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.
Patent No.:
Date of Patent:
Feb. 10, 2026
Filed:
Mar. 29, 2023
Beijing Tiantan Hospital, Capital Medical University, Beijing, CN;
Beihang University, Beijing, CN;
Zixiao Li, Beijing, CN;
Tao Liu, Beijing, CN;
Liyuan Zhang, Beijing, CN;
Yongjun Wang, Beijing, CN;
Yuehua Pu, Beijing, CN;
Jing Jing, Beijing, CN;
Jian Cheng, Beijing, CN;
Ziyang Liu, Beijing, CN;
Zhe Zhang, Beijing, CN;
Wanlin Zhu, Beijing, CN;
Beijing Tiantan Hospital, Capital Medical University, Beijing, CN;
Beihang University, Beijing, CN;
Abstract
The present disclosure provides an intracranial artery stenosis detection method and system. The present disclosure obtains artery stenosis detection results based on a first maximum intensity projection (MIP) image and a second MIP image obtained by preprocessing a medical image by adopting a detection model based on an adaptive triplet attention module and generates an auxiliary report and visualization results according to target category information in the artery stenosis detection results. Therefore, the problem that existing manual interpretation methods are easily affected by the subjective experience of doctors and are time-consuming and laborious can be solved, thus improving accuracy and efficiency of intracranial artery stenosis detection. Moreover, by inserting the adaptive triplet attention module into a backbone network of YOLOv4, the present disclosure can realize focus on key regions of high-dimensional features, reduce focus on irrelevant features, and provide characterization ability of the detection model.