Patent Publication Number: US-2022238224-A1

Title: Method for providing semi-quantitative test results for drug test strips using machine learning

Description:
FIELD OF THE INVENTION 
     The present invention is related to systems and methods for interpreting the concentration of biological materials contained in a drug test strip. Particularly, the present invention is related to system and methods for providing semi-quantitative test results for drug test strips using machine learning. 
     BACKGROUND OF THE INVENTION 
     “Drug testing” is performed to determine if a particular drug is present in a sample or is present in the sample in sufficient concentration. The sample tested may be, for example, urine, blood, or saliva. The form of testing done depends on how much information the tester needs about the concentration level of the drug in the sample. 
     For example, qualitative testing can be used if it is desirable to know whether the concentration of a drug is above a predetermined concentration level (“cut-off concentration”). Qualitative drug testing is typically performed at a collection facility using, for example, various on-site sample collection methods. Typical collection methods include urine cups, drug test strips, oral swabs, drug test sticks configured to collect and saliva stick, or the lock. 
     Since qualitative drug testing focuses on whether the drug is above the cut-off concentration level, the results of qualitative drug testing are reported as either Positive or Negative. As used with respect to qualitative testing, a positive result means that the drug is in the sample in concentration levels higher than the cut-off concentration. A negative result means that the concentration of the drug tested for is below the cut-off concentration. 
     It is well known to use drug testing strips (“pads”) in conjunction with the drug testing collection device when testing for the presence of a specific drug or class of drugs. Drug test strips used in drug testing have been impregnated with biochemical reagents that respond to the presence of drugs from a specific drug class. When there is sufficient concentration of drugs from the class present, the drug test strip will change color to indicate the presence of the drug in the sample. 
     More specifically, it is well known that each test strip contains reagents that change color when exposed to an analyte solution. Each drug testing strip will typically include reagent pads for detecting or measuring analytes present in a biological sample such as urine or saliva. It is also well known that test strips may be used to test for analytes customarily found in the human body. For example, certain test strips that may test for glucose, bilirubin, ketones, specific gravity, blood, pH, protein, urobilirubin, nitrate, leukocytes, microalbumin, creatine, or the like. 
     Typical examples of a conventional method for collecting a sample to be tested includes a drug testing urine cup, or saliva stick (e.g., “diagnostic device”). Conventional diagnostic devices may incorporate one or more drug testing strips in its construction. A diagnostic device may include several test strips for testing for the presence of multiple drugs. The drug panels may appear as optically visible spaced lines. Such diagnostic devices may include a portion having spatially separated optically detectable signal (SSOSD) test strips, where each spatially separated test strip is configured to test for a different distinct analyte. 
     A SSOSD test strip may be configured to report the drug test results as optically detectable signals (e.g. immunoassay lines) indicating the presence, absence or relative levels of the various analytes. During use, a diagnostic device including a drug test panel (e.g., multiple drug test strips) may be placed in contact with a sample, such as a biological sample, to determine the presence of a drug in the sample. The drug test strips are comprised of absorbent material. A portion of the sample to be tested is partially absorbed by the drug test strips, typically through capillary action. If the analytes for which one of the test strip is design to detect are present, the test strip will react with drug-specific antibodies and give an optical indication of the drug&#39;s presence. In a typical example, the optical indication is visible as a change in color of the test strip. 
     In qualitative drug testing, the diagnostic device may include multiple test strips (i.e., “T line(s)”) configured to changed colors to indicate the presence of a drug for which the test strip is testing. 
     For example, in a “positive” test result, the T Line disappears only if the concentration of analytes is above the cut-off concentration level. Contrarily, if the T-line shows any visible color, then the result of the drug test is Negative. A negative result means that the drug concentrations in the sample are below the cut-off concentration levels for a particular drug tested being tested. 
     In most cases, results received from the diagnostic device must be confirmed. The diagnostic device test panel may include a control line (“C” line”) for use in confirming if the drug test strip is giving an accurate result. The C line may be used as a quality control technique to ensure, for example, that the test strip has not expired, or that the proper testing procedure has been followed. 
