Diagnostic accuracy of a novel artificial intelligence system for adenoma detection in daily practice: a prospective non-randomized 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 (P = 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 (P=0.72) from routine colonoscopy (17/311, 5.5%; 95%CI 3.2-8.6). There was no significant difference (P=0.50) in ADR between routine colonoscopy (78/150, 52.0%; 95%CI 43.760.2) and the AI system (76/150, 50.7%; 95%Cl 42.458.9). 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%).