Real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists

Giuseppe Biscaglia; Francesco Cocomazzi; Marco Gentile; Ilaria Loconte; Alessia Mileti; Rosa Paolillo; Antonella Marra; Stefano Castellana; Tommaso Mazza; Alfredo Di Leo; Francesco Perri

doi:10.1055/a-1783-9678

Real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists

Endosc Int Open. 2022 May 13;10(5):E616-E621. doi: 10.1055/a-1783-9678. eCollection 2022 May.

Affiliations

¹ Division of Gastroenterology and Endoscopy, "Casa Sollievo della Sofferenza" Hospital, IRCCS, San Giovanni Rotondo, Italy.
² Section of Gastroenterology, Department of Emergency and Organ Transplantation, University of Bari, Italy.
³ Laboratory of Bioinformatics, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.

Abstract

Background and study aims Adenoma detection rate (ADR) is a well-accepted quality indicator of screening colonoscopy. In recent years, the added value of artificial intelligence (AI) has been demonstrated in terms of ADR and adenoma miss rate (AMR). To date, there are no studies evaluating the impact of AI on the performance of trainee endoscopists (TEs). This study aimed to assess whether AI might eliminate any difference in ADR or AMR between TEs and experienced endoscopists (EEs). Patients and methods We performed a prospective observational study in 45 subjects referred for screening colonoscopy. A same-day tandem examination was carried out for each patient by a TE with the AI assistance and subsequently by an EE unaware of the lesions detected by the TE. Besides ADR and AMR, we also calculated for each subgroup of endoscopists the adenoma per colonoscopy (APC), polyp detection rate (PDR), polyp per colonoscopy (PPC) and polyp miss rate (PMR). Subgroup analyses according to size, morphology, and site were also performed. Results ADR, APC, PDR, and PPC of AI-supported TEs were 38 %, 0.93, 62 %, 1.93, respectively. The corresponding parameters for EEs were 40 %, 1.07, 58 %, 2.22. No significant difference was found for each analysis between the two groups ( P > 0.05). AMR and PMR for AI-assisted TEs were 12.5 % and 13 %, respectively. Sub-analyses did not show any significant difference ( P > 0.05) between the two categories of operators. Conclusions In this single-center prospective study, the possible impact of AI on endoscopist quality training was demonstrated. In the future, this could result in better efficacy of screening colonoscopy by reducing the incidence of interval or missed cancers.

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