance. Specifically, thus, the image quality parameter QP2 as illustrated herein relates to the maximum value of luminance. Therefore, by evaluating the maximum illuminance of each image, an image quality value QVx, such as QV2, can be derived independently from each of the images in FIGS. 2A, 2B, and 2C.

[0112] For example, FIG. 2A shows a maximum illuminance within the optimal range. Thus, when compared with a first predetermined threshold TV1(QP2), the image quality value QV2 derived from this image can easily exceed the threshold TV1(QP2), for example QV2 ≥ TV1(QP2). Based on the comparison of QV2 with TV1(QP2), a separate numerical quality assessment value IAV(QP2) may be assigned to the image quality parameter QP2. For example, IAV(QP2) = 2 may be assigned to QP2, which in this case reflects an extremely good level of quality with respect to the image quality parameter QP2.

[0113] Alternatively, FIG. 2B shows the maximum illuminance within the sub-optimal range. Thus, when compared with a predetermined threshold TV1(QP2), the image quality value QV2 derived from this image may not exceed the threshold TV1(QP2), e.g., QV2 < TV1(QP2), but may still exceed a second predetermined threshold TV2(QP2), e.g., TV2(QP2) ≤ QV2 < TV1(QP2). Based on the comparison of QV2 with TV1(QP2) and TV2(QP2), a separate numerical quality assessment value IAV(QP2) different from the one previously assigned (with respect to FIG. 2A) can be assigned to the image quality parameter QP2. For example, IAV(QP2) = 1 can be assigned to QP2, which in this case reflects an acceptable level of quality with respect to the image quality parameter QP2.

[0114] Still alternatively, FIG. 2C shows the maximum illuminance within the saturation range. Thus, when compared with a predetermined threshold TV2(QP2), the image quality value QV2 derived from this image may not exceed the threshold TV2(QP2), e.g., QV2 < TV2(QP2). Based on the comparison of QV2 with TV2(QP2), a separate numerical quality assessment value IAV(QP2) different from the ones previously assigned (with respect to FIGS. 2A and 2B respectively) can be assigned to the image quality parameter QP2. For example, IAV(QP2) = 0 can be assigned to QP2, which in this case reflects a poor and thus insufficient level of quality with respect to the image quality parameter QP2.

[0115] FIGS. 3A to 3C illustrate another example of an image quality parameter QPx, such as QP3, where QP3 is a noise parameter. The noise parameter (also referred to as the "image noise" parameter) can be quantified by evaluating the signal-to-noise ratio of the image. Thus, by evaluating the signal-to-noise ratio of each image, image quality values QVx, such as QV3, can be independently derived from each of the images in FIGS. 3A, 3B, and 3C respectively.

[0116] For example, Figure 3A shows a very high signal-to-noise ratio. Therefore, when compared with the first predetermined threshold TV1(QP3), the image quality value QV3 derived from this image can easily exceed the threshold TV1(QP3), e.g., QV3 ≥ TV1(QP3). Based on the comparison of QV3 with TV1(QP3), a separate numerical quality assessment value IAV(QP3) can be assigned to the image quality parameter QP3. For example, IAV(QP3) = 2 can be assigned to QP3, which in this case reflects an extremely good level of quality with respect to the image quality parameter QP3.

[0117] Alternatively, Figure 3B shows a medium signal-to-noise ratio. Therefore, when compared with the predetermined threshold TV1(QP3), the image quality value QV3 derived from this image may not exceed the threshold TV1(QP3), e.g., QV3 < TV1(QP3), but can still exceed the second predetermined threshold TV2(QP3), e.g., TV2(QP3) ≤ QV3 < TV1(QP3). Based on the comparison of QV3 with TV1(QP3) and TV2(QP3), a separate numerical quality assessment value IAV(QP3) different from that assigned previously (with respect to Figure 3A) can be assigned to the image quality parameter QP3. For example, IAV(QP3) = 1 can be assigned to QP3, which in this case reflects an acceptable level of quality with respect to the image quality parameter QP3.

