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Yuan XL, Liu W, Liu Y, Zeng XH, Mou Y, Wu CC, Ye LS, Zhang YH, He L, Feng J, Zhang WH, Wang J, Chen X, Hu YX, Zhang KH, Hu B. Artificial intelligence for diagnosing microvessels of precancerous lesions and superficial esophageal squamous cell carcinomas: a multicenter study. Surg Endosc 2022; 36:8651-8662. [PMID: 35705757 PMCID: PMC9613556 DOI: 10.1007/s00464-022-09353-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 05/20/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND Intrapapillary capillary loop (IPCL) is an important factor for predicting invasion depth of esophageal squamous cell carcinoma (ESCC). The invasion depth is closely related to the selection of treatment strategy. However, diagnosis of IPCLs is complicated and subject to interobserver variability. This study aimed to develop an artificial intelligence (AI) system to predict IPCLs subtypes of precancerous lesions and superficial ESCC. METHODS Images of magnifying endoscopy with narrow band imaging from three hospitals were collected retrospectively. IPCLs subtypes were annotated on images by expert endoscopists according to Japanese Endoscopic Society classification. The performance of the AI system was evaluated using internal and external validation datasets (IVD and EVD) and compared with that of the 11 endoscopists. RESULTS A total of 7094 images from 685 patients were used to train and validate the AI system. The combined accuracy of the AI system for diagnosing IPCLs subtypes in IVD and EVD was 91.3% and 89.8%, respectively. The AI system achieved better performance than endoscopists in predicting IPCLs subtypes and invasion depth. The ability of junior endoscopists to diagnose IPCLs subtypes (combined accuracy: 84.7% vs 78.2%, P < 0.0001) and invasion depth (combined accuracy: 74.4% vs 67.9%, P < 0.0001) were significantly improved with AI system assistance. Although there was no significant differences, the performance of senior endoscopists was slightly elevated. CONCLUSIONS The proposed AI system could improve the diagnostic ability of endoscopists to predict IPCLs classification of precancerous lesions and superficial ESCC.
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Affiliation(s)
- Xiang-Lei Yuan
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, China
| | - Wei Liu
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, China
| | - Yan Liu
- School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Xian-Hui Zeng
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, China
| | - Yi Mou
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, China
| | - Chun-Cheng Wu
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, China
| | - Lian-Song Ye
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, China
| | - Yu-Hang Zhang
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, China
| | - Long He
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, China
| | - Jing Feng
- Department of Gastroenterology, Zhongshan Hospital, Xiamen University, Xiamen, China
| | - Wan-Hong Zhang
- Department of Gastroenterology, Cangxi People's Hospital, Guangyuan, China
| | - Jun Wang
- Department of Gastroenterology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xin Chen
- The First People's Hospital of Shuangliu District, Chengdu, China
| | - Yan-Xing Hu
- Xiamen Innovision Medical Technology Co, Ltd., Xiamen, China
| | - Kai-Hua Zhang
- ERCDF, Ministry of Education and School of Computing and Software, Nanjing University of Information Science and Technology, Nanjing, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, China.
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