     The C line may be represented by a visible line on the drug strip. If the control line disappears when placed in contact with the sample, then the drug test strip results are deemed unreliable. If the result of the drug test is that the “C” line is visible and the “T” line is also visible then the drug test&#39;s negative is validated. However, if the “C” line is visible and the T line disappears, then the test&#39;s Positive result is validated. On the other hand, if the C Line disappears, then the test results are invalid no matter whether the T Line is visible or not. 
     In some case, such as for example where the tester is required to determine the concentration of drugs in a sample, the tester may prefer semi-quantitative testing over qualitative testing. Semi-quantitative drug testing is typically performed on a biological sample using an auto-analyzer such as those typically found in the clinic, hospital and toxicology laboratories. Semi-quantitative testing is ordinarily not done on the testing site. Instead, the sample is delivered to the testing facility for processing. 
     As with qualitative drug testing, semi-quantitative drug testing uses biochemical reagents that respond to the presence of drugs from a specific class. However, while results of the qualitative test depend on the concentration of the analytes relative to the cut-off level for the specific drug, the results of a semi-quantitative drug test are reported as numerical results representative of the concentration levels of the analyte in the sample being tested. In some instances, the numerical result from the semi-quantitative test represents the summed concentrations from all drugs in the class that contribute to the response. For instance, the numerical result generated by the semi-quantitative test for opiates customarily includes the summed contributions from morphine, codeine, hydrocodone, hydromorphone, and to a lesser extent, oxycodone and oxymorphone. 
     Like as what was described with respect to qualitative testing, the test strips change to indicate the presence of a particular drug. The magnitude of the color change is proportional to analyte concentration in the sample being tested. For example, the higher the concentration of the analyte in the sample, the more drastic the change in color. That is, in a typical example, the more concentration of the analyte in the sample, the more luminous the color of the test strip. 
     The advantage of conventional qualitative testing over semi-quantitative testing is that qualitative test results may be interpreted in real-time at the testing site. On the other hand, the semi-quantitative testing gives more information about the sample, such as, the concentration of the analyte. Unfortunately, the downside to semi-quantitative testing is that semi-quantitative tests must be read in a lab, hospital or other facility typically removed from the collection site. As such, semi-quantitative test results are not read in real-time. 
     What is needed is a system and method to interpret semi-quantitative test at the collection site in real-time. 
     SUMMARY OF THE INVENTION 
     The present invention teaches improvements not found in the prior art. The invention teaches a computer-enabled system and method for providing semi-quantitative test results for drug test strips using machine learning. The invention teaches a computer-enabled system and method for providing semi-quantitative test results in real-time, on the collection site. 
     In one exemplary embodiment of the invention, a computer-enabled application for determining the concentration of analytes in a biological sample is configured to be managed by a hand-held computing device. In a preferred embodiment, the computing device is a photonic enabled device. An example of a photonic enabled computing device would be a camera enable mobile phone, iphone or tablet. In such case, the camera will be enabled to take a picture of a drug strip color indication. 
     In another embodiment of the invention, the computer application is configured to receive the color indication data (e.g., digital image) from the photonic enabled device and translate the color indication data into a semi-quantitative result. In one aspect, results of the computer application may include a numerical result that represents the concentration level of drugs in the biological sample. 
     In yet another embodiment of the invention, the present invention teaches a drug screening system which includes a stand for holding the photonic enabled device. The diagnostic device which has been placed in contact with a biological sample to be tested, may be fitted in the stand. In a particular embodiment, the stand may be configured to position the photonic device&#39;s camera, facing the diagnostic device. In this way, the stand ensures that the distance of the diagnostic device from the photonic device may be consistent from one test to another. 
     In one exemplary embodiment, a machine learning model of reference diagnostic devices is used to determine which testing control variables are used to evaluate a drug test strip. In one aspect, a database of multiple machine learning models is generated using a plurality of digital images of reference diagnostic devices. One or more of the diagnostic devices may be “related.” Related diagnostic devices as used herein are those diagnostic devices that require the same control variables to interpret them. In one aspect, groups of related diagnostic devices serve as a machine learning data set to generate the machine learning models for each group of related diagnostic devices. 
