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

Zhao Shi, Chongchang Miao, U. Joseph Schoepf, Rock H. Savage, Danielle M. Dargis, Chengwei Pan, Xue Chai, Xiu Li Li, Shuang Xia, Xin Zhang, Yan Gu, Yonggang Zhang, Bin Hu, Wenda Xu, Changsheng Zhou, Song Luo, Hao Wang, Li Mao, Kongming Liang, Lili Wen, Longjiang Zhou, Yizhou Yu, Guang Ming Lu () and Long Jiang Zhang ()
Additional contact information
Zhao Shi: Jinling Hospital, Medical School of Nanjing University
Chongchang Miao: Lianyungang First People’s Hospital
U. Joseph Schoepf: Medical University of South Carolina
Rock H. Savage: Medical University of South Carolina
Danielle M. Dargis: Medical University of South Carolina
Chengwei Pan: Peking University
Xue Chai: Affiliated Nanjing Brain Hospital, Nanjing Medical University
Xiu Li Li: DeepWise AI lab.
Shuang Xia: Tianjin First Central Hospital
Xin Zhang: Jinling Hospital, Medical School of Nanjing University
Yan Gu: Lianyungang First People’s Hospital
Yonggang Zhang: Lianyungang First People’s Hospital
Bin Hu: Jinling Hospital, Medical School of Nanjing University
Wenda Xu: Jinling Hospital, Medical School of Nanjing University
Changsheng Zhou: Jinling Hospital, Medical School of Nanjing University
Song Luo: Jinling Hospital, Medical School of Nanjing University
Hao Wang: DeepWise AI lab.
Li Mao: DeepWise AI lab.
Kongming Liang: DeepWise AI lab.
Lili Wen: Jinling Hospital, Medical School of Nanjing University
Longjiang Zhou: Jinling Hospital, Medical School of Nanjing University
Yizhou Yu: DeepWise AI lab.
Guang Ming Lu: Jinling Hospital, Medical School of Nanjing University
Long Jiang Zhang: Jinling Hospital, Medical School of Nanjing University

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: 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 verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients’ care in comparison to clinicians’ assessment.

Date: 2020
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DOI: 10.1038/s41467-020-19527-w

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