[0118] Still alternatively, Figure 3C shows a low signal-to-noise ratio. Therefore, when compared with the predetermined threshold TV2(QP3), the image quality value QV3 derived from this image may not exceed the threshold TV2(QP3), e.g., QV3 < TV2(QP3). Based on the comparison of QV3 with TV2(QP3), a separate numerical quality assessment value IAV(QP3) different from that assigned previously (with respect to Figure 3A and Figure 3B respectively) can be assigned to the image quality parameter QP3. For example, IAV(QP3) = 0 can be assigned to QP3, which in this case reflects a poor and thus insufficient level of quality with respect to the image quality parameter QP3.

[0119] Figures 4A through 4D illustrate another example of the image quality parameter QPx, such as QP4, where QP4 is the sharpness parameter. Sharpness can be determined, for example, but not limited to, an evaluation of the image power spectral density. Such an evaluation of the image power spectral density typically reflects any smoothing applied and is therefore independent of any smartphone-specific pre-testing or pre-calibration, providing a measure of the image's quality level without the need for a color reference card. Particularly in images containing one or more sharp edges, smoothing or extensive image compression can be detected from a two-dimensional Fourier transform. The information contained in the two-dimensional Fourier transform can be radially averaged (specifically, in the case where the center is at a frequency of 0,0). Such radial averaging reduces dimensionality. Furthermore, it helps interpret the spectral characteristics of the image. Lossy compression of an image (i.e., where a certain amount of information is lost during image compression) alters the image's power spectral density. Such alteration can be read from the radial average of the two-dimensional Fourier transform. Specifically, the effect of such image compression can be illustrated by comparing the radial average of the two-dimensional Fourier transform for one and the same image before and after compression. Alternatively, the effect of such image smoothing can be illustrated by comparing the radial average of the two-dimensional Fourier transform for one and the same image before and after smoothing or filtering.

[0120] Figure 4A illustrates the effect of image compression in detail. In the upper left portion of Figure 4A, an image representing a test element (such as lateral flow measurement, e.g., for virus detection) is shown, featuring sharp edges and a low level of noise (i.e., signal-to-noise ratio). In the upper right portion of Figure 4A, the same image is shown, but compressed using an algorithm from the Joint Image Experts Group (standard JPEG, 8-bit), resulting in a low quality factor of 25%. The bottom portion of Figure 4A shows a single line of signal from the green channel of the images before and after compression, representing the pixel values.

[0121] The image before compression (top left of Figure 4A) is compared with the same image compressed using the JPEG algorithm (top right of Figure 4A). Figure 4B shows a comparison of the radial averages of the two-dimensional Fourier transforms of the images before and after compression. From Figure 4B, it can be inferred that some information in the higher spatial frequencies is lost from the original image after compression. This is because the intensity of the Fourier transform drawn for the compressed image is found at higher spatial frequencies than the intensity of the Fourier transform drawn for the uncompressed image, indicating that less information is contained at those higher spatial frequencies.

[0122] Figure 4C specifically illustrates the effect of image smoothing. In the upper left portion of Figure 4C, an image representing a test element (such as lateral flow measurement, e.g., for virus detection) is shown, including sharp edges and a low level of noise (i.e., signal-to-noise ratio). In the upper right portion of Figure 4C, the same image is shown, but after smoothing with a Gaussian filter (σ = 2), resulting in a blurred image. The bottom portion of Figure 4C shows a single line of signal from the green channel of the images before and after compression, representing the pixel values.

[0123] The image before compression (top left of Figure 4C) is compared with the same image smoothed with a Gaussian filter (top right of Figure 4C). Figure 4D shows a comparison of the radial averages of the two-dimensional Fourier transforms of the images before and after compression. From Figure 4D, it can be inferred that some information in the higher spatial frequencies is lost from the original image after compression. This is because the intensity of the Fourier transform drawn for the smoothed image is found at higher spatial frequencies than the intensity of the Fourier transform drawn for the unsmoothed image, indicating that less information is contained at those higher spatial frequencies.

[0124] In summary, it can be noted that the exponent that best describes the radial mean decay of the two-dimensional Fourier transform is a suitable measure for identifying optimal and suboptimal or near-optimal sharpness. In this example (virus detection using lateral flow assay (LFA)), specifically, the sharpness of the LFA's control line can be used for this purpose. Therefore, the radial mean of the two-dimensional Fourier transform of the image can be used to derive an image quality value QV4 for the image quality parameter QP4, which can be compared to a predetermined threshold TV(QP4). Based on this comparison, a separate numerical quality rating value IAV(QP4) can be assigned to QP4.

