US 11,887,307 B1Granted

Computer systems and computer-implemented methods for rapid diagnostic test result interpretation platform utilizing computer vision

AssigneeBio-Marketing-T, Ltd. (Bmt)

InventorsRoey Tamir, Idan Tamir

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Abstract

Disclosed are computer systems and computer-implemented methods for rapid diagnostic test result interpretation platform employing computer vision. The system can visually analyze the test device in real-time by receiving image streams of the test device from the camera system. The detection system can be used to identify the test result in the test region. In some embodiments, the test result is binary (e.g., Positive/Negative). In some embodiments, the test result is in semi-quantitative formats (e.g., 1-10). The test result can be sent back to the user, operator, HMO, or physician for further confirmation, recording, and medical treatment decision/follow-up. The detection system can close the loop between the user of the test device and the HMO/physician for recording of the test result and the provision of medical treatment/follow-up.

Claims21

Dependent claims

A method comprising:

receiving, by a processor, at least one first image of a first device;
training, by the processor, a deep learning module comprising a plurality of neural networks to:
identify the first device in the at least one first image and one of a presence or absence of a first test region of the first device in the at least one first image based on classifying a plurality of device features and a plurality of test region features identified in the at least one first image compared with a first reference image of the first device;
when the absence of the first test region in the at least one first image is identified or when the classifying of the plurality of test region features is below a base-level confidence:
generate at least one imaging directing command to capture at least one second image of at least one of the first device or the first test region of the first device;
wherein at least one neural network of the plurality of neural networks is configured based on at least one computer vision technique;
receiving, by the processor, the at least one second image based on the at least one imaging directing command;
inputting, by the processor, the at least one second image of the first device into the deep learning module to:
classify the plurality of device features and the plurality of test region features of the first test region identified in the at least one second image compared with a second reference image of a visual indicator indicative of a first test result in the first test region of the first device;
when the presence of the first test region in the at least one second image is identified or when the classifying of the plurality of test region features is higher than the base-level confidence:
retrain the deep learning module for improved identification of the first device based on the plurality of device features and the plurality of test region features identified in the at least one first image and the at least one second image;
and
train the deep learning module for identification of the first test result in the first test region based on the plurality of device features and the plurality of test region features identified in the at least one first image and the at least one second image;
receiving, by the processor, at least one third image of a second device;
and
inputting, by the processor, the at least one third image into the deep learning module to identify a second test result in a second test region of the second device in the at least one third image.

13 more elements

A system comprising:

a processor configured to:
receive at least one first image of a first device;
train a deep learning module comprising a plurality of neural networks to:
identify the first device in the at least one first image and one of a presence or absence of a first test region of the first device in the at least one first image based on classifying a plurality of device features and a plurality of test region features identified in the at least one first image compared with a first reference image of the first device;
when the absence of the first test region in the at least one first image is identified or when the classifying of the plurality of test region features is below a base-level confidence:
generate at least one imaging directing command to capture at least one second image of at least one of the first device or the first test region of the first device;
wherein at least one neural network of the plurality of neural networks is configured based on at least one computer vision technique;
receive the at least one second image based on the at least one imaging directing command;
input the at least one second image of the first device into the deep learning module to:
classify the plurality of device features and the plurality of test region features of the first test region identified in the at least one second image compared with a second reference image of a visual indicator indicative of a first test result in the first test region of the first device;
when the presence of the first test region in the at least one second image is identified or when the classifying of the plurality of test region features is higher than the base-level confidence:
retrain the deep learning module for improved identification of the first device based on the plurality of device features and the plurality of test region features identified in the at least one first image and the at least one second image;
and
train the deep learning module for identification of the first test result in the first test region based on the plurality of device features and the plurality of test region features identified in the at least one first image and the at least one second image;
receive at least one third image of a second device;
and
input the at least one third image into the deep learning module to identify a second test result in a second test region of the second device in the at least one third image.

14 more elements

At least one computer-readable storage medium encoded with computer-executable instructions that, when executed by a computer, cause the computer to carry out a method of:

receiving at least one first image of a first device;
training a deep learning module comprising a plurality of neural networks to:
identify the first device in the at least one first image and one of a presence or absence of a first test region of the first device in the at least one first image based on classifying a plurality of device features and a plurality of test region features identified in the at least one first image compared with a first reference image of the first device;
when the absence of the first test region in the at least one first image is identified or when the classifying of the plurality of test region features is below a base-level confidence:
generate at least one imaging directing command to capture at least one second image of at least one of the first device or the first test region of the first device;
wherein at least one neural network of the plurality of neural networks is configured based on at least one computer vision technique;
receiving the at least one second image based on the at least one imaging directing command;
inputting the at least one second image of the first device into the deep learning module to:
classify the plurality of device features and the plurality of test region features of the first test region identified in the at least one second image compared with a second reference image of a visual indicator indicative of a first test result in the first test region of the first device;
when the presence of the first test region in the at least one second image is identified or when the classifying of the plurality of test region features is higher than the base-level confidence:
retrain the deep learning module for improved identification of the first device based on the plurality of device features and the plurality of test region features identified in the at least one first image and the at least one second image;
and
train the deep learning module for identification of the first test result in the first test region based on the plurality of device features and the plurality of test region features identified in the at least one first image and the at least one second image;
receiving at least one third image of a second device;
and
inputting the at least one third image into the deep learning module to identify a second test result in a second test region of the second device in the at least one third image.

13 more elements

Dependencies are inferred from the claim text.

Facts

Application: 18/064,649Filed 12 Dec 2022 · continuation
Priority: 10 Aug 2022
Published: US 2024/0054647 A115 Feb 2024
Granted: 30 Jan 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification10 codes

G06T 7/0012
Inspection of images, e.g. flaw detection: biomedical image inspection
G06T 7/0014
Biomedical image inspection: using an image reference approach
G06V 10/25
Image preprocessing: determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/764
Using pattern recognition or machine learning: using classification, e.g. of video objects