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WO2025173009A1 - Dispositif, système et procédé de test quantitatif colorimétrique à flux latéral basé sur l'ia - Google Patents

Dispositif, système et procédé de test quantitatif colorimétrique à flux latéral basé sur l'ia

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Publication number
WO2025173009A1
WO2025173009A1 PCT/IL2025/050158 IL2025050158W WO2025173009A1 WO 2025173009 A1 WO2025173009 A1 WO 2025173009A1 IL 2025050158 W IL2025050158 W IL 2025050158W WO 2025173009 A1 WO2025173009 A1 WO 2025173009A1
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Prior art keywords
test
lfia
colorimetric
analyte
strip
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PCT/IL2025/050158
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English (en)
Inventor
Rafi BENTAL
Amit ASSA
Adnan Agbaria
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Quadpoint Labs Ltd
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Quadpoint Labs Ltd
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Publication date
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Publication of WO2025173009A1 publication Critical patent/WO2025173009A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/60—Image enhancement or restoration using machine learning, e.g. neural networks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/06—Investigating concentration of particle suspensions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/543—Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals
    • G01N33/54366—Apparatus specially adapted for solid-phase testing
    • G01N33/54386—Analytical elements
    • G01N33/54387—Immunochromatographic test strips
    • G01N33/54388—Immunochromatographic test strips based on lateral flow
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30204—Marker

Definitions

  • This disclosure generally relates to quantitative lateral flow immunoassay (LFIA), specifically to home-based point-of-care (POC) kits and method for LFIA.
  • LFIA quantitative lateral flow immunoassay
  • POC point-of-care
  • Home-based LFIA is qualitative and relies on visual detection, by human eyes, of coloration of a test line (the T line) and a control line (the C line).
  • LFIA readers Today’s quantitative LFIA technologies require dedicated readers (referred to herein as “LFIA readers”) which provide full control over parameters, such as environment illumination level, shadowing, orientation, and camera type.
  • LFIA readers include a mechanical part that can store and shield of the LFIA strip, a broadband light source that illuminates the test line and control line of the assay, a sensor that acquires images of the region of interest (ROI) including the test line, and a processor with image-processing software that calculates the presence or concentration of a target analyte from an image.
  • ROI region of interest
  • Such devices are expensive and usually require special training for operation.
  • LFIA quantitative lateral flow immunoassay
  • POC point-of-care
  • the image is initially normalized for environmental differences (such as light exposure etc.) and then a second Al model is applied, the second model trained on various control test mark intensities measured using an LFIA reader, enabling quantitative analysis of the test mark obtained.
  • This advantageously obviates the need for the expensive LFIA reader device for a quantitative analysis of the sample, which in turn enables a point of care or even home testing.
  • the LFIA strip analyzed preferably and advantageously includes more than one test area, each test area including a different concentration of the agent that produces a colorimetric test mark (T) in response to the presence of an analyte in the biological sample.
  • T colorimetric test mark
  • the hereindisclosed LFIA strip may further include one or more “elapsed time” control areas (ETC), optionally instead of or in addition to the control-area used for confirmation of correct lateral flow of the sample.
  • ETC elapsed time control areas
  • the ETC is configured to change (e.g. in color and/or color intensity) in response to time to thereby indicate the biological time of the test, i.e. time elapsed between the sample reaching the ETC and capturing of the image.
  • the ETC may include multiple ETCs (also referred to as an “ETC matrix”) each ETC including a different concentration of the agent capturing the control substance and thus changing its color and/or color intensity at different concentrations of a control substance i.e., at different time after reaching the ETC area, e.g. at intervals of between from 30 seconds to 5 minutes.
  • ETC matrix also referred to as an “ETC matrix”
  • an Al model may be applied on the image, which model is configured to calibrate the color intensity of the test areas (T) over time.
  • the herein disclosed LFIA strip and associated method enables home detection/monitoring of medical conditions (such as but not limited to colorectal cancer), and thus increase screening compliance (e.g., as compared to the often-feared endoscopic procedure) and thus increase the rate of early versus late-stage detection (which in the case of colorectal cancer may change 10-year survival rate to 90% as compared to 10%, respectively).
