Real-time computer aided colonoscopy versus standard colonoscopy for improving adenoma detection rate: A meta-analysis of randomized-controlled trials

Abstract

Background: Recent prospective randomized controlled trials have evaluated deep convolutional neural network (CNN) based computer aided detection (CADe) of lesions in real-time colonoscopy. We conducted this meta-analysis to compare the adenoma detection rate (ADR) of deep CNN based CADe assisted colonoscopy to standard colonoscopy (SC) from randomized controlled trials (RCTs). Methods: Multiple databases were searched (from inception to May 2020) and parallel RCTs that compared deep CNN based CADe assisted colonoscopy to SC were included for this analysis. Using Mantel-Haenzel (M-H) random effects model, pooled risk ratios (RR) and mean difference (MD) were calculated. In between study heterogeneity was assessed by I2% values. Outcomes assessed included other per patient adenoma parameters. Findings: Six RCTs were included in our final analysis that utilized deep CNN based CADe system in real-time colonoscopy. Total numbers of patients assessed were 4962 (2480 in CADe and 2482 in SC group). CADe based colonoscopy demonstrated statistically higher pooled ADR, RR=1.5 (95% CI 1.3–1.72), p<0.0001, I2=56%; and pooled PDR, RR=1.42 (95% CI 1.33–1.51), p<0.00001, I2=9%; when compared to SC. Per patient adenoma detection parameters were significantly better with CADe colonoscopy when compared to SC, with increased scope withdrawal time (mean difference = 0.38, 95% CI 0.05–0.72, p = 0.02). Interpretation: Based on our meta-analysis, deep CNN based CADe colonoscopy achieved significantly higher ADR metrics, albeit with increased scope withdrawal time when compared to SC.

Keywords

• Convolutional neural networks
• Colonoscopy
• Adenoma detection rate