US 12,119,107 B2
AI system for predicting reading time and reading complexity for reviewing 2D/3D breast images
Ashwini Kshirsagar, Santa Clara, CA (US); Haili Chui, Santa Clara, CA (US); Nikolaos Gkanatsios, Danbury, CT (US); Adora Dsouza, Sunnyvale, CA (US); and Xiangwei Zhang, Fremont, CA (US)
Assigned to Hologic, Inc., Marlborough, MA (US)
Filed by Hologic, Inc., Marlborough, MA (US)
Filed on May 18, 2023, as Appl. No. 18/319,727.
Application 18/319,727 is a continuation of application No. 17/033,372, filed on Sep. 25, 2020, granted, now 11,694,792.
Claims priority of provisional application 62/907,257, filed on Sep. 27, 2019.
Prior Publication US 2024/0021297 A1, Jan. 18, 2024
Int. Cl. G16H 40/20 (2018.01); G06Q 10/0631 (2023.01); G06Q 10/0639 (2023.01); G06Q 10/1093 (2023.01); G06T 7/00 (2017.01); G16H 10/20 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01)
CPC G16H 40/20 (2018.01) [G06Q 10/06311 (2013.01); G06Q 10/06398 (2013.01); G06Q 10/1097 (2013.01); G06T 7/0012 (2013.01); G16H 10/20 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G06T 2200/24 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30068 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method of analyzing medical image data, the method comprising:
receiving, from an X-ray imaging system, mammographic exam data for a patient, wherein the mammographic exam data includes breast image data including one or more X-ray images of the patient's breast tissue;
processing the breast image data to determine one or more image factors;
providing the mammographic exam data and the determined one or more image factors to a predictive model;
determining, by the predictive model, correlations between the one or more image factors and training factors, wherein the training factors are determined based at least in part on mammographic exam data for one or more patients and evaluation data for one or more exam readers, wherein determining the correlations comprises evaluating the determined one or more image factors against a complexity index, wherein the complexity index comprises machine learning-generated mappings of a plurality of factors that affect an amount of time required to interpret the mammographic exam data, wherein the plurality of factors include image factors derived from a training set of mammographic exam data and reader factors derived from a training set of evaluation data;
determining an expected reading time for the mammographic exam data based on the correlations;
assigning a complexity label to the breast image data based on at least one of the expected reading time, the correlations, and a portion of the breast image data;
receiving a selection of the mammographic exam data for the patient; and
in response to receiving the selection of the mammographic exam data, displaying the complexity label in association with the mammographic exam data.