Nature Communications (Nov 2020)
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,
- Long Jiang Zhang
Affiliations
- Zhao Shi
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University
- Chongchang Miao
- Department of Radiology, Lianyungang First People’s Hospital
- U. Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina
- Rock H. Savage
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina
- Danielle M. Dargis
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina
- Chengwei Pan
- Computer Science Department, School of EECS, Peking University
- Xue Chai
- Department of Radiology, Affiliated Nanjing Brain Hospital, Nanjing Medical University
- Xiu Li Li
- DeepWise AI lab.
- Shuang Xia
- Department of Radiology, Tianjin First Central Hospital
- Xin Zhang
- Department of Neurosurgery, Jinling Hospital, Medical School of Nanjing University
- Yan Gu
- Department of Radiology, Lianyungang First People’s Hospital
- Yonggang Zhang
- Department of Radiology, Lianyungang First People’s Hospital
- Bin Hu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University
- Wenda Xu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University
- Changsheng Zhou
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University
- Song Luo
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University
- Hao Wang
- DeepWise AI lab.
- Li Mao
- DeepWise AI lab.
- Kongming Liang
- DeepWise AI lab.
- Lili Wen
- Department of Neurosurgery, Jinling Hospital, Medical School of Nanjing University
- Longjiang Zhou
- Department of Neurosurgery, Jinling Hospital, Medical School of Nanjing University
- Yizhou Yu
- DeepWise AI lab.
- Guang Ming Lu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University
- Long Jiang Zhang
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University
- DOI
- https://doi.org/10.1038/s41467-020-19527-w
- Journal volume & issue
-
Vol. 11,
no. 1
pp. 1 – 11
Abstract
Interpretation of Computed Tomography Angiography for intracranial aneurysm diagnosis can be time-consuming and challenging. Here, the authors present a deep-learning-based framework achieving improved performance compared to that of radiologists and expert neurosurgeons.
