Impact of Artificial Intelligence on Miss Rate of Colorectal Neoplasia
Michael B Wallace, Prateek Sharma, Pradeep Bhandari, James East, Giulio Antonelli, Roberto Lorenzetti, Micheal Vieth, Ilaria Speranza, Marco Spadaccini, Madhav Desai, Frank J Lukens, Genci Babameto, Daisy Batista, Davinder Singh, William Palmer, Francisco Ramirez, Rebecca Palmer, Tisha Lunsford, Kevin Ruff, Elizabeth Bird-Liebermann, Victor Ciofoaia, Sophie Arndtz, David Cangemi, Kirsty Puddick, Gregory Derfus, Amitpal S Johal, Mohammed Barawi, Luigi Longo, Luigi Moro, Alessandro Repici, Cesare Hassan, Michael B Wallace, Prateek Sharma, Pradeep Bhandari, James East, Giulio Antonelli, Roberto Lorenzetti, Micheal Vieth, Ilaria Speranza, Marco Spadaccini, Madhav Desai, Frank J Lukens, Genci Babameto, Daisy Batista, Davinder Singh, William Palmer, Francisco Ramirez, Rebecca Palmer, Tisha Lunsford, Kevin Ruff, Elizabeth Bird-Liebermann, Victor Ciofoaia, Sophie Arndtz, David Cangemi, Kirsty Puddick, Gregory Derfus, Amitpal S Johal, Mohammed Barawi, Luigi Longo, Luigi Moro, Alessandro Repici, Cesare Hassan
Abstract
Background & aims: Artificial intelligence (AI) may detect colorectal polyps that have been missed due to perceptual pitfalls. By reducing such miss rate, AI may increase the detection of colorectal neoplasia leading to a higher degree of colorectal cancer (CRC) prevention.
Methods: Patients undergoing CRC screening or surveillance were enrolled in 8 centers (Italy, UK, US), and randomized (1:1) to undergo 2 same-day, back-to-back colonoscopies with or without AI (deep learning computer aided diagnosis endoscopy) in 2 different arms, namely AI followed by colonoscopy without AI or vice-versa. Adenoma miss rate (AMR) was calculated as the number of histologically verified lesions detected at second colonoscopy divided by the total number of lesions detected at first and second colonoscopy. Mean number of lesions detected in the second colonoscopy and proportion of false negative subjects (no lesion at first colonoscopy and at least 1 at second) were calculated. Odds ratios (ORs) and 95% confidence intervals (CIs) were adjusted by endoscopist, age, sex, and indication for colonoscopy. Adverse events were also measured.
Results: A total of 230 subjects (116 AI first, 114 standard colonoscopy first) were included in the study analysis. AMR was 15.5% (38 of 246) and 32.4% (80 of 247) in the arm with AI and non-AI colonoscopy first, respectively (adjusted OR, 0.38; 95% CI, 0.23-0.62). In detail, AMR was lower for AI first for the ≤5 mm (15.9% vs 35.8%; OR, 0.34; 95% CI, 0.21-0.55) and nonpolypoid lesions (16.8% vs 45.8%; OR, 0.24; 95% CI, 0.13-0.43), and it was lower both in the proximal (18.3% vs 32.5%; OR, 0.46; 95% CI, 0.26-0.78) and distal colon (10.8% vs 32.1%; OR, 0.25; 95% CI, 0.11-0.57). Mean number of adenomas at second colonoscopy was lower in the AI-first group as compared with non-AI colonoscopy first (0.33 ± 0.63 vs 0.70 ± 0.97, P < .001). False negative rates were 6.8% (3 of 44 patients) and 29.6% (13 of 44) in the AI and non-AI first arms, respectively (OR, 0.17; 95% CI, 0.05-0.67). No difference in the rate of adverse events was found between the 2 groups.
Conclusions: AI resulted in an approximately 2-fold reduction in miss rate of colorectal neoplasia, supporting AI-benefit in reducing perceptual errors for small and subtle lesions at standard colonoscopy.
Clinicaltrials: gov, Number: NCT03954548.
Keywords: Adenoma Miss Rate; Artificial Intelligence; Colorectal Cancer; Miss Rate; Tandem Colonoscopy.
Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.
Source: PubMed