  • the herein disclosed LFIA strip and associated method enable to conduct the sample in a home-setting, thus avoiding issues such as privacy, discomfort and shame often involved with handing over samples, in particular stool samples.
  • this LFIA strip and associated method may advantageously pre-screen subjects (e.g. for colorectal cancer), such that only subjects for which a risk has been identified are sent to further testing (such as endoscopic testing), thus significantly reducing screening costs.
  • the LFIA strip further includes a control area configured to produce at least two colorimetric control marks (C) in response to lateral flow of the test sample therethrough, irrespective of presence or absence of an analyte in the test sample, wherein each of the at least two colorimetric control marks (C) has a different response time indicative of the time that has elapsed between loading of the biological sample and image capturing.
  • the method further includes adjusting the calculated analyte concentration, based on time that has elapsed between loading of the biological sample and image capturing.
  • the imaging conditions comprise camera quality, camera configuration/setting, surrounding light condition, shadowing and/or any combination thereof.
  • the method further comprises pre-processing the image to convert all colorimetric marks to a single-color monochromatic scale.
  • the method further comprises applying an object detection algorithm on the image to identify the LFIA strip or parts thereof.
  • the first and second Al models are convoluted neural network (CNN) models.
  • the CNN model is a multi-classification model.
  • the multi-classification model is configured to predict a probability that the colorimetric test mark (T) matches with a color intensity of each of the plurality of control test marks (TC).
  • the LFIA strip comprises at least two test areas, each test area comprising a different concentration of the agent, such that an essentially linear correlation between color intensity and analyte concentration is obtained at different analyte concentration, for each of the at least two test areas.
  • At least a portion of the at least two test areas provide a different color intensity, thereby increasing linear data point range and accuracy.
  • the multi-classification model is further configured to calculate the concentrations of the analyte in the biological sample, based on matching of the color intensities obtained in each of the at least two test areas to a most similar of the plurality of control test marks (TC).
  • a lateral flow immunoassay analysis (LFIA) strip comprising: a membrane configured for lateral flow of a test sample, the membrane comprising: a control area configured to produce a colorimetric control mark (C) in response to the lateral flow of the test sample therethrough irrespective of presence or absence of an analyte in the test sample; a test area comprising an agent configured to produce a colorimetric test mark (T) in response to presence of the analyte in the test sample; a reference area comprising: an isolating layer; and a secondary membrane comprising a colorimetric reference mark (R) having a predefined color intensity, the colorimetric reference mark (R) formed before exposing the LFIA strip to the biological sample, wherein the secondary membrane is shielded off from contact with the test sample by the isolating layer, such that the color intensity of the colorimetric reference mark (R) is independent of lateral flow of the test sample through the LFIA strip and , wherein the
  • the LFIA strip comprises at least two test areas (T), each test area comprising a different concentration of the agent, such that a minimal concentration of the analyte sufficient to produce the colorimetric test mark (T) differs in each area.
  • the LFIA strip comprises at least two colorimetric reference marks (R), each having a different preformed and predefined color intensity.
  • the membrane and the secondary membrane are made from the same material. According to some embodiments, the membrane and the secondary membrane are nitrocellulose membranes.
  • the LFIA strip further comprises a sample pad configured to receive the test sample.
  • the LFIA strip further comprises a conjugation pad configured to release a conjugate that binds the analyte if present.
  • the LFIA strip further comprises an absorption pad configured to wick the prevent backflow of the test sample.
  • the LFIA strip further comprises an adhesive pad configured to sequentially adhere the sample pad, the conjugation pad, the membrane and the absorption pad.
  • control area comprises at least two colorimetric control marks (C), wherein each of the at least two colorimetric control marks (C) has a different response time which is indicative of the time that has elapsed between loading of the biological sample and image capturing.
  • a method for quantitative analysis of lateral flow immunoassay analysis (LFIA) results comprising: a) loading a biological sample on an LFIA strip including: i. at least two test areas, each test area comprising a different concentration of an agent configured to produce a colorimetric test mark (T) in response to the presence of an analyte in the biological sample, such that an essentially linear correlation between color intensity and analyte concentration is obtained; ii. a reference area comprising at least two colorimetric reference marks (R), each reference mark having a different predefined color and color intensity, formed before exposing the LFIA strip to the biological sample; and iii.