[0125] Figure 5 illustrates, in perspective view, an embodiment of a kit 148 and a mobile device specifically configured for performing analytical measurements. Kit 148 includes a mobile device 112 and at least one optical testing element 118. The mobile device 112, having a camera 114, may further include a processor 149. The mobile device 112 (specifically, by means of the processor 149) may be configured to perform the methods described herein. The optical testing element 118 may be an optical test strip. Specifically, the optical testing element 118 specifically has at least one reagent testing region 120, wherein the at least one reagent testing region 120 is configured to perform at least one optical detection reaction in the presence of an analyte. For this purpose, the reagent testing region 120 may contain at least one test chemical for detecting at least one analyte in a sample of bodily fluids. The optical testing element 118 may further include a reference region 121. As shown in FIG5, the mobile device 112 can capture at least one image 124 received in step (i) by using camera 114, wherein image 124 includes at least a portion of reagent testing area 120 of optical testing element 118 of a sample to which body fluid has been applied.

[0126] Figure 6 shows a flowchart of an exemplary embodiment of a method (specifically a computer-implemented method) for determining information items about the quality of an image according to the present invention, for example by utilizing the kit shown in Figure 5.

[0127] In this document, information regarding image quality relates to the suitability of the image for use in an analytical measurement method for detecting at least one analyte in bodily fluids. In such an analytical measurement method, at least one analyte is detected from at least one reagent testing area 120 of an optical testing element 120. The at least one reagent testing area 120 is configured to perform at least one optical detection reaction in the presence of the analyte. Specifically, information regarding the quality of the image received in step (i) may include Boolean information. More specifically, the Boolean information may indicate that the image quality is sufficient for use in an analytical measurement method for detecting at least one analyte in bodily fluids. Alternatively, the Boolean information may indicate that the image quality is insufficient for use in an analytical measurement method.

[0128] A method for determining information items regarding the quality of an image is performed using a mobile device 112 having at least one camera 114. Specifically, the mobile device 112 has at least one processor 149. The method includes, in a first step i) (depicted by reference numeral 100 in FIG. 6), specifically by the processor 149 of the mobile device 112, receiving at least one image 124 captured by the camera 114 of the mobile device 112. Herein, image 124 includes at least a portion of a reagent testing area 120 of an optical testing element 118. The reagent testing area 120 has a sample to which bodily fluid has been applied.

[0129] The method further includes, in step ii) (depicted by reference numeral 200 in FIG6), determining, independently of each other, an image quality value QVx for each of at least two different image quality parameters QPx from the image received in step (i).

[0130] At least two image quality parameters QPx are selected from a predetermined set of image quality parameters QP1 to QPn, where x is a value from 1 to n, and n is the total number of image quality parameters contained in the predetermined set of image quality parameters. For example, if n = 3, the predetermined set of image quality parameters includes, specifically, three image quality parameters: QP1, QP2, and QP3. Further, in step ii), at least two of the image quality parameters QPx are independently configured to evaluate at least one of the following: the color characteristics of the image and the spatial characteristics of the image. Specifically, at least two of the image quality parameters QPx are independently configured to evaluate at least one of the following: illuminance non-uniformity, maximum illuminance, noise, and sharpness.

[0131] Generally, image quality parameters QPx (specifically, illumination non-uniformity, maximum illumination, noise, and sharpness) are quantifiable. In particular, they are quantified by the image quality value QVx obtained in step ii).

[0132] The method further includes, in step iii) (described by reference numeral 300 in FIG. 6), comparing each of the image quality values ​​QVx obtained in step (ii) independently with one or more predetermined thresholds TV(QPx). The predetermined thresholds TV(QPx) are numerical values ​​that can be selected or determined prior to implementing the method of the invention using a series of experimental data (e.g., from calibration curves). Each of the predetermined thresholds TV(QPx) is suitable for a particular image quality value QVx. As shown in the examples above (e.g., relative to FIG. 1A-1C, FIG. 2A-2C, and FIG. 3A-3C), more than one predetermined threshold TV(QPx) can be used for one or more of the image quality parameters QPx, such as two or three, or all of them.

