We aim to develop and validate a deep learning-based system that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting gastric neoplasm, identifying EGC, and predicting EGC invasion depth and differentiation status. Herein, we provide a state-of-the-art comparison of the system with endoscopists using real-time videos in a nationwide human-machine competition.

This multicenter, prospective, real-time, competitive comparative, diagnostic study enrolled consecutive patients who received magnifying narrow-band (M-NBI) endoscopy in Peking University Cancer Hospital from June 9, 2020, to November 17, 2020. The offline competition was conducted in Wuhan, China, and the endoscopists and the system simultaneously read the patients’ videos and made the diagnoses. The primary outcomes were sensitivity in detecting neoplasms and diagnosing EGCs.

In total, 100 videos, including 37 EGCs and 63 noncancerous lesions, were enrolled; 46 endoscopists from 44 hospitals in 19 provinces in China participated in the competition. The sensitivity rates of the system for detecting neoplasms and diagnosing EGCs were 87.81% and 100%, respectively, significantly higher than those of endoscopists (83.51%; 95% confidence interval [CI], 81.23 - 85.79); (87.13%; 95% CI, 83.75 - 90.51), respectively]. Accuracy rates of the system for predicting EGC invasion depth and differentiation status were 78.57% and 71.43%, respectively, slightly higher than those of endoscopists (63.75%; 95% CI, 61.12 - 66.39); (64.41%; 95% CI, 60.65 - 68.16).

The system outperformed endoscopists in identifying EGCs and comparable with endoscopists in predicting EGC invasion depth and differentiation status in videos. It could be a powerful tool to assist endoscopists in EGC diagnosis in clinical practice.