Diagnostic accuracy of a novel artificial intelligence system for adenoma detection in daily practice: a prospective nonrandomized comparative study.

Background: Adenoma detection rate (ADR) varies significantly between endoscopists, with adenoma miss rates (AMRs) up to 26 %. Artificial intelligence (AI) systems may improve endoscopy quality and reduce the rate of interval cancer. We evaluated the efficacy of an AI system in real-time colonoscopy and its influence on AMR and ADR.

Methods: This prospective, nonrandomized, comparative study analyzed patients undergoing diagnostic colonoscopy at a single endoscopy center in Germany from June to October 2020. Every patient was examined concurrently by an endoscopist and AI using two opposing screens. The AI system, overseen by a second observer, was not visible to the endoscopist. AMR was the primary outcome. Both methods were compared using McNemar test.

Results: 150 patients were included (mean age 65 years [standard deviation 14]; 69 women). There was no significant or clinically relevant difference ( = 0.75) in AMR between the AI system (6/197, 3.0 %; 95 % confidence interval [CI] 1.1-6.5) and routine colonoscopy (4/197, 2.0 %; 95 %CI 0.6-5.1). The polyp miss rate of the AI system (14/311, 4.5 %; 95 %CI 2.5-7.4) was not significantly different ( = 0.72) from routine colonoscopy (17/311, 5.5 %; 95 %CI 3.2-8.6). There was no significant difference ( = 0.50) in ADR between routine colonoscopy (78/150, 52.0 %; 95 %CI 43.7-60.2) and the AI system (76/150, 50.7 %; 95 %CI 42.4-58.9). Routine colonoscopy detected adenomas in two patients that were missed by the AI system.

Conclusion: The AI system performance was comparable to that of experienced endoscopists during real-time colonoscopy with similar high ADR (> 50 %).