[0133] Further, in step iii), based on a comparison of the image quality value QVx obtained in step (ii) with one or more predetermined thresholds TV(QPx), individual numerical quality rating values ​​IAV(QPx) are independently assigned to each of the at least two different image quality parameters QPx in step (ii). Hereinafter, individual numerical quality rating values ​​are selected from a predetermined set of individual numerical quality rating values. By way of example only, the predetermined set of individual numerical quality rating values ​​may include or consist of the following: for example, the numbers 0 and 1; 0, 1 and 2; 0, 1, 2 and 3. Specifically, any particular individual numerical quality rating value IAV(QPx) (such as 0, 1, or 2) may represent a particular category or level of quality associated with the corresponding image quality parameter QPx. For example, a particular individual numerical quality rating value IAV(QPx) 0 may represent a category or level of insufficient quality associated with the corresponding image quality parameter QPx. Similarly, IAV(QPx) being 1 or 2 can respectively represent different specific categories or levels of quality, such as high quality (e.g., IAV(QPx) = 1) or very high quality (e.g., IAV(QPx) = 2), related to the corresponding image quality parameter QPx.

[0134] Optionally, if at least one (e.g., 1, 2, 3, or 4, or all of them) of the individual numerical quality rating values ​​IAV(QPx) of one or more of the image quality parameters QPx assigned in step (iii) to step (ii) corresponds to a predetermined category of insufficient quality related to the affected image quality parameter QPx, the method may further include the following additional steps iii.1) and / or iii.2): iii.1) Receive another image according to step i); or iii.2) Abort the method.

[0135] Specifically, in this document, step iii.1) may be performed once or more before step iii.2). Alternatively, step iv) of the method may be performed only if none of the individual numerical quality ratings IAV(QPx) of the image quality parameter QPx assigned to step (ii) in step (iii) corresponds to a predetermined category of inadequate quality associated with any of the image quality parameters QPx.

[0136] The method further includes, in step iv) (depicted by reference numeral 400 in FIG6), deriving an overall image quality rating (OAV) from the individual numerical quality ratings (IAV) (QPx) assigned in step (iii). Determining the overall image quality rating (OAV) in step (iv) may specifically include, by applying a mathematical function, such as addition, combining two or more (e.g., all) of the individual numerical quality ratings (IAV) (QPx) assigned in step (iii). For example, in step ii), image quality values ​​QV1, QV2, and QV3 can be derived for the three image quality parameters QP1, QP2, and QP3; and, based on a comparison of each of the image quality values ​​QV1, QV2, and QV3 with one or more predetermined thresholds TV(QP1), TV(QP2), and TV(QP3), individual numerical quality rating values ​​IAV(QP1), IAV(QP2), and IAV(QP3) can be assigned to each of the image quality parameters QP1, QP2, and QP3 respectively. Therefore, if IAV(QP1) = 1, IAV(QP2) = 1, and IAV(QP3) = 2, then in step iv), the overall image quality rating value OAV = 4 can be derived, for example, by adding the individual values ​​IAV(QP1), IAV(QP2), and IAV(QP3). Alternatively, if IAV(QP1) = 1, IAV(QP2) = 0, and IAV(QP3) = 1, then the overall image quality rating OAV = 2 can be obtained by addition in step iv).

[0137] Further, in step iv), the method includes comparing the overall image quality assessment value OAV obtained in step iv) with at least one overall predetermined threshold OTV. The overall predetermined threshold OTV is a numerical value that can be selected or determined through a series of experimental data before implementing the method of the present invention. Further still, in step iv), the method includes determining an information item regarding the quality of the image received in step (i) based on the comparison between OAV and OTV.

[0138] As a first example in this regard, it can be exemplified as above that in step iv), the overall image quality assessment value OAV = 4 is obtained, and the overall predetermined threshold OTV can be the numerical value OTV = 3; then comparing OAV with OTV gives OTV ≤ OAV (i.e., 3 ≤ 4). Since in this example, the overall image quality assessment value OAV has exceeded the overall predetermined threshold OTV, this can indicate that the level or category of the quality of the image received in step ii) is high (or good, or acceptable; regardless of the name or label, it can be applied to this specific quality level), and thus, particularly with respect to the suitability of the image for use in an analytical measurement method for detecting at least one analyte in a body fluid, it is sufficient. Therefore, specifically, in this first example in this regard, the information item regarding the quality of the image received in step (i) can be determined in step iv) to be sufficient with respect to the suitability of the image for use in an analytical measurement method for detecting at least one analyte in a body fluid.