  • LFIA lateral flow immunoassay analysis
  • a control area configured to produce a colorimetric control mark (C) in response to the lateral flow of the test sample therethrough irrespective of presence or absence of an analyte in the test sample; b) capturing an image of the LFIA strip; c) applying an object detection algorithm on the image to identify the LFIA strip or parts thereof; d) removing image environmental conditions differences from the captured image or parts thereof that contain the picture of the LFIA strip, by applying a first CNN model on the captured image or parts thereof, the first CNN model trained on a plurality of images of colorimetric reference mark (R) captured under different imaging conditions and normalized to a color intensity of the colorimetric reference mark (R) measured using an LFIA reader, thereby obtaining a normalized image; and e) calculating a concentration of the analyte in the biological sample by applying a second CNN model on the color intensity of the colorimetric test mark (T) in the normalized image, the second Al model trained on a plurality of various control test mark (CT) intens
  • control area includes at least two colorimetric control marks (C) each having a different response time indicative of the time that has elapsed between loading of the biological sample and image capturing.
  • the method further includes adjusting the calculated analyte concentration, based on the time that has elapsed between loading of the biological sample and image capturing. According to some embodiments, if the time elapsed exceeds a predetermined threshold value, the calculated analyte concentration is deemed invalid.
  • Certain embodiments of the present disclosure may include some, all, or none of the above advantages.
  • One or more technical advantages may be readily apparent to those skilled in the art from the figures, descriptions and claims included herein.
  • specific advantages have been enumerated above, various embodiments may include all, some or none of the enumerated advantages.
  • FIG. 1 shows an LFIA strip with embedded reference areas each having a different color intensity, according to some embodiments
  • FIG. 2A illustratively depicts an exemplary LFIA strip including a single test area and a single reference area, according to some embodiments;
  • FIG. 2B illustratively depicts an exemplary LFIA strip including a single test area and a plurality of reference area (here six), according to some embodiments;
  • FIG. 3 is a schematic graph illustrating the color intensity as a function of analyte concentration of a LFIA, according to some embodiments
  • FIG. 4A illustratively depicts an exemplary LFIA strip including a plurality of test areas (here three) and a single reference area, according to some embodiments;
  • FIG. 4B illustratively depicts an exemplary LFIA strip including a plurality of test areas (here three) and a plurality of reference areas (here six), according to some embodiments;
  • FIG. 5 is a schematic graph illustrating the color intensity as a function of concentration for multiple T-areas (here three) with different cut-off points, according to some embodiments;
  • FIG. 6 illustratively depicts an exemplary LFIA strip including a plurality of test areas (here three) for more than one analyte (here 2 analytes) and a plurality of reference areas (here six), according to some embodiments;
  • FIG. 7 is an exemplary flow chart of the herein disclosed method, according to some embodiments.
  • FIG. 8 illustratively depicts an exemplary LFIA strip including a plurality of test areas (here three) and a plurality of reference areas (here six) and a plurality of lapsed-time control areas (here six), according to some embodiments, according to some embodiments; and
  • FIG. 9 illustratively depicts, of an LFIA strip as disclosed herein and associated method, according to some embodiments.
  • a computer implemented method for Al-based quantitative colorimetric lateral flow immunoassay analysis including: receiving an image captured with a handheld digital imaging device, the image capturing an LFIA strip that has been exposed to a biological sample, wherein the LFIA strip includes: a) a test area comprising an agent configured to produce a colorimetric test mark (T) in response to the presence of an analyte in the biological sample, and a reference area comprising at least two colorimetric reference marks (R) each having a predefined color and/or color intensity formed before or irrespective of the exposing of the LFIA strip to the biological sample; removing environmental conditions differences from the captured image or parts thereof that contain the picture of the LFIA strip, by applying a first Al model on the captured image or parts thereof, the first Al model trained on a plurality of images of the colorimetric reference mark (R) captured under different imaging conditions and normalized to a color intensity of the colorimetric reference mark (R) measured using
  • the at least two test areas may include different agents, i.e. different antibodies, each antibody detecting a different protein.
