Autonomous Artificial Intelligence vs Artificial Intelligence–Assisted Human Optical Diagnosis of Colorectal Polyps: A Randomized Controlled Trial

Abstract

BackgroundArtificial intelligence (AI)-based optical diagnosis systems (CADx) have been developed to allow pathology prediction of colorectal polyps during colonoscopies. However, CADx systems have not yet been validated for autonomous performance. Therefore, we conducted a trial comparing Autonomous AI to AI assisted human (AI-H) optical diagnosis. MethodsWe performed a randomized non-inferiority trial of patients undergoing elective colonoscopies in one academic institution. Patients were randomized int: 1) Autonomous AI-based CADx optical diagnosis of diminutive polyps without human input; 2) endoscopists performed optical diagnosis of diminutive polyps after seeing the real-time CADx diagnosis. Primary outcome was accuracy in optical diagnosis in both arms using pathology as gold standard. Secondary outcomes included agreement with pathology for surveillance intervals. Results467 patients were randomized (238 patients/158 polyps in the Autonomous AI group; 229 patients/179 polyps in the AI-H group). Accuracy for optical diagnosis was 77.2% (95%Confidence Interval 69.7-84.7) in the Autonomous AI group and 72.1% (95%CI 65.5-78.6) in the AI-H group (p=0.86). For high confidence diagnoses, accuracy for optical diagnosis was 77.2% (95%CI 69.7-84.7) in the Autonomous AI group and 75.5% (95%CI 67.9-82.0) in the AI-H group. Autonomous AI had statistically significantly higher agreement with pathology-based surveillance intervals compared to AI-H (91.5% [95%CI 86.9-96.1] vs 82.1% [95%CI 76.5-87.7]; p=0.016). ConclusionsAutonomous AI-based optical diagnosis exhibits non-inferior accuracy to endoscopist-based diagnosis. Both Autonomous AI and AI-H exhibited relatively low accuracy for optical diagnosis, however Autonomous AI achieved higher agreement with pathology-based surveillance intervals.