Artificial Intelligence-Aided Colonoscopy for Polyp Detection: A Systematic Review and Meta-Analysis of Randomized Clinical Trials

Background: This study aimed to compare artificial intelligence (AI)-aided colonoscopy with conventional colonoscopy for polyp detection. Methods: A systematic literature search was performed in PubMed and Ovid for randomized clinical trials (RCTs) comparing AI-aided colonoscopy with conventional colonoscopy for polyp detection. The last search was performed on July 22, 2020. The primary outcome was polyp detection rate (PDR) and adenoma detection rate (ADR). Results: Seven RCTs published between 2019 and 2020 with a total of 5427 individuals were included. When compared with conventional colonoscopy, AI-aided colonoscopy significantly improved PDR (P < .001, odds ratio [OR] = 1.95, 95% confidence interval [CI]: 1.75 to 2.19, I2 = 0%) and ADR (P < .001, OR = 1.72, 95% CI: 1.52 to 1.95, I2 = 33%). Besides, polyps in the AI-aided group were significantly smaller in size than those in conventional group (P = .004, weighted mean difference = -0.48, 95% CI: -0.81 to -0.15, I2 = 0%). In addition, AI-aided group detected significantly less proportion of advanced adenoma (P = .03, OR = 0.70, 95% CI: 0.50 to 0.97, I2 = 46%), pedicle polyps (P < .001, OR = 0.64, 95% CI: 0.49 to 0.83, I2 = 0%), and pedicle adenomas (P < .001, OR = 0.60, 95% CI: 0.44 to 0.80, I2 = 0%). Conclusion: AI-aided colonoscopy could significantly increase the PDR and ADR, especially for those with small size. Besides, the shape and pathology recognition of the AI technique should be further improved in the future.