[0139] As a second example in this regard, it can be exemplified as above that in step iv), the overall image quality assessment value OAV = 2 is obtained; given that the overall predetermined threshold OTV is 3 (i.e., OTV = 3), comparing OAV with OTV gives OAV < OTV (i.e., 2 < 3). Since in this example, the overall image quality assessment value OAV has not exceeded the overall predetermined threshold OTV, this can indicate that the level or category of the quality of the image received in step ii) is poor and thus insufficient, particularly with respect to the suitability of the image for use in an analytical measurement method for detecting at least one analyte in a body fluid. Therefore, specifically, in this second example in this regard, the information item regarding the quality of the image received in step (i) can be determined in step iv) to be insufficient with respect to the suitability of the image for use in an analytical measurement method for detecting at least one analyte in a body fluid.

[0140] Optionally, if the overall image quality rating (OAV) obtained in step iv) corresponds to a predetermined category of insufficient quality related to at least one overall predetermined threshold (OTV), the method may further include additional steps iv.1) and / or iv.2): iv.1) Obtain another image according to step i); or iv.2) Abort the method.

[0141] Specifically, in this article, step iii.1) may be performed once or more before step iii.2).

[0142] Figure 7 shows a flowchart of an exemplary embodiment of an analytical measurement method (specifically a computer-implemented method and / or an in vitro method) for detecting at least one analyte in a sample of bodily fluids according to the invention, for example, by utilizing the kit 148 shown in Figure 5.

[0143] The analytical measurement method for detecting at least one analyte in a sample of bodily fluids is performed using a moving device 112 having at least one camera 114 and specifically at least one processor 149. Herein, at least one analyte is detected from at least one reagent testing area 120 of an optical testing element 118. The at least one reagent testing area 120 is configured to perform at least one optical detection reaction in the presence of the analyte.

[0144] The analytical measurement method includes, in the first step (i) (depicted by reference numeral 500), a method for determining information items regarding the quality of the image 124 received in step (i).

[0145] The analytical measurement method further includes, in step II) (depicted by reference numeral 600), determining in step iv) whether information items regarding the quality of the image captured in step (i) indicate that the quality of the image received in step i) is sufficient for use in an analytical measurement method for detecting at least one analyte in bodily fluids.

[0146] If, as a result of the determination, the information item regarding the quality of the image captured in step (i) indicates that the quality of the image received in step (i) is insufficient for use in the analytical measurement method, then, for example, another image can be received by repeating step (i), or the method can be aborted. Specifically, if another image should be received, a prompt message indicating this can be displayed, for example, on the display of the mobile device 112. Otherwise, if the method is aborted, a message can be displayed, for example, on the display of the mobile device 112 notifying of the poor quality of the image received in step (i).

[0147] Alternatively, if, as determined in step iv), the information item regarding the quality of the image captured in step (i) indicates that the quality of the image received in step i) is sufficient for use in the analytical measurement method, then in step II), at least one analyte is detected. For this purpose, in step II), the image received in step (i) is evaluated, and the analyte is detected from at least one optical detection reaction at at least one reagent test area 120 of the optical test element 118. Specifically, detecting at least one analyte in body fluids may include determining the concentration of the analyte in the body fluids.

[0148] Furthermore, the present invention includes the following embodiments: Example 1: A method for determining information items related to image quality, specifically a computer-implemented method. -The information item regarding image quality relates to the suitability of the image for use in an analytical measurement method for detecting at least one analyte in bodily fluids, wherein the at least one analyte is detected from at least one reagent test area of ​​an optical test element, and wherein the at least one reagent test area is configured to perform at least one optical detection reaction in the presence of the analyte; This determination is made using a mobile device having at least one camera, and the method includes: (i) Receive at least one image captured by the camera, wherein the image includes at least a portion of the reagent test area of ​​the optical test element to which the body fluid has been applied; (ii) From the image received in step (i), independently derive 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, where x is a value from 1 to n, and where n is the total number of image quality parameters contained in the predetermined set of image quality parameters, wherein the at least two image quality parameters in the image quality parameters QPx are independently configured to evaluate at least one of the following: the color characteristics of the image and the spatial characteristics of the image; (iii) Each of the image quality values ​​QVx obtained in step (ii) is independently compared with one or more predetermined thresholds TV(QPx); and based on the comparison, an individual numerical quality rating value IAV(QPx) is independently assigned to each of the at least two different image quality parameters QPx in step (ii), wherein the individual numerical quality rating value is selected from a predetermined set of individual numerical quality rating values; and (iv) Obtain an overall image quality rating (OAV) from the individual numerical quality rating (IAV) (QPx) assigned in step (iii); compare the overall image quality rating (OAV) with at least one overall predetermined threshold (OTV); and based on the comparison, determine the information item regarding the quality of the image received in step (i).