     In another exemplary embodiment, the present invention the machine learning model of related diagnostic devices includes generating a machine learning model of the drug strip panels corresponding to each of the machine models of reference diagnostic devices. In one aspect, the drug strip panels corresponding to the diagnostic devices used as machine learning data sets are used as the machine learning data set for generating the drug strip panel machine learning models. 
     In another aspect, the machine learning model of drug strip panels is segregated into a grid of images, wherein at least one of the modules of the grid of images includes control variables for use in evaluating an active diagnostic device. 
     In still another exemplary embodiment of the invention, a digital image of an active diagnostic device is captured by a photonic enabled device. An active diagnostic device is one that has been placed in contact with a biological fluid to be tested. In one aspect, the digital image of the active diagnostic device is used to select at least one of the machine learning model of diagnostic devices. The machine model of the drug panel corresponding to the selected machine learning model of diagnostic devices is used to select the machine learning model of drug strip panel to be used to evaluate the active diagnostic device. 
     In still another exemplary embodiment of the invention, a diagnostic device identifier for the active diagnostic device is used to select the machine learning model of diagnostic devices to be evaluated. 
     The present invention is designed for use interpreting single or multiple drug test strips. Typical bodily fluids tested by the invention include those used to test urine or saliva, including drug test strips designed for use with urine cups, urine dip cards, saliva box, saliva tube, saliva stick. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is an exemplary depiction of a diagnostic device that may be used with the present invention; 
         FIG. 2  is an exemplary depiction of a mobile device (e.g., smart phone) that may be used with the present invention; 
         FIG. 3  is an exemplary is an exemplary diagnostic table-top stand that may be used with the present invention; 
         FIG. 3  is another exemplary computing environment in which the present invention may be used; 
         FIG. 4  is a example of a system for using the diagnostic method according to various embodiments of the present invention; 
         FIG. 5  is another typical example of a method for evaluating a diagnostic device that is practiced according to the invention; 
         FIG. 6  is an exemplary method for generating a database of machine learning models which use a plurality of reference diagnostic devices as the machine learning data set; 
         FIG. 7  is an exemplary method for generating a database of machine learning models which use a plurality of reference SSODS test strips as the machine learning data set, and generating test control variables; and 
         FIG. 8  is an exemplary method for evaluating a diagnostic device according to the present invention. 
     
    
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS OF THE PRESENT INVENTION 
     This disclosure is set forth in the context of representative embodiments that are not to be otherwise limiting in any way. 
     The things and methods described herein should not be construed as being limiting in any way. Instead, this disclosure is directed toward all novel and non-obvious features and aspects of the various disclosed embodiments, alone and in various combinations and sub-combinations with one another. The disclosed systems, methods, and apparatus are not limited to any specific aspect or feature or combinations thereof, nor do the disclosed things and methods require that any one or more specific advantages be present or problems be solved. 
     Although the operations of some of the disclosed methods are described in a particular, sequential order for convenient presentation, it should be understood that this manner of description encompasses rearrangement, unless a particular ordering is required by specific language set forth below. For example, operations described sequentially may in some cases be rearranged, omitted, or performed concurrently. Moreover, for the sake of simplicity, the attached figures may not show the various ways in which the disclosed things and methods can be used in conjunction with other things and methods. 
     Additionally, the description sometimes uses terms like “produce,” “generate,” “select,” “capture,” and “provide” to describe the disclosed methods. These terms are high-level abstractions of the actual operations that are performed. The actual operations that correspond to these terms can vary depending on the particular implementation and are readily discernible by one of ordinary skill in the art. 
     As used in this application and in the claims, the singular forms “a,” “an,” and “the” include the plural forms unless the context clearly dictates otherwise. Additionally, the term “includes” means “comprises.” Further, the term “coupled” encompasses mechanical, electrical, magnetic, optical, and physical, as well as other practical ways of coupling or linking items together, and does not exclude the presence of intermediate elements between the coupled items. 
