TY - JOUR A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Probst, Andreas A1 - Manzeneder, Johannes A1 - De Souza Jr., Luis Antonio A1 - Papa, João Paulo A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma T2 - GuT N2 - Computer-aided diagnosis using deep learning (CAD-DL) may be an instrument to improve endoscopic assessment of Barrett’s oesophagus (BE) and early oesophageal adenocarcinoma (EAC). Based on still images from two databases, the diagnosis of EAC by CAD-DL reached sensitivities/specificities of 97%/88% (Augsburg data) and 92%/100% (Medical Image Computing and Computer-Assisted Intervention [MICCAI] data) for white light (WL) images and 94%/80% for narrow band images (NBI) (Augsburg data), respectively. Tumour margins delineated by experts into images were detected satisfactorily with a Dice coefficient (D) of 0.72. This could be a first step towards CAD-DL for BE assessment. If developed further, it could become a useful adjunctive tool for patient management. KW - Speiseröhrenkrebs KW - Diagnose KW - Computerunterstütztes Verfahren KW - Maschinelles Lernen Y1 - 2018 UR - https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/6 UR - https://nbn-resolving.org/urn:nbn:de:bvb:898-opus4-68 N1 - Corresponding authors: Alanna Ebigbo and Christoph Palm VL - 68 IS - 7 SP - 1143 EP - 1145 PB - British Society of Gastroenterology ER -