[0149] Example 2: The computer-implemented method according to Example 1, wherein the information item regarding the quality of the image received in step (i) includes Boolean information, specifically indicating the following Boolean information: the quality of the image is sufficient for use in the analytical measurement method for detecting the at least one analyte in the body fluid, or the quality of the image is insufficient for use in the analytical measurement method.

[0150] Example 3: The computer-implemented method according to Example 1 or 2, wherein the at least two different image quality parameters in the image quality parameter QPx are configured independently to evaluate at least one of the following: illuminance non-uniformity, maximum illuminance, noise, and sharpness.

[0151] Example 4: A computer-implemented method according to any one of the preceding examples, wherein the set of individual numerical quality assessment values of step (iii) includes at least a first individual numerical quality assessment value IAV1(QPx) and a second individual numerical quality assessment value IAV2(QPx) independently of each other for at least two of the image quality parameters QPx, where IAV1(QPx) < IAV2(QPx), and wherein the first IAV1(QPx) corresponds to a predetermined class of insufficient quality related to the image quality parameter QPx; wherein the set of individual numerical quality assessment values may further include one or more additional individual numerical quality assessment values, such as a third individual numerical quality assessment value IAV3(QPx), independently of each other for the at least two image quality parameters QPx, where IAV1(QPx) < IAV2(QPx) < IAV3(QPx); wherein the better the image quality related to the image quality parameter QPx is evaluated, the higher the actual values of specific individual numerical quality assessment values such as IAV1(QPx), IAV2(QPx), and IAV3(QPx).

[0152] Example 5: A computer-implemented method according to any one of the preceding examples, further comprising if at least one of the individual numerical quality assessment values IAV(QPx) of the image quality parameter QPx assigned to step (ii) in step (iii) corresponds to a predetermined class of insufficient quality related to the affected image quality parameter QPx, then iii.1) receiving another image according to step i); or iii.2) aborting the method.

[0153] Example 6: A computer-implemented method according to any one of the preceding examples, further comprising if the overall image quality assessment value OAV obtained in step (iv) corresponds to a predetermined class of insufficient quality related to the at least one overall predetermined threshold OTV, then iv.1) receiving another image according to step i); or iv.2) aborting the method.

[0154] Example 7: A computer-implemented method according to any one of the preceding two examples, wherein steps iii.1), iii.2), iv.1) and / or iv.2) are performed only if at least one criterion, specifically two criteria, among the following criteria are met: - A predetermined period of time has not elapsed since the at least one image was first obtained in step i); - The predetermined maximum number of attempts to implement this computer-based method has not yet been exceeded.

[0155] Example 8: A computer-implemented method according to any of the foregoing embodiments, wherein step iv is performed only if any of the individual numerical quality rating values ​​IAV(QPx) assigned to the image quality parameter QPx in step (iii) in step (ii) does not correspond to a predetermined category of insufficient quality associated with any of the image quality parameters QPx.

[0156] Example 9: A computer-implemented method according to any of the preceding embodiments, wherein in step (iv), the overall image quality rating value OAV is obtained by combining two or more of the individual numerical quality rating values ​​IAV(QPx) assigned in step (iii) by applying a mathematical function, specifically all of them.

[0157] Example 10: A computer-implemented analytical measurement method for detecting at least one analyte in a sample of bodily fluid using a mobile device having at least one camera, wherein the at least one analyte is detected from at least one reagent testing area of ​​an optical testing element, and wherein the at least one reagent testing area is configured to perform at least one optical detection reaction in the presence of the analyte, the method comprising: I) Performing a computer-implemented method according to any one of Embodiments 1 to 9 for determining information items regarding the quality of the image received in step (i); II) If the information item regarding 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 in the analytical measurement method for detecting the at least one analyte in the body fluid, then the at least one analyte is detected from the at least one optical detection reaction at the at least one reagent test area of ​​the optical test element by evaluating the image captured in step (i).

