The use of a real-time computer-aided detection system for visible lesions in the Barrett's esophagus during live endoscopic procedures: a pilot study (with video)

Barrett's Oesophagus Imaging for Artificial Intelligence (BONS-AI) consortium, Tim Boers, Joost A. van der Putten, Koen Kusters, Fons van der Sommen, Peter H.N. de With, Jacques B. Bergman (Corresponding author)

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

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Background and aims: This pilot study evaluated the performance of a recently developed computer-aided detection (CADe) system for Barrett's neoplasia during live endoscopic procedures. Methods: Fifteen patients with a visible lesion and 15 without were included in this study. A CAD-assisted workflow was used that included a slow pullback video recording of the entire Barrett's segment with live CADe assistance, followed by CADe-assisted level-based video recordings every 2 cm of the Barrett's segment. Outcomes were per-patient and per-level diagnostic accuracy of the CAD-assisted workflow, in which the primary outcome was per-patient in vivo CADe sensitivity. Results: In the per-patient analyses, the CADe system detected all visible lesions (sensitivity 100%). Per-patient CADe specificity was 53%. Per-level sensitivity and specificity of the CADe assisted workflow were 100% and 73%, respectively. Conclusions: In this pilot study, detection by the CADe system of all potentially neoplastic lesions in Barrett's esophagus was comparable to that of an expert endoscopist. Continued refinement of the system may improve specificity. External validation in larger multicenter studies is planned. (Clinical trial registration number: NCT05628441.)

Originele taal-2Engels
Pagina's (van-tot)527-531.e3
Aantal pagina's8
TijdschriftGastrointestinal Endoscopy
Volume100
Nummer van het tijdschrift3
Vroegere onlinedatum9 apr. 2024
DOI's
StatusGepubliceerd - sep. 2024

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