Endoscopy 2022; 54(05): 465-472
DOI: 10.1055/a-1556-5984
Original article

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

Carolin Zippelius
1   Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
,
Saleh A. Alqahtani
2   Division of Gastroenterology and Hepatology, Johns Hopkins University, Baltimore, Maryland, United States
,
Jörg Schedel
1   Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
,
Dominic Brookman-Amissah
1   Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
,
Klaus Muehlenberg
1   Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
,
Christoph Federle
1   Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
,
Andrea Salzberger
1   Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
,
Wolfgang Schorr
1   Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
3   Liver Transplant Center, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
,
Oliver Pech
1   Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
› Author Affiliations
Trial Registration: Clinical Trials Register (https://drks-neu.uniklinik-freiburg.de/) Registration number (trial ID): DRKS00022279 Type of study: Prospective cohort study

Abstract

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 43.7–60.2) 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 %).



Publication History

Received: 14 March 2021

Accepted after revision: 22 July 2021

Publication Date:
22 July 2021 (online)

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