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

Babu P Mohan; Antonio Facciorusso; Shahab R Khan; Saurabh Chandan; Lena L Kassab; Paraskevas Gkolfakis; Georgios Tziatzios; Konstantinos Triantafyllou; Douglas G Adler

doi:10.1016/j.eclinm.2020.100622

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

EClinicalMedicine. 2020 Nov 21:29-30:100622. doi: 10.1016/j.eclinm.2020.100622. eCollection 2020 Dec.

Authors

Babu P Mohan^{1

2}, Antonio Facciorusso^{3

2}, Shahab R Khan^{4

2}, Saurabh Chandan^{5

2}, Lena L Kassab^{6

2}, Paraskevas Gkolfakis^{7

2}, Georgios Tziatzios^{7

2}, Konstantinos Triantafyllou^{7

2}, Douglas G Adler^{1

2}

Affiliations

¹ Gastroenterology and Hepatology, University of Utah Health, Salt Lake City, UT, USA.
² Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA.
³ Gastroenterology Unit, University of Foggia, Foggia, Italy.
⁴ Gastroenterology, Rush University Medical Center, Chicago, IL, USA.
⁵ Gastroenterology and Hepatology, CHI Creighton University Medical Center, Omaha, NE, USA.
⁶ Internal Medicine, Mayo Clinic, Rochester, MIN, USA.
⁷ Hepatogastroenterology Unit, 2nd Dep of Internal Medicine - Propaedeutic Medical School, National and Kapodistrian University of Athens, Athens, Greece.

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: Adenoma detection rate; Colonoscopy; Convolutional neural networks.