A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

Shi, Zhao; Miao, Chongchang; Joseph Schoepf, U.; H. Savage, Rock; M. Dargis, Danielle; Pan, Chengwei; Chai, Xue; Li Li, Xiu; Xia, Shuang; Zhang, Xin; Gu, Yan; Zhang, Yonggang; Hu, Bin; Xu, Wenda; Zhou, Changsheng; Luo, Song; Wang, Hao; Mao, Li; Liang, Kongming; Wen, Lili; Zhou, Longjiang; Yu, Yizhou; Lu, Guangming; Jiang Zhang, Long

doi:10.1038/s41467-020-19527-w

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A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

Published in Nature Communications • Nov 30, 2020

Authors:

Zhao Shi

Chongchang Miao

U. Joseph Schoepf

Abstract

Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verif...

Subject

Digital subtraction angiography

Medicine

Radiology

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