     Any of the disclosed methods can be implemented as computer-executable instructions stored on one or more computer-readable storage media (e.g., non-transitory computer-readable media, such as one or more volatile memory components (such as DRAM or SRAM), or nonvolatile memory components (such as hard drives)) and executed on a computer (e.g., any commercially available computer, including smartphones or other mobile devices that include computing hardware). Any of the computer-executable instructions for implementing the disclosed techniques as well as any data created and used during implementation of the disclosed embodiments can be stored on one or more computer-readable media (e.g., non-transitory computer-readable media). 
     Consequently, when the present invention describes various data, such as codes, identifiers, or the like, it is understood that the data is stored in the computer-readable storage media and the data may be manipulated, retrieved, or otherwise operated on by the processors described herein. 
     Where the invention discusses a method step or process, it is to be understood that the method step or process may be implemented with computer-executable instructions. The computer-executable instructions can be part of, for example, a dedicated software application or a software application that is accessed or downloaded via a web browser or other software application (such as a remote computing application). Such software can be executed, for example, on a single local computer (e.g., any suitable commercially available computer) or in a network environment (e.g., via the Internet, a wide-area network, a local-area network, a client-server network (such as a cloud computing network), or other such network) using one or more network computers. 
     For clarity, only certain selected aspects of the software-based implementations are described. Other details that are well known in the art are omitted. For example, it should be understood that the disclosed technology is not limited to any specific computer language or program. For instance, the disclosed technology can be implemented by software written in C++, Java, Perl, JavaScript, HTML5, or any other suitable programming language. Likewise, the disclosed technology is not limited to any particular computer or type of hardware. Certain details of suitable computers and hardware are well known and need not be set forth in detail in this disclosure. 
     Furthermore, any of the software-based embodiments (comprising, for example, computer-executable instructions for causing a computer to perform any of the disclosed methods) can be uploaded, downloaded, or remotely accessed through a suitable communication means. Such suitable communication means include, for example, the Internet, the World Wide Web, an intranet, software applications, cable (including fiber optic cable), magnetic communications, electromagnetic communications (including RF, microwave, and infrared communications), electronic communications, or other such communication means. 
     Theories of operation, scientific principles or other theoretical descriptions presented herein in reference to the apparatus or methods of this disclosure have been provided for the purposes of better understanding and are not intended to be limiting in scope. The apparatus and methods in the appended claims are not limited to those apparatus and methods that function in the manner described by such theories of operation. 
     In the following description, certain terms may be used such as “up,” “down,” “upper,” “lower,” “horizontal,” “vertical,” “left,” “right,” “over,” “on,” “near,” and the like. These terms are used, where applicable, to provide some clarity of description when dealing with relative relationships. But, these terms are not intended to imply absolute relationships, positions, and/or orientations. 
     As used in this disclosure, “automatically” means that an act can occur after a previous act without the need to receive additional user input before initiating performance of the act. 
     As used in the disclosure, “application” includes programs implemented with computer-executable instructions executable by a mobile device, as well as programs where some or all of the application functionality is accessed over a network, for example, network applications or web applications. 
     The invention makes reference to machine learning models and machine learning data sets. What is meant by a machine learning model is one of a learning-based decision model which uses a machine learning algorithm, such as AdaBoost, which evaluates inputs based on a model trained on past inputs to predict the user response. Thus, when using a machine learning based model as disclosed herein, the digital image, such as digital images of the reference diagnostic devices, is fed back to the decision model to update the learning. The behavior measurement can be additionally uploaded to a server for aggregate learning of the user behavior. 
     More sophisticated super-resolution algorithms may be used in this invention typically leverage machine learning techniques. Among them are sparse-representation, Kernel Ridge Regression (KRR), anchored neighbor regression (ANR), and in-place example regression. 
     In addition, it is well known that dedicated processing resources, such as graphical processing units, may be provided as various implementation forms, wherein the graphics processing units are a typical example of dedicated processing resources. In the present disclosure, to illustrate specific examples in detail, graphics processing units are taken as an example for describing the technical solution. However, such illustration is merely exemplary, and the technical solution of the present disclosure may be applicable to any dedicated processing resources rather than being limited to graphics processing units. 
