A17 COMPARING ENDOSCOPIST DIAGNOSIS OF COLORECTAL POLYPS ASSISTED BY ARTIFICIAL INTELLIGENCE (CADX) VS CADX WITHOUT ENDOSCOPIST INPUT: A RANDOMIZED CONTROLLED TRIAL
R Djinbachian, C Haumesser, M Taghiakbari, A Alj, A Barkun, J Liu, B Panzini, S Sidani, D von Renteln

TL;DR
The study compares AI-assisted colonoscopy diagnosis with and without endoscopist input, finding similar accuracy in both approaches.
Contribution
This is the first randomized trial comparing AI-only diagnosis with endoscopist-assisted AI diagnosis for colorectal polyps.
Findings
CADx without human input had 76.3% accuracy, similar to 70.5% in the endoscopist-assisted group.
Sensitivity and specificity for adenoma detection were comparable between the two groups.
Results suggest AI alone could support resect-and-discard strategies without endoscopist involvement.
Abstract
Artificial intelligence-based optical diagnosis systems (CADx) have been developped to assist in eliminating the need for histologic diagnosis of diminutive colorectal polyps (resect-and-discard and diagnose-and-leave strategies). However, these systems have not yet been implemented in routine clinical practice. We were interested in evaluating the performance of CADx without human input to diagnoses performed by endoscopists assisted by CADx. We performed a randomized clinical trial of patients undergoing elective colonoscopies at the CHUM. Patients were randomized into two arms: 1) optical diagnosis of colorectal polyps using CADx without human input; 2) endoscopists performed optical diagnosis after consulting a real-time CADx diagnosis (Human in the Loop [HiL]). Primary outcome was accuracy in optical diagnosis for both arms. 467 patients were randomized (229 in the CADx group,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsColorectal Cancer Screening and Detection
