Real-World Assessment of the Efficacy of Computer-Assisted Diagnosis in Colonoscopy: A Single Institution Cohort Study in Singapore

• Published in: Mayo Clinic Proceedings. Digital health 2024-12, Vol.2 (4), p.647-655
• Main Authors: Koh, Gabrielle E., Ng, Brittany, Lagström, Ronja M.B., Foo, Fung-Joon, Chin, Shuen-Ern, Wan, Fang-Ting, Kam, Juinn Huar, Yeung, Baldwin, Kwan, Clarence, Hassan, Cesare, Gögenur, Ismail, Koh, Frederick H.
• Format: Article
• Language: English
• ISSN: 2949-7612; 2949-7612
• DOI: 10.1016/j.mcpdig.2024.10.002

To review the efficacy and accuracy of the GI Genius Intelligent Endoscopy Module Computer-Assisted Diagnosis (CADx) program in colonic adenoma detection and real-time polyp characterization. Colonoscopy remains the gold standard in colonic screening and evaluation. The incorporation of artificial intelligence (AI) technology therefore allows for optimized endoscopic performance. However, validation of most CADx programs with real-world data remains scarce. This prospective cohort study was conducted within a single Singaporean institution between April 1, 2023 and December 31, 2023. Videos of all AI-enabled colonoscopies were reviewed with polyp-by-polyp analysis performed. Real-time polyp characterization predictions after sustained polyp detection were compared against final histology results to assess the accuracy of the CADx system at colonic adenoma identification. A total of 808 videos of CADx colonoscopies were reviewed. Out of the 781 polypectomies performed, 543 (69.5%) and 222 (28.4%) were adenomas and non-adenomas on final histology, respectively. Overall, GI Genius correctly characterized adenomas with 89.4% sensitivity, 61.7% specificity, a positive predictive value of 85.4%, a negative predictive value of 69.8%, and 81.5% accuracy. The negative predictive value for rectosigmoid lesions (80.3%) was notably higher than for colonic lesions (54.2%), attributed to the increased prevalence of hyperplastic rectosigmoid polyps (11.4%) vs other colonic regions (5.4%). Computer-Assisted Diagnosis is therefore a promising adjunct in colonoscopy with substantial clinical implications. Accurate identification of low-risk non-adenomatous polyps encourages the adoption of “resect-and-discard” strategies. However, further calibration of AI systems is needed before the acceptance of such strategies as the new standard of care.