The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: A systematic review with meta-analysis

Background and aim: The utility of artificial intelligence (AI) in colonoscopy has gained popularity in current times. Recent trials have evaluated the efficacy of deep convolutional neural network (DCNN)-based AI system in colonoscopy for improving adenoma detection rate (ADR) and polyp detection rate (PDR). We performed a systematic review and meta-analysis of the available studies to assess the impact of DCNN-based AI-assisted colonoscopy in improving the ADR and PDR.

Methods: We queried the following database for this study: PubMed, Embase, Cochrane Library, Web of Sciences, and Computers and Applied Sciences. We only included randomized controlled trials that compared AI colonoscopy to standard colonoscopy (SC). Our outcomes included ADR and PDR. Risk ratios (RR) with 95% confidence interval (CI) were calculated using random effects model and DerSimonian-Laird approach for each outcome.

Results: A total of three studies with 2815 patients (1415 in SC group and 1400 in AI group) were included. AI colonoscopy resulted in significantly improved ADR (32.9% vs 20.8%, RR: 1.58, 95% CI 1.39-1.80, P = < 0.001) and PDR (43.0% vs 27.8%, RR: 1.55, 95% CI 1.39-1.72, P = < 0.001) compared with SC.

Conclusion: Given the results and limitations, the utility of AI colonoscopy holds promise and should be evaluated in more randomized controlled trials across different population, especially in patients solely undergoing colonoscopy for screening purpose as improved ADR will ultimately help in reducing incident colorectal cancer.