AI system offers no benefit in upper endoscopy, study finds

Artificial intelligence (AI) assistance during upper endoscopy did not significantly improve the detection of gastric neoplasms after centralized pathology review in a large multi-center randomized controlled trial, although the system reduced blind spots and increased inspection time.

"Our study is the first to underscore the critical role of rigorous pathologic review in evaluating AI systems," noted Zehua Dong, PhD, of the Department of Gastroenterology at Renmin Hospital of Wuhan University in China, and colleagues.

The study, published in Gastroenterology, enrolled 29,514 patients undergoing esophagogastroduodenoscopy (EGD) at 24 hospitals across 12 provinces in China between December 2021 and November 2023. Patients were randomized to AI-assisted EGD using the ENDOANGEL-GN system or standard EGD without AI assistance. The primary endpoint was the detection rate of gastric neoplasms after centralized endoscopic and pathologic review.

In the intention-to-treat cohort, gastric neoplasms were detected in 1.42% of patients in the AI-assisted group compared with 1.25% in the control group, indicating no statistically significant difference.

When outcomes were assessed using the original pathology reports from participating centers, the AI-assisted group showed a higher detection rate (4.06% vs 3.57%). This difference was not significant after centralized pathology review. However, "subgroup analysis suggested potential benefit among less experienced endoscopists and during fatigue periods," wrote Dr. Dong and colleagues.

The trial also found substantial reclassification of dysplasia diagnoses during centralized pathology review. After expert review, 83.58% of lesions initially diagnosed as low-grade intraepithelial neoplasia and 14% of those diagnosed as high-grade intraepithelial neoplasia were reclassified as benign. In contrast, only 0.72% of lesions initially considered non-neoplastic were later reclassified as neoplasms.

Secondary outcomes showed no significant differences between groups in early gastric cancer detection or in detection of intestinal metaplasia and/or gastric atrophy in the intention-to-treat analysis after pathology review.

Quality metrics differed between the groups. The AI-assisted system significantly reduced the number of blind spots during endoscopy, with an average of 1.07 blind spots in the AI group compared with 2.52 in the control group. Procedure time and inspection time were longer in the AI-assisted group (7.69 vs 7.33 minutes, respectively).

In exploratory subgroup analyses, AI assistance was associated with a higher detection rate of gastric neoplasms among endoscopists with less than three years of experience (1.44% vs 0.78%).

The investigators also evaluated the diagnostic performance of the AI system in lesion recognition. In the experimental group, ENDOANGEL-GN identified 100% of pathologically confirmed gastric adenocarcinoma, 91.9% of high-grade intraepithelial neoplasia, and 57.1% of low-grade intraepithelial neoplasia. False-positive alerts were most commonly associated with chronic inflammation, intestinal metaplasia, gastric atrophy, and other benign mucosal findings.

The authors noted that the study population included a high proportion of tertiary hospitals and experienced endoscopists, which may have influenced the results.

"Although no overall benefit was observed in detecting gastric neoplasms, the findings highlight the critical impact of centralized pathologic review on the evaluation of AI performance. Subgroup analyses suggest that AI may provide advantages in specific clinical scenarios, underscoring the importance of optimizing AI applications in targeted clinical settings," concluded investigators.

They disclosed having no conflicts of interest.