     The processors describe in this invention may encompass a machine learning engine, and the databases described herein may have instructions that direct and/or cause the dynamic training response output generated machine learning model. The instructions included in one or more databases include herein provides the instructions to enable dynamic training response output generation control and to set, define, and/or iteratively refine optimization rules and/or other parameters used by the dynamic training response output generation control platform. 
       FIG. 1  is an exemplary depiction of a diagnostic device  100  that may be used with this invention. While diagnostic device  100  is depicted as a drug dipstick, the present invention contemplates using a urine cup, saliva stick, or any similar diagnostic device for use in analyzing the analytes in a biological sample. Diagnostic device  100  may include a housing  102  for including a drug panel  104 , therewith. In one exemplary embodiment, drug panel  104  is included on the face of housing  102 . In another exemplary embodiment, drug panel  104  may contained within housing. 
     Additionally, diagnostic device  100  may include a diagnostic device identifier  108 . Diagnostic device identifier  108  may be useful for identifying the manufacture, manufacturer part number, stockkeeping unit, bar code, QR code or the like. Further still, while the present invention describes the diagnostic device identifier  108  as being encrypted, the diagnostic device identifier  108  may be unencrypted when including with housing  102 . 
     Drug panel  104  is configured to come in contact with a biological sample, such as, saliva, urine, blood, or the like when used. Drug panel  104  include one or more test strips  106 . Test strips  106  of the conventional type ordinarily used to collect biological samples. Test strips  106  may include both T-Lines, for use in testing the presence of a drug, and C-Lines for use in verifying the validity of the test results. 
       FIG. 2  illustrates a generalized example of a suitable computing environment, mobile device  200 , in which described embodiments, techniques, and technologies may be implemented. For example, the mobile device  300  is configured to implement the functionality for generating and transmitting context data, application metadata, and network addresses and computer-executable instructions for applications, as described herein. The mobile device  200  is not intended to suggest any limitation as to scope of use or functionality of the technology, as the technology may be implemented in diverse general-purpose or special-purpose computing environments. The disclosed technology may be implemented with other any hand-held devices, which includes multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, and the like. Mobile device  200  may be, for example, a smart phone, iPad, smart pad, or the like. 
     With reference to  FIG. 2 , hand-held mobile device  200  includes at least one central processing unit  210  and memory  220 . In  FIG. 2 , this most basic configuration of mobile device  200  is included within a dashed line. The central processing unit  210  executes computer-executable instructions and may be a real or a virtual processor. In a multi-processing system, multiple processing units execute computer-executable instructions to increase processing power and as such, multiple processors can be running simultaneously. The memory  220  may be volatile memory (e.g., registers, cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flash memory, etc.), or some combination of the two. The memory  220  stores software  280  that can, for example, implement the technologies described herein. 
     Mobile device  200  may have additional features. For example, mobile device  300  includes storage  240 . Mobile device  200  may further in, one or more output devices  260 , such as, a computer screen or output ports. Mobile device  200  may further include a machine learning engine  290 , for performing a machine learning algorithm. Mobile device  200  may further include a digital camera  270  for capturing a digital image of a subject. An interconnection mechanism (not shown) such as a bus, a controller, or a network, interconnects the components of the mobile device  200 . Typically, operating system software (not shown) provides an operating environment for other software executing in the mobile device  200 , and coordinates activities of the components of the mobile device  200 . 
     The storage  240  may be a removable or non-removable memory, and may include flash memory, CD-ROMs, CD-RWs, DVDs, or any other non-transitory storage medium which can be used to store information and that can be accessed within the computing environment  200 . The storage  240  stores instructions for the software  280 , which can implement technologies described herein. 
