RESUMO
BACKGROUND: Adenoma detection rate (ADR) varies significantly between endoscopists, with adenoma miss rates (AMRs) up to 26â%. 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 (Pâ=â0.75) in AMR between the AI system (6/197, 3.0â%; 95â% confidence interval [CI] 1.1-6.5) and routine colonoscopy (4/197, 2.0â%; 95â%CI 0.6-5.1). The polyp miss rate of the AI system (14/311, 4.5â%; 95â%CI 2.5-7.4) was not significantly different (Pâ=â0.72) from routine colonoscopy (17/311, 5.5â%; 95â%CI 3.2-8.6). There was no significant difference (Pâ=â0.50) in ADR between routine colonoscopy (78/150, 52.0â%; 95â%CI 43.7-60.2) and the AI system (76/150, 50.7â%; 95â%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 (>â50â%).