[0158] Example 11: A computer-implemented analytical measurement method according to any of the foregoing embodiments relating to analytical measurement methods, wherein detecting the at least one analyte in the body fluid comprises determining the concentration of the analyte in the body fluid.

[0159] Example 12: A computer-implemented analytical measurement method according to any of the foregoing embodiments relating to analytical measurement methods, wherein the optical testing element is selected from the group consisting of: test strips, test measurements (specifically lateral flow measurements), test bars (specifically test dip bars), test boxes, test strips, test paper, and test chips.

[0160] Example 13: A computer-implemented analytical measurement method according to any of the foregoing embodiments relating to analytical measurement methods, wherein the method further includes providing the optical test element.

[0161] Example 14: A mobile device having at least one camera and at least one processor, the mobile device being configured for... - Determine information items regarding the quality of the image; wherein the moving device is further configured to perform at least steps i) to iv) of the computer-implemented method according to any one of embodiments 1 to 9; or - Detecting the concentration of an analyte in body fluids, wherein the at least one analyte is detected by an optical detection reaction at a reagent testing area; wherein the mobile device is further configured to perform at least steps I) to II) of the computer-implemented analytical measurement method according to any one of Examples 10 to 13.

[0162] Example 15: A kit comprising a mobile device according to Example 14 and at least one optical testing element having at least one reagent testing area, wherein the at least one reagent testing area is configured to perform at least one optical detection reaction in the presence of the analyte.

[0163] Example 16: A computer program comprising instructions that, when executed by a mobile device according to Example 14, cause the mobile device to perform... -At least steps i) to iv) of the computer-implemented method according to any one of Embodiments 1 to 9; or -At least steps I) to II) of the computer-implemented analytical measurement method according to any one of Examples 10 to 13.

[0164] Example 17: A computer-readable storage medium comprising instructions that, when executed by a mobile device according to Example 14, cause the mobile device to perform... -At least steps i) to iv) of the computer-implemented method according to any one of Embodiments 1 to 9; or -At least steps I) to II) of the computer-implemented analytical measurement method according to any one of Examples 10 to 13.

[0165] List of reference numerals 112 Mobile devices 114 cameras 118 Optical Testing Elements 120 Reagent Testing Area 121 Reference Area 124 images 148 kits 149 processor 100 Step i) of the method for determining information items regarding the quality of the image received in step (i) 200 Step ii) of the method for determining information items regarding the quality of the image received in step (i) 300 Step iii) of the method for determining information items regarding the quality of the image received in step (i). 400 Step iv) of the method for determining information items regarding the quality of the image received in step (i). 500 Steps in Analytical Measurement Methods (I) 600 Analytical Measurement Methods (Steps II)

Claims

1. A computer-implemented method for determining information items about the quality of an image using a moving device having at least one camera. -The information item regarding the quality of the image relates to the suitability of the image for use in an analytical measurement method for detecting at least one analyte in bodily fluids, wherein the at least one analyte is detected from at least one reagent test area of ​​an optical test element, and wherein the at least one reagent test area is configured to perform at least one optical detection reaction in the presence of the analyte. The method includes: (i) Receive at least one image captured by the camera, wherein the image includes at least a portion of the reagent test area of ​​the optical test element to which the body fluid has been applied; (ii) From the image received in step (i), independently determine 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, where x is a value from 1 to n, and where n is the total number of image quality parameters contained in the predetermined set of image quality parameters, wherein the at least two different image quality parameters in the image quality parameters QPx are independently configured to evaluate at least one of the following: color characteristics of the image and spatial characteristics of the image; (iii) Each of the image quality values ​​QVx determined in step (ii) is independently compared with one or more predetermined thresholds TV (QPx); and based on the comparison, an individual numerical quality rating value IAV (QPx) is independently assigned to each of the at least two different image quality parameters QPx in step (ii), wherein the individual numerical quality rating value is selected from a predetermined set of individual numerical quality rating values; as well as (iv) Derive the overall image quality rating (OAV) from the individual numerical quality rating (IAV) (QPx) assigned in step (iii); compare the overall image quality rating (OAV) with at least one overall predetermined threshold (OTV); and based on the comparison, determine the information item regarding the quality of the image received in step (i). Step iv is performed only if any of the individual numerical quality rating values ​​IAV(QPx) assigned to the image quality parameter QPx in step (iii) in step (ii) does not correspond to a predetermined category of insufficient quality associated with any of the image quality parameters QPx.