     Mobile device  200  may include input device(s)  250  may be a touch input device, such as a keyboard, keypad, mouse, pen, or trackball, a voice input device, a scanning device, proximity sensor, image-capture device, or another device, that provides input to the computing environment  300 . For audio, the input device(s)  250  may be a sound card or similar device that accepts audio input in analog or digital form, or a CD-ROM reader that provides audio samples to the mobile device  200 . The output device(s)  260  may be a display, printer, speaker, CD-writer, or another device that provides output from the mobile device  200 . 
     Computer-readable media are any available media that can be accessed within a mobile device  200 . By way of example, and not limitation, with the mobile device  300 , computer-readable media include memory  220  and/or storage  240 . As should be readily understood, the term computer-readable storage media includes non-transitory storage media for data storage such as memory  220  and storage  240 , and not transmission media such as modulated data signals. 
       FIG. 3  shows an exemplary drug testing stand  300  that may be used with the present invention. Stand  300  may be constructed of a rigid housing  302 . As shown, stand  300  may include a front tray  306  for receiving mobile device  200 , therein. (Shown in  FIG. 4 , below) Stand front tray  306  is constructed to receive mobile device  200  and hold mobile device  200  immobile therein. In one embodiment, tray  306  may hold mobile device  200  removably fixed thereon. 
     Housing  302  may further include a stand compartment  304  for receiving a diagnostic device  100 . (Shown in  FIG. 4 ). Compartment  304  may be configured to hold diagnostic device  100  immovably. Compartment  304  may removably hold diagnostic device  100 , therein. A suitable stand  300  for use with this invention is described in U.S. Provisional Application No. 62/948,260, which is incorporated herein by reference. 
       FIG. 4  is an exemplary embodiment of the present invention showing the diagnostic device  100 , and the mobile device  200  removably affixed to stand  300 . As is shown, diagnostic device  100  is removably affixed to compartment  304 , and mobile device  200  is removably affixed to front tray  306 . In preferred embodiment, stand  300  is configured hold mobile device  200  at a fixed distance, d, from diagnostic device  100 . In one embodiment, stand  300  is configured hold mobile device  200  at a fixed distance, d, from the focal point of digital camera  270 . 
       FIG. 5  depicts an exemplary method  500  for determining the concentration of analytes in a biological sample using a machine learning algorithm. To prepare the diagnostic device  100  for processing, diagnostic device  100  is placed in contact with a biological sample as is done in conventional drug testing. For each is understanding, a diagnostic device  100  that has been placed in contact with a biological sample in preparation for reading is called an “active diagnostic device.” 
     According to the method shown, an active diagnostic device  100  is placed on stand  200  (Step  502 ) in compartment  304 . Additionally, mobile device  200  may be placed on a stand  300  (Step  504 ) in tray  306 . The user may take a digital image of the drug test strip  100  using the mobile device  200  (Step  506 ). The drug test strip  100  may be placed in the camera optical focal point  270 . That is, drug test strip  100  may be located on stand  300  such that the camera may 
     With brief reference to  FIG. 6 , a method for generating a database of machine learning models, is prepared using a machine learning algorithm which uses a plurality of digital images of diagnostic devices as the data set for the algorithm. The plurality of digital images of diagnostic devices that are provided to the machine learning algorithm may pre-recorded images of diagnostic devices showing the diagnostic device identifier  108 , and drug panel  104 . As used herein, the diagnostic devices shown in the pre-recorded digital images are called “reference diagnostic devices.” Consequently, the output of the machine learning algorithm is called a “machine learning model of diagnostic devices,” herein. 
     In a preferred embodiment, a plurality of digital images of the referenced diagnostic devices are separated into a group of related diagnostic devices. (Step  604 ) In one particular embodiment, related diagnostic devices may be diagnostic devices that share a manufacture, stock keeping unit number, QR code, or bar code. In this way, the groups that are formed are groups of related reference diagnostic device. 
     In accordance with this invention, the machine learning model database is constructed by providing the machine learning algorithm with a plurality of digital images of diagnostic devices of that are related. More particularly, the machine learning algorithm is provided groups of related diagnostic devices. As such, the resulting machine learning models generated by the machine learning algorithm represents a distinct machine learning model for each unique group of related diagnostic devices. (Step  606 ) Each distinct machine learning model of diagnostic devices is provided with a distinct group identifier for use in recalling the machine learning model from memory  220 . (Step  608 ) According to various embodiments of the invention, the machine learning algorithm may be stored in memory  220  and accessed by processor  210 . 
