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:
Jul. 04, 2023

Filed:

Sep. 25, 2020
Applicant:

Hologic, Inc., Marlborough, MA (US);

Inventors:

Ashwini Kshirsagar, Santa Clara, CA (US);

Haili Chui, Santa Clara, CA (US);

Nikolaos Gkanatsios, Danbury, CT (US);

Adora Dsouza, Sunnyvale, CA (US);

Xiangwei Zhang, Fremont, CA (US);

Assignee:

Hologic, Inc., Marlborough, MA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G16H 40/20 (2018.01); G16H 30/20 (2018.01); G16H 50/30 (2018.01); G16H 50/20 (2018.01); G16H 30/40 (2018.01); G16H 10/20 (2018.01); G06Q 10/0631 (2023.01); G06Q 10/0639 (2023.01); G06Q 10/1093 (2023.01); G06T 7/00 (2017.01);
U.S. Cl.
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);
Abstract

Examples of the present disclosure describe systems and methods for predicting the reading time and/or reading complexity of a breast image. In aspects, a first set of data relating to the reading time of breast images may be collected from one or more data sources, such as image acquisition workstations, image review workstations, and healthcare professional profile data. The first set of data may be used to train a predictive model to predict/estimate an expected reading time and/or an expected reading complexity for various breast images. Subsequently, a second set of data comprising at least one breast image may be provided as input to the trained predictive model. The trained predictive model may output an estimated reading time and/or reading complexity for the breast image. The output of the trained predictive model may be used to prioritize mammographic studies or optimize the utilization of available time for radiologists.


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