Artificial intelligence for detecting and delineating a small flat-type early esophageal squamous cell carcinoma under multimodal imaging

CC BY-NC-ND 4.0 · Endoscopy 2023; 55(S 01): E141-E142
DOI: 10.1055/a-1956-0569

E-Videos

Autor*innen

Xianglei Yuan‡

¹Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
Xianhui Zeng‡

¹Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
Long He

¹Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
Liansong Ye

¹Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
Wei Liu

¹Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
Yanxing Hu

²Xiamen Innovision Medical Technology Co., Ltd., Xiamen, Fujian Province, China
Bing Hu

¹Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China

References
1 Fukuda H, Ishihara R, Kato Y. et al. Comparison of performances of artificial intelligence versus expert endoscopists for real-time assisted diagnosis of esophageal squamous cell carcinoma (with video). Gastrointest Endosc 2020; 92: 848-855
2 Yang XX, Li Z, Shao XJ. et al. Real-time artificial intelligence for endoscopic diagnosis of early esophageal squamous cell cancer (with video). Dig Endosc 2021; 33: 1075-1084
3 Yuan XL, Guo LJ, Liu W. et al. Artificial intelligence for detecting superficial esophageal squamous cell carcinoma under multiple endoscopic imaging modalities: A multicenter study. J Gastroenterol Hepatol 2022; 37: 169-178
4 Bolya D, Zhou C, Xiao F. et al. YOLACT++ Better real-time instance segmentation. IEEE Trans Pattern Anal Mach Intell 2022; 44: 1108-1121