A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images
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.
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Nature Portfolio
2020
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oai:doaj.org-article:5ecb0208268e4d118fe4dd3dd8db1b142021-12-02T17:31:24ZA clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images10.1038/s41467-020-19527-w2041-1723https://doaj.org/article/5ecb0208268e4d118fe4dd3dd8db1b142020-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19527-whttps://doaj.org/toc/2041-1723Interpretation 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.Zhao ShiChongchang MiaoU. Joseph SchoepfRock H. SavageDanielle M. DargisChengwei PanXue ChaiXiu Li LiShuang XiaXin ZhangYan GuYonggang ZhangBin HuWenda XuChangsheng ZhouSong LuoHao WangLi MaoKongming LiangLili WenLongjiang ZhouYizhou YuGuang Ming LuLong Jiang ZhangNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020) |
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DOAJ |
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DOAJ |
| language |
EN |
| topic |
Science Q |
| spellingShingle |
Science Q 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 A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images |
| description |
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. |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Zhao Shi |
| title |
A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images |
| title_short |
A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images |
| title_full |
A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images |
| title_fullStr |
A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images |
| title_full_unstemmed |
A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images |
| title_sort |
clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images |
| publisher |
Nature Portfolio |
| publishDate |
2020 |
| url |
https://doaj.org/article/5ecb0208268e4d118fe4dd3dd8db1b14 |
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