Application of an artificial intelligence system for endoscopic diagnosis of superficial esophageal squamous cell carcinoma.

Background: Upper gastrointestinal endoscopy is critical for esophageal squamous cell carcinoma (ESCC) detection; however, endoscopists require long-term training to avoid missing superficial lesions.

Aim: To develop a deep learning computer-assisted diagnosis (CAD) system for endoscopic detection of superficial ESCC and investigate its application value.

Methods: We configured the CAD system for white-light and narrow-band imaging modes based on the YOLO v5 algorithm. A total of 4447 images from 837 patients and 1695 images from 323 patients were included in the training and testing datasets, respectively. Two experts and two non-expert endoscopists reviewed the testing dataset independently and with computer assistance. The diagnostic performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity.

Results: The area under the receiver operating characteristics curve, accuracy, sensitivity, and specificity of the CAD system were 0.982 [95% confidence interval (CI): 0.969-0.994], 92.9% (95%CI: 89.5%-95.2%), 91.9% (95%CI: 87.4%-94.9%), and 94.7% (95%CI: 89.0%-97.6%), respectively. The accuracy of CAD was significantly higher than that of non-expert endoscopists (78.3%, < 0.001 compared with CAD) and comparable to that of expert endoscopists (91.0%, = 0.129 compared with CAD). After referring to the CAD results, the accuracy of the non-expert endoscopists significantly improved (88.2% 78.3%, < 0.001). Lesions with Paris classification type 0-IIb were more likely to be inaccurately identified by the CAD system.

Conclusion: The diagnostic performance of the CAD system is promising and may assist in improving detectability, particularly for inexperienced endoscopists.