2. The computer-implemented method of claim 1, wherein the information item regarding the quality of the image received in step (i) includes Boolean information, specifically indicating that the quality of the image is sufficient for use in the analytical measurement method for detecting the at least one analyte in the body fluid, or that the quality of the image is insufficient for use in the analytical measurement method.

3. The computer-implemented method according to claim 1 or 2, wherein the at least two different image quality parameters in the image quality parameter QPx are configured independently to evaluate at least one of the following: illuminance non-uniformity, maximum illuminance, noise, and sharpness.

4. The computer-implemented method according to any one of the preceding claims, wherein the set of individual numerical quality ratings in step (iii) independently comprises, for at least two of the image quality parameters QPx, at least a first individual numerical quality rating IAV1(QPx) and a second individual numerical quality rating IAV2(QPx), wherein IAV1(QPx) < IAV2(QPx), and wherein the first IAV1(QPx) corresponds to a predetermined category of insufficient quality associated with the image quality parameter QPx; wherein the set of individual numerical quality ratings may further include, independently of each of the at least two image quality parameters QPx, one or more additional individual numerical quality ratings, such as a third individual numerical quality rating IAV3(QPx), wherein IAV1(QPx) < IAV2(QPx) < IAV3(QPx); wherein the better the image quality associated with the image quality parameter QPx is evaluated, the better the specific individual numerical quality ratings such as IAV1(QPx), IAV2(QPx) are evaluated. The higher the actual value of IAV3(QPx), the better.

5. The computer-implemented method according to any one of the preceding claims, further comprising: if at least one of the individual numerical quality rating values ​​IAV(QPx) assigned to the image quality parameter QPx in step (iii) in step (ii) corresponds to a predetermined category of insufficient quality related to the affected image quality parameter QPx, then iii.1) Receive another image according to step i); or iii.2) Abort the method.

6. The computer-implemented method according to any one of the preceding claims, further comprising: if the overall image quality rating (OAV) obtained in step iv) corresponds to a predetermined category of insufficient quality related to the at least one overall predetermined threshold (OTV), then iv.1) Receive another image according to step i); or iv.2) Abort the method.

7. The computer-implemented method according to any one of the preceding two 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 are satisfied: a) From the moment the at least one image was first acquired in step i), a predetermined time period has not yet elapsed; b) The predetermined 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), obtaining the overall image quality rating OAV comprises combining two or more, specifically all, of the individual numerical quality ratings IAV(QPx) assigned in step (iii) by applying a mathematical function.

9. A computer-implemented analytical measurement method for detecting at least one analyte in a sample of bodily fluid, wherein the detection may include determining the concentration of the analyte in the bodily fluid using a moving device having at least one camera, wherein the at least one analyte is detected from at least one reagent testing area of ​​an optical testing element, and wherein the at least one reagent testing area is configured to perform at least one optical detection reaction in the presence of the analyte, the method comprising: I) A computer-implemented method according to any one of claims 1 to 8 for determining information items regarding the quality of the image received in step (i); II) If the information item regarding 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 in the analytical measurement method for detecting the at least one analyte in the body fluid, then the at least one analyte is detected from the at least one optical detection reaction at the at least one reagent test area of ​​the optical test element by evaluating the image captured in step (i).

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 use - Determine information items regarding the quality of the image, wherein the moving device is further configured to perform at least steps i) to iv) of the computer-implemented method according to any one of claims 1 to 8; or - Detecting the concentration of an analyte in body fluids, wherein the at least one analyte is detected by an optical detection reaction at a reagent testing area; wherein the mobile device is further configured to perform 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 a mobile device according to claim 11 and at least one optical testing element having at least one reagent testing area, wherein the at least one reagent testing area is configured to perform at least one optical detection reaction in the presence of the analyte.

13. A computer program comprising instructions that, when executed by a mobile device according to claim 11, cause the mobile device to perform... - 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 that, when executed by a mobile device according to claim 11, cause the mobile device to perform... - 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.

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