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:
Mar. 25, 2025

Filed:

Nov. 16, 2020
Applicant:

Samsung Life Public Welfare Foundation, Seoul, KR;

Inventors:

Oh Young Bang, Seoul, KR;

Yoon-Chul Kim, Seoul, KR;

Jong-Won Chung, Seoul, KR;

Woo-Keun Seo, Seoul, KR;

Gyeong-Moon Kim, Seoul, KR;

Geon Ha Kim, Gwacheon si, KR;

Pyoung Jeon, Seoul, KR;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/055 (2006.01); A61B 5/00 (2006.01); A61B 6/50 (2024.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06T 7/00 (2017.01); G06V 10/25 (2022.01); G06V 10/50 (2022.01); G06V 10/75 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01);
U.S. Cl.
CPC ...
A61B 5/055 (2013.01); A61B 5/0042 (2013.01); A61B 5/7267 (2013.01); A61B 5/7275 (2013.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06T 7/0014 (2013.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); A61B 6/501 (2013.01); A61B 2576/026 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30096 (2013.01); G06V 10/25 (2022.01); G06V 10/50 (2022.01); G06V 10/758 (2022.01); G06V 10/811 (2022.01); G06V 10/82 (2022.01); G06V 2201/031 (2022.01);
Abstract

According to an embodiment of the present disclosure, there is provided a method of predicting a brain tissue lesion distribution, the method including: a model learning operation of learning a prediction model for predicting a brain tissue lesion distribution of a subject by using brain image data of a plurality of previous patients; an input obtaining operation of obtaining input data from brain image data of the subject; and an output operation of generating output image data including information on the lesion distribution after recanalization treatment for the subject, by using the prediction model. The prediction model includes a success prediction model that is learned by using data of patients in which recanalization treatment is successful among the plurality of previous patients, and a failure prediction model that is learned by using data of patients in which recanalization treatment fails among the plurality of previous patients.


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