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Lin D, Xiong J, Liu C, Zhao L, Li Z, Yu S, Wu X, Ge Z, Hu X, Wang B, Fu M, Zhao X, Wang X, Zhu Y, Chen C, Li T, Li Y, Wei W, Zhao M, Li J, Xu F, Ding L, Tan G, Xiang Y, Hu Y, Zhang P, Han Y, Li JPO, Wei L, Zhu P, Liu Y, Chen W, Ting DSW, Wong TY, Chen Y, Lin H. Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study. LANCET DIGITAL HEALTH 2021; 3:e486-e495. [PMID: 34325853 DOI: 10.1016/s2589-7500(21)00086-8] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 04/21/2021] [Accepted: 05/07/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND Medical artificial intelligence (AI) has entered the clinical implementation phase, although real-world performance of deep-learning systems (DLSs) for screening fundus disease remains unsatisfactory. Our study aimed to train a clinically applicable DLS for fundus diseases using data derived from the real world, and externally test the model using fundus photographs collected prospectively from the settings in which the model would most likely be adopted. METHODS In this national real-world evidence study, we trained a DLS, the Comprehensive AI Retinal Expert (CARE) system, to identify the 14 most common retinal abnormalities using 207 228 colour fundus photographs derived from 16 clinical settings with different disease distributions. CARE was internally validated using 21 867 photographs and externally tested using 18 136 photographs prospectively collected from 35 real-world settings across China where CARE might be adopted, including eight tertiary hospitals, six community hospitals, and 21 physical examination centres. The performance of CARE was further compared with that of 16 ophthalmologists and tested using datasets with non-Chinese ethnicities and previously unused camera types. This study was registered with ClinicalTrials.gov, NCT04213430, and is currently closed. FINDINGS The area under the receiver operating characteristic curve (AUC) in the internal validation set was 0·955 (SD 0·046). AUC values in the external test set were 0·965 (0·035) in tertiary hospitals, 0·983 (0·031) in community hospitals, and 0·953 (0·042) in physical examination centres. The performance of CARE was similar to that of ophthalmologists. Large variations in sensitivity were observed among the ophthalmologists in different regions and with varying experience. The system retained strong identification performance when tested using the non-Chinese dataset (AUC 0·960, 95% CI 0·957-0·964 in referable diabetic retinopathy). INTERPRETATION Our DLS (CARE) showed satisfactory performance for screening multiple retinal abnormalities in real-world settings using prospectively collected fundus photographs, and so could allow the system to be implemented and adopted for clinical care. FUNDING This study was funded by the National Key R&D Programme of China, the Science and Technology Planning Projects of Guangdong Province, the National Natural Science Foundation of China, the Natural Science Foundation of Guangdong Province, and the Fundamental Research Funds for the Central Universities. TRANSLATION For the Chinese translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jianhao Xiong
- Beijing Eaglevision Technology Development, Beijing, China
| | - Congxin Liu
- Beijing Eaglevision Technology Development, Beijing, China
| | - Lanqin Zhao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zhongwen Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shanshan Yu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zongyuan Ge
- Department of Electrical and Computer Systems Engineering, Faculty of Engineering, Monash University, Melbourne, VIC, Australia
| | - Xinyue Hu
- Beijing Eaglevision Technology Development, Beijing, China
| | - Bin Wang
- Beijing Eaglevision Technology Development, Beijing, China
| | - Meng Fu
- Beijing Eaglevision Technology Development, Beijing, China
| | - Xin Zhao
- Beijing Eaglevision Technology Development, Beijing, China
| | - Xin Wang
- Centre for Precision Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yi Zhu
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Chuan Chen
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Tao Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yonghao Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Wenbin Wei
- Beijing Tongren Eye Centre, Beijing Key Laboratory of Intraocular Tumour Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Mingwei Zhao
- Department of Ophthalmology, Ophthalmology and Optometry Centre, Peking University People's Hospital, Beijing, China
| | - Jianqiao Li
- Department of Ophthalmology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Fan Xu
- Department of Ophthalmology, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Lin Ding
- Department of Ophthalmology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Shanxi, China
| | - Gang Tan
- Department of Ophthalmology, University of South China, Hengyang, Hunan, China
| | - Yi Xiang
- Department of Ophthalmology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yongcheng Hu
- Bayannur Paralympic Eye Hospital, Bayannur, Inner Mongolia, China
| | - Ping Zhang
- Bayannur Paralympic Eye Hospital, Bayannur, Inner Mongolia, China
| | - Yu Han
- Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China
| | | | - Lai Wei
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Pengzhi Zhu
- Guangdong Medical Devices Quality Surveillance and Test Institute, Guangzhou, Guangdong, China
| | - Yizhi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Weirong Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Daniel S W Ting
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Tien Y Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Yuzhong Chen
- Beijing Eaglevision Technology Development, Beijing, China.
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China; Centre for Precision Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
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