BACKGROUND: Artificial intelligence (AI) has made remarkable progress in image recognition using deep learning systems. It has been used to detect esophageal squamous cell carcinoma (ESCC); however, none of the previous reports were investigations in a clinical setting, being retrospective in design. We therefore conducted this trial to determine how AI can help endoscopists detect ESCC in a clinical setting.

METHODS: This was a prospective, single-center, exploratory, and randomized controlled trial. High risk patients with ESCC undergoing screening or surveillance esophagogastroduodenoscopy were enrolled and randomly assigned to either the AI or control groups. In the AI group, the endoscopists watched both the AI monitor that detected ESCC with annotation and the normal monitor simultaneously; in the control group, the endoscopists watched only the normal monitor. In both groups, the endoscopists observed the esophagus using white-light imaging (WLI), followed by narrow-band imaging (NBI), then iodine staining. The primary end point was the enhanced detection rate of ESCC by nonexperts using AI. The detection rate was defined as the ratio of WLI/NBI-detected ESCCs to all ESCCs detected by iodine staining.

RESULTS: 320 patients were included in the analysis. The detection rate of ESCC among nonexperts was 47% in the AI group and 45% in the control group (P = 0.93), with no significant difference, which was similarly found for experts (87% vs. 57%; P = 0.20) and all endoscopists (57% vs. 50%; P = 0.70).

CONCLUSIONS: This study could not demonstrate an improvement in the esophageal cancer detection rate using the AI diagnostic support system for ESCC.