Intelligent Medicine (Nov 2022)
Study design of deep learning based automatic detection of cerebrovascular diseases on medical imaging: a position paper from Chinese Association of Radiologists
- Longjiang Zhang,
- Zhao Shi,
- Min Chen,
- Yingmin Chen,
- Jingliang Cheng,
- Li Fan,
- Nan Hong,
- Wenxiao Jia,
- Guihua Jiang,
- Shenghong Ju,
- Xiaogang Li,
- Xiuli Li,
- Changhong Liang,
- Weihua Liao,
- Shiyuan Liu,
- Zaiming Lu,
- Lin Ma,
- Ke Ren,
- Pengfei Rong,
- Bin Song,
- Gang Sun,
- Rongpin Wang,
- Zhibo Wen,
- Haibo Xu,
- Kai Xu,
- Fuhua Yan,
- Yizhou Yu,
- Yunfei Zha,
- Fandong Zhang,
- Minwen Zheng,
- Zhen Zhou,
- Wenzhen Zhu,
- Guangming Lu,
- Zhengyu Jin
Affiliations
- Longjiang Zhang
- Department of Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China
- Zhao Shi
- Department of Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China
- Min Chen
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Beijing 100005, China
- Yingmin Chen
- Department of Radiology, Hebei General Hospital, Shijiazhuang, Hebei 050199, China
- Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Li Fan
- Department of Medical Imaging and Nuclear Medicine, Changzheng Hospital of Naval Medical University, Shanghai 200072, China
- Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing 100044, China
- Wenxiao Jia
- Imaging Center, First Affiliated, Hospital of Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region 830054, China
- Guihua Jiang
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong 510050, China
- Shenghong Ju
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 211189, China
- Xiaogang Li
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, Liaoning 110011, China
- Xiuli Li
- DeepWise AI lab. Beijing 100089, China
- Changhong Liang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 519041, China
- Weihua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Shiyuan Liu
- Department of Medical Imaging and Nuclear Medicine, Changzheng Hospital of Naval Medical University, Shanghai 200072, China
- Zaiming Lu
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110001, China
- Lin Ma
- Department of Radiology, Chinese PLA (People's Liberation Army) General Hospital, Beijing 100853, China
- Ke Ren
- Department of Radiology, Xiang’ an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian 361005, China
- Pengfei Rong
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha, Hunan 410013, China
- Bin Song
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China
- Gang Sun
- Department of Nuclear Medicine, 960 Hospital of PLA, Ji'nan, Shandong 250012, China
- Rongpin Wang
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou 550499, China
- Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, China
- Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, Hubei 430062, China
- Kai Xu
- Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
- Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai 200025, China
- Yizhou Yu
- The University of Hong Kong, Hong Kong Special Administrative Region, China
- Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, China
- Fandong Zhang
- DeepWise AI lab. Beijing 100089, China
- Minwen Zheng
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710032, China
- Zhen Zhou
- DeepWise AI lab. Beijing 100089, China
- Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
- Guangming Lu
- Department of Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China; Corresponding authors: Guangming Lu, Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China (Email: [email protected]); Zhengyu Jin, Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100730, China (Email: [email protected]).
- Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100710, China; Corresponding authors: Guangming Lu, Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China (Email: [email protected]); Zhengyu Jin, Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100730, China (Email: [email protected]).
- Journal volume & issue
-
Vol. 2,
no. 4
pp. 221 – 229
Abstract
In recent years, with the development of artificial intelligence, especially deep learning technology, researches on automatic detection of cerebrovascular diseases on medical images have made tremendous progress and these models are gradually entering into clinical practice. However, because of the complexity and flexibility of the deep learning algorithms, these researches have great variability on model building, validation process, performance description and results interpretation. The lack of a reliable, consistent, standardized design protocol has, to a certain extent, affected the progress of clinical translation and technology development of computer aided detection systems. After reviewing a large number of literatures and extensive discussion with domestic experts, this position paper put forward recommendations of standardized design on the key steps of deep learning-based automatic image detection models for cerebrovascular diseases. With further research and application expansion, this position paper would continue to be updated and gradually extended to evaluate the generalizability and clinical application efficacy of such tools.