     With return reference to  FIG. 5 , as noted, diagnostic devices  100  may include a unique diagnostic device identifier  108 , whether encrypted or unencrypted. In this case, diagnostic device identifier  108  may be used to identify whether the diagnostic device  100  is related to at least any one other of the diagnostic devices comprising the machine learning models of diagnostic devices. The diagnostic device identifier  106  provides information to the mobile device processor  210  concerning the identity of the diagnostic device. A digital image of the identifier  106  is captured by digital camera optical focal point  270  and provided to processor  220 . Processor  220  may receive the diagnostic device identifier  106  (Sep  508 ) and compare (i.e. “match”) the diagnostic device identifier  106  to a distinct group identifier for at least one of the machine learning models for each group of related diagnostic devices discussed in Step  608  above (Step  510 ). If a match is made, the diagnostic device identifier may be considered valid. By valid what is meant is that the diagnostic device that is identified is one capable of being evaluated according to the method of this invention. 
     More specifically, if a match is made, then the diagnostic device  100  may be evaluated by the methods of the present invention. That is, a diagnostic device  100  is valid if the methods of this invention can determine the concentration of analytes contained in its drug panel. That is, in one embodiment of the invention, mobile device  200  may capture a digital image of the diagnostic device  100  including the diagnostic device identifier  109 , and provide the digital image to the processor  210 . Processor  210  may access memory  220  to retrieve identifying information unique identifier for the machine learning model of the diagnostic device stored therein. (Step  508 ) Processor  210  matches the diagnostic device identifier  109  to at least one identifier for at least one of the stored unique machine learning model of diagnostic devices. (Step  510 ) The matched identifier for the at least one of the stored unique machine learning models of diagnostic devices is used to determine which control variables are to be used to determine the concentration of analytes in the diagnostic device drug panel  104 . (Step  512 ) 
     In accordance with the invention, a data base of machine learning models of drug panels  104  is generated for use in evaluating the concentration of analytes contained in the drug panel of an active diagnostic device. As noted, drug panels  104  include test strips  106 , which absorb the biological fluid to be tested. The method according to this invention evaluates the drug test strips  106  to determine the concentration of analytes present. 
     The present invention compares an image of the drug test strips  106  for an active diagnostic device  100  to a machine learning model of drug panel  104 . As described more fully below, the drug panel  104  may be segregated into grid having a plurality of modules, wherein at least one module includes a drug control variable that can be used to evaluate the concentration of analytes in drug panel  104 . 514  To evaluate the concentration level of analytes absorbed by the drug test strips  106 , a database of machine learning models of drug panels is generated. 
     With reference to  FIG. 7 , pre-recorded digital images of drug panels  104  containing drug test strips  106  is provided to a machine learning algorithm, such as is described above. In a preferred embodiment, the plurality of pre-recorded test strips  104  include multiple test strips  106 , which show differing concentration level of analytes. For example, the test strips  104  may indicate different levels of concentrations by indicating different intensities of color corresponding to the concentration level. Therefore, the data set of test strips  104  used to generate the machine learning models of the drug panels includes drug test strips  104  having different shades and intensities of colors representing the concentration of analytes. 
     Pre-recorded digital images of related diagnostic devices drug panels  104  are grouped together (Step  702 ) and used as data sets for the machine learning algorithm to generate the database of the machine learning models of drug panels. (Step  704 ). In one embodiment, a plurality of pre-recorded digital images of test strips  106  that indicate the same level of concentration to within a predetermined tolerance are grouped together. For example, the image of drug test strips  104  of related diagnostic devices  100 , which indicate the same concentration of analytes (i.e., X %) to within a tolerance of 0.01% may be grouped and used a data set for a machine learning algorithm to produce a machine learning model of a drug test strips indicating a concentration of X %. 
     The ensure increase accuracy of the analysis, the machine learning models of the drug test strips  104 , the machine learning model of drug panels is separated into a grid, wherein each module of the grid includes a control variable used to evaluate the drug panel  104  of an active diagnostic device  100 . (Step  706 ). The control variables may include a portion of machine learning model of a test strip  104  contained in the grid. In another embodiment, the control variables may be stored in a database in memory  220 . In another embodiment, the control variable may be stored in memory  220  and correlated to the modules of the machine learning model of drug panel grids. 
     In another exemplary embodiment, a unique machine learning model of drug panels identifier is assigned to each machine learning model of drug panels. Once the processor determines the desired machine learning model of diagnostic devices that matches the active diagnostic device, then processor  210  selects the corresponding machine model of drug panels to be used to evaluate the concentration of analytes in the active diagnostic device drug panel. (Step  708 ) 
     With return reference to  FIG. 8 , a digital image of the active diagnostic device drug panel  104  (and drug test strips  106 ) must be prepared to be compared to the selected machine learning model of drug panels having the control variables. As such, an image of the active diagnostic device drug panel  104  is captured by digital camera  270  and provided to processor  210 . Processor  210  then separates the image of the active drug panel  104  into a grid of images. (Step  804 ) 
     Processor  210  may select at least one of the machine learning model of drug panels  104  for comparing to the drug panel  104  of the active diagnostic device  100 . Processor  210  may select the desired machine learning model of drug panels  104  by based on the machine learning models of diagnostic devices  100 . (Step  808 ) Processor  210  may then compare corresponding modules of the active diagnostic device drug panel  104  grid to corresponding grid modules of the machine learning model drug panel containing control variable. (Step  810 ) 
     With return reference to  FIG. 5 , processor  210  may map at least one of the modules of the grid of the active diagnostic device drug panels  104  to the drug control variables used to evaluate the concentration of analytes in drug panel  104  on a grid-by-grid basis. (Step  516 ) In accordance with the invention, in one particular embodiment the present invention may be configured to interpret the semi-quantitative result of the test based on the comparison of the images of the machine language generated data. For example, the drug test strip may be configured to indicate the concentration of the drug present by correlating the concentration to the intensity of the color shown on the T Line. Test results for semi-quantitative tests may be reported as trace amount; 1+, 2+, or 3+; or positive at  1 : 160  (titer or dilution). (Step  518 ) The semi-quantitative results would be a range based on the cutoff level which could detect semi-quantitative values-50% below all the way up to 2× time the concentration value with line intensity or pantone color. The results of the semi-quantitative analysis of an active diagnostic device may be reported to output devices  260 . (Step  520 ) 
     The disclosed methods, apparatus, and systems should not be construed as limiting in any way. Instead, the present disclosure is directed toward all novel and nonobvious features and aspects of the various disclosed embodiments, alone and in various combinations and sub-combinations with one another. The disclosed methods, apparatus, and systems are not limited to any specific aspect or feature or combination thereof, nor do the disclosed embodiments require that any one or more specific advantages be present or problems be solved. The techniques and solutions described in this application can be used in various combinations to provide a better user experience with mobile devices, including mobile devices such as smartphones. 
     Any of the methods described herein can be performed via one or more computer-readable media (e.g., storage or other tangible media) comprising (e.g., having or storing) computer-executable instructions for performing (e.g., causing a computing device to perform) such methods. Operation can be fully automatic, semi-automatic, or involve manual intervention. 
     It will be recognized that the various embodiments can be modified in arrangement and detail without departing from such principles. It should be understood that the programs, processes, or methods described herein are not related or limited to any particular type of computing environment, unless indicated otherwise. Various types of general purpose or specialized computing environments may be used with or perform operations in accordance with the teachings described herein. Elements of embodiments shown in software may be implemented in hardware and vice versa. 
     In view of the many possible embodiments to which the principles of the disclosed invention may be applied, it should be recognized that the illustrated embodiments are only preferred examples of the invention and should not be taken as limiting the scope of the invention.