Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma

  • 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.

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Author:Alanna EbigboORCiD, Robert MendelORCiD, Andreas Probst, Johannes Manzeneder, Luis Antonio De Souza Jr.ORCiD, João Paulo PapaORCiD, Christoph PalmORCiDGND, Helmut Messmann
Parent Title (English):GuT
Publisher:British Society of Gastroenterology
Document Type:Article
Year of first Publication:2019
Publishing Institution:Ostbayerische Technische Hochschule Regensburg
Release Date:2019/10/07
GND Keyword:Speiseröhrenkrebs; Diagnose; Computerunterstütztes Verfