The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 06, 2026

Filed:

Sep. 28, 2023
Applicant:

Aidoc Medical Ltd, Tel Aviv, IL;

Inventors:

Itay Katzir, Ganei Tikva, IL;

Ariel Persiko, Lod, IL;

Idan Bassukevitz, Givatayim, IL;

Assignee:

Aidoc Medical Ltd, Tel Aviv, IL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/94 (2022.01); G16H 30/40 (2018.01);
U.S. Cl.
CPC ...
G06V 10/776 (2022.01); G06V 10/774 (2022.01); G06V 10/945 (2022.01); G16H 30/40 (2018.01); G06V 2201/03 (2022.01); G06V 2201/10 (2022.01);
Abstract

There is provided a method, comprising: feeding target metadata parameters of target medical image(s) into a performance estimation machine learning (ML) model, and obtaining a performance metric of a computer vision ML model as an outcome of the performance estimation ML model, wherein the performance estimation ML model is trained on a training dataset comprising records, wherein a record is created by feeding a sample medical image into the computer vision ML model, obtaining an outcome of the computer vision ML model, obtaining a ground truth of the sample medical image corresponding to the outcome, extracting metadata parameters associated with the sample medical image, and wherein the record includes metadata parameters associated with the sample medical image and a ground truth label comprising a performance metric of the computer vision ML model computed based on the ground truth of the sample medical image and the outcome.


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