Study design of deep learning based automatic detection of cerebrovascular diseases on medical imaging: a position paper from Chinese Association of Radiologists
单位：[1]Department of Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China[2]Department of Radiology, Beijing Hospital, National Center of Gerontology, Beijing 100005, China[3]Department of Radiology, Hebei General Hospital, Shijiazhuang, Hebei 050199, China[4]Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China[5]Department of Medical Imaging and Nuclear Medicine, Changzheng Hospital of Naval Medical University, Shanghai 200072, China[6]Department of Radiology, Peking University People’s Hospital, Beijing 100044, China[7]Imaging Center, First Affiliated, Hospital of Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region 830054, China[8]Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong 510050, China[9]Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 211189, China[10]Department of Radiology, General Hospital of Northern Theater Command, Shenyang, Liaoning 110011, China[11]DeepWise AI lab. Beijing 100089, China[12]Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 519041, China[13]Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China[14]Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110001, China中国医科大学附属盛京医院中国医科大学盛京医院[15]Department of Radiology, Chinese PLA (People’s Liberation Army) General Hospital, Beijing 100853, China[16]Department of Radiology, Xiang’ an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian 361005, China[17]Department of Radiology, Third Xiangya Hospital, Central South University, Changsha, Hunan 410013, China[18]Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China四川大学华西医院[19]Department of Nuclear Medicine, 960 Hospital of PLA, Ji’nan, Shandong 250012, China[20]Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, Guizhou 550499, China[21]Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, China南方医科大学珠江医院[22]Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, Hubei 430062, China[23]Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu 221004, China[24]Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai 200025, China[25]The University of Hong Kong, Hong Kong Special Administrative Region, China[26]Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, China[27]Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi 710032, China[28]Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China放射科华中科技大学同济医学院附属同济医院[29]Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100710, China
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.
基金：
Key Program of the National Natural Sci-ence Foundation of China [81830057, 82230068]; Young Scientists Fund of the National Natural Science Foundation of China [82102155]
第一作者单位：[1]Department of Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China
共同第一作者：
通讯作者：
推荐引用方式(GB/T 7714)：
Zhang Longjiang,Shi Zhao,Chen Min,et al.Study design of deep learning based automatic detection of cerebrovascular diseases on medical imaging: a position paper from Chinese Association of Radiologists[J].INTELLIGENT MEDICINE.2022,2(4):221-229.doi:10.1016/j.imed.2022.07.001.
APA：
Zhang, Longjiang,Shi, Zhao,Chen, Min,Chen, Yingmin,Cheng, Jingliang...&Jin, Zhengyu.(2022).Study design of deep learning based automatic detection of cerebrovascular diseases on medical imaging: a position paper from Chinese Association of Radiologists.INTELLIGENT MEDICINE,2,(4)
MLA：
Zhang, Longjiang,et al."Study design of deep learning based automatic detection of cerebrovascular diseases on medical imaging: a position paper from Chinese Association of Radiologists".INTELLIGENT MEDICINE 2..4(2022):221-229