  • the different antibodies may detect different proteins involved in a same medical condition, e.g. different proteins associated with colon cancer.
  • the different antibodies may detect different proteins involved in different medical conditions.
  • At least a portion of the at least two test areas provide a different color and/or color intensity, thereby increasing linear data point range and accuracy. That is, by including a plurality of test areas, each test area including a different concentration of the agent (and thus different detection cut-off points), the linear range of the strip is advantageously expanded and may match or even exceed the accuracy and range of LFIA readers.
  • a first of the control areas may include a large concentration of the secondary antibody and a color/color intensity change will occur as soon as the sample reaches the first control area. This first control area is indicative of the test being performed correctly and initiates the biological timer of the test”. According to some embodiments, if the image is captured prior to the color/color intensity of the first control area occurring a message may be issued to the user (e.g. via a dedicated App) that the test is invalid and should be re-executed. According to some embodiments, a second of the control areas may include a concentration of the secondary antibody that ensures that it will only change its color/color intensity upon sufficient time having passed to expect a test-mark color/color intensity change. According to some embodiments, if the image is captured prior to the color/color intensity change of the second control areas, a message may be issued to the user (e.g. via a dedicated App) to re-capture the image.
  • a third of the control areas may include a concentration of the secondary antibody that ensures that it will only change its color/color intensity when too much time has passed to ensure test reliability.
  • a message may be issued to the user (e.g. via a dedicated App) that the test is invalid and should be re-executed.
  • additional control areas may also be included, each of the additional control areas including different concentrations of the secondary antibody in a range between that of the second and third control areas, such that their color/color intensity changes are indicative of the amount of time having passed between appearance of the first control area color/color intensity change and the capturing of the image, e.g., in intervals of 5, 10, 20, 30 or 60 seconds.
  • each possibility is a separate embodiment.
  • the LFIA includes a plurality of control area indicative of the biological age/time of the test.
  • calculating the concentration of the analyte in the biological sample may further include taking into consideration the biological age/time of the test.
  • a lateral flow immunoassay analysis (LFIA) strip including: a membrane configured for lateral flow of a test sample, the membrane including: a control area configured to produce a colorimetric control mark (C) in response to the lateral flow of the test sample therethrough irrespective of presence or absence of an analyte in the test sample; a test area comprising an agent configured to produce a colorimetric test mark (T) in response to presence of the analyte in the test sample, a reference area including: an isolating layer; and a secondary membrane comprising a colorimetric reference mark (R) having a predefined color intensity, the colorimetric reference mark (R) formed before exposing the LFIA strip to the biological sample, wherein the secondary membrane is shielded off from contact with the test sample by the isolating layer, such that the color intensity of the colorimetric reference mark (R) is independent of lateral flow of the test sample through the LFIA strip.
  • LFIA lateral flow immunoassay analysis
  • the LFIA strip includes at least two test areas (T), each test area comprising a different concentration of the agent, such that a minimal concentration of the analyte sufficient to produce the colorimetric test mark (T) differs in each area.
  • the LFIA strip includes more than two test areas (e.g. 3, 4, 6, 8, 10 or more test areas), least two test areas (T), each test area comprising a different concentration of the agent. Each possibility is a separate embodiment.
  • the LFIA strip comprises at least two colorimetric reference marks (R), each having a different preformed and/or predefined color intensity.
  • the membrane and the secondary membrane are made from the same material. According to some embodiments, the membrane and the secondary membrane are nitrocellulose membranes.
  • the LFIA strip further includes a sample pad configured to receive the test sample.
  • the LFIA strip further includes a conjugation pad configured to release a conjugate that binds the analyte if present.
  • the LFIA strip further includes an absorption pad configured to wick the prevent backflow of the test sample.
  • the LFIA strip further includes an adhesive pad configured to sequentially adhere the sample pad, the conjugation pad, the membrane and the absorption pad.
  • a second of the control areas may include a concentration of the secondary antibody that ensures that it will only change its color/color intensity upon sufficient time having passed to expect a test-mark color/color intensity change.
  • a message may be issued to the user (e.g. via a dedicated App) to re-capture the image.
  • FIG. 1 shows a schematic outline of an LFIA strip 100 according to some embodiments.
  • LFAI strip 100 includes a test area 110 which includes an agent configured to change its color and/or color intensity in response to binding an analyte present in a biological sample run through LFIA strip 100.
  • test area 110 includes a single test area, here in the form of a T-line.
  • LFIA strip 100 further includes a control area 120 (here in the form of a C-line), which produces a colorimetric control mark in response to lateral flow of the biological sample therethrough, but irrespective of presence or absence of an analyte in the test sample.
  • control area serves as a positive control validating the lateral flow of the biological sample through LFIA strip 100.
  • the real concentration of the T-mark may be determined by applying a second trained Al model on the normalized image based on utilizing a linear region (shaded in grey) of the color intensity as shown in FIG. 3.
  • the test can be done at home using a standard smartphone by untrained users.
  • the concentration/color intensity of the T-mark and the embedded reference marks concentrations are known.
  • the Al model e.g. convoluted network model - CNN learns the color intensity distance between the T-mark and matches it to a concentration distance.
  • the given T-mark is then tagged to the color intensity reference mark (obtained by LFAI reader and denoted herein as “CT”) with the shortest concentration distance.
  • the model predicts a probability P for the T-mark to match with a specific color intensity.
  • P[i] is the probability of the color intensity L[i] to have the shortest distance to the T-mark. If C[i] is the predefined concentration of the color intensity L[i], the T- mark concentration will be defined by the equation:
  • FIG. 4A and FIG. 4B show LFIA strips 400 with multiple test areas 410 with different detection cut-off points. This allows expanding the linear range 510 of the strip as illustrated in FIG. 5. Moreover, as seen from FIG. 5, it also improves accuracy in the area where overlapping 520 exists.
  • FC T_dot_concentation[i]
  • Strip concentration P[j] * T _dot_concentration[j].
  • FIG. 6 depicts an LFIA strip 600 that is essentially similar to the LFIA strips described with reference to the previous figures, but which includes a plurality of test areas (single or set), here T-dots 610a and 610b each including a different agent for detecting different analytes.
  • An overall outline of a flow 700 of the herein disclosed method is provided in FIG. 7. It is understood that while the steps are shown as sequential some may be conducted simultaneously. According to some embodiments, at least some of the steps may be executed by applying a single Al model capable of executing various tasks. According to some embodiments, at least some of the steps are executed by applying different Al models.
  • a first Al model may then be applied, which first model is capable of detecting/framing a region of interest (ROI).
  • ROI recognition models may be utilized and specifically trained on LFIA strips. It is understood that during setup, the first algorithm (object detection algorithm, such as YOLO) is trained (step 722) for recognition of LFIA strips. Moreover, as more images are acquired, the training may be continuously updated.
  • a second Al model (a trained CNN model) may be applied on the ROI (step 730), which algorithm is configured to normalize the image properties of the ROI, based on one or more reference marks embedded in the LFIA strip. That is, based on the noise level derived from a detected deviance between the expected color intensity and the actual color intensity of the reference mark in the captured image, an inverse noise algorithm is applied (step 734) on the one or more test areas.
  • the second algorithm is trained (step 732) on a plurality of images captured under various environmental conditions.
  • the concentration of the analyte is determined based on the modified color intensity of the T-marks in step 740. This is achieved by applying a third Al model (CNN model), trained during setup (step 732) for determining a correlation between color intensity and analyte concentration. According to some embodiments, this step also includes taking into consideration the biological age/time of the sample, based on a color/color intensity of two or more control-areas, as essentially described herein. Finally, in step 750 the analyte concentration is outputted and provided to the user and/or a caregiver.
  • CNN model trained during setup
  • first, second and third Al models may be separate models or be part of an integrated flow.
  • FIG. 8 depicts an LFIA 800 strip that is essentially similar to the LFIA strips described with reference to the previous figures, but which further includes a modified control area 870 with a plurality of control-areas, also referred to as an “elapsed time” control area or ETC in which each is configured to change its color and/or color intensity according to the time that has passed since the biological sample has reached the control area. That is, each control area changes its color at different intervals and as such reflects the time that has passed between loading of the sample on sample pad 240 and image capturing.
  • method 700 may be modified to include a fourth Al model configured to adjust the determined analyte concentration based on the time that has passed. Moreover, in case too much time has passed, and the determined concentration is at risk of being inaccurate, an error message may be issued in step 750 of method 700.
  • FIG. 9 illustrates an LFIA strip 900 and associated AL based analysis 950. It is understood that while specific models are mentioned with respect to this figure, these models are exemplary and may be replaced by others. Those skilled in the art will readily understand which algorithms can be suitable at the various steps.
  • LFIA 900 includes a QR-code 902 which links the LFIA strip to a dedicated mobile application.
  • the user is guided through the testing steps and then directed to capture an image of the LFIA upon completion of the test. The user may then upload the image (alternatively it is automatically uploaded), and the virtual lab running dedicated Al models is initiated.
  • the LFIA strip 900 is identified in the image using ROI detection algorithms such as but not limited to Faster R-CNN.
  • the test validity is also identified by identification of a first control area, here shown as first control line 904 which includes a sufficiently high concentration of a secondary antibody to initiate a color/color intensity change essentially as soon as the sample reaches first control line 904.
  • Step 956 a step of noise removal (step 956) is initiated. That is, the image is calibrated using a matrix of 1 reference marks 906 having a predefined color and/or color intensity, to remove noise from the image resulting from the camera used and/or environment conditions, such as lightning etc.
  • Step 956 includes picture distortion analysis via an analysis of the color/color intensity of reference marks 906, and an inverse distortion picture is created.
  • Suitable Al models for use in step 956 include Mask-RCNN, Unet, DeepLav3+, and K-Means.
  • step 958 an analysis of the protein concentration of various proteins (here 4 different proteins) is conducted based on the color/color intensity of test areas 908a-908d.
  • Step 958 includes utilizing Al models trained on the color/color intensity of a large plurality of controlmarks having different known concentration of the analyte (as further elaborated herein). Suitable Al models for use in step 958 include Mask-RCNN, Unet, DeepLav3+, and K-Means. As seen, each test area includes several test dots, each including different concentrations of test antibody (here 3), thereby ensuring that a window obtained at which there is a linear correlation between color intensity and analyte concentration.
  • step 960 which is optional LLM models may be applied to generate a summary report with the test results and optionally an indication of the subject’s risk and/or a recommendation for further actions (e.g. further testing).
  • Suitable Al models for use in step 960 include MedPalm or GPT-4 tuned.
  • the terms “approximately”, “essentially” and “about” in reference to a number are generally taken to include numbers that fall within a range of 5% or in the range of 1% in either direction (greater than or less than) the number unless otherwise stated or otherwise evident from the context (except where such number would exceed 100% of a possible value). Where ranges are stated, the endpoints are included within the range unless otherwise stated or otherwise evident from the context.

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Abstract

L'invention concerne un test immunochromatographique à flux latéral (LFIA) quantitatif, en particulier des kits et une méthode délocalisés à domicile permettant d'effectuer un LFIA quantitatif.
PCT/IL2025/050158 2024-02-14 2025-02-13 Dispositif, système et procédé de test quantitatif colorimétrique à flux latéral basé sur l'ia Pending WO2025173009A1 (fr)

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WO2018060998A1 (fr) * 2016-09-29 2018-04-05 Memed Diagnostics Ltd. Méthodes de pronostic et de traitement
EP3791167A1 (fr) * 2018-05-07 2021-03-17 Immundiagnostik AG Système d'analyse par immunochromatographie sur membrane quantitative
US20220084659A1 (en) * 2020-09-17 2022-03-17 Scanwell Health, Inc. Diagnostic test kits and methods of analyzing the same

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018060998A1 (fr) * 2016-09-29 2018-04-05 Memed Diagnostics Ltd. Méthodes de pronostic et de traitement
EP3791167A1 (fr) * 2018-05-07 2021-03-17 Immundiagnostik AG Système d'analyse par immunochromatographie sur membrane quantitative
US20220084659A1 (en) * 2020-09-17 2022-03-17 Scanwell Health, Inc. Diagnostic test kits and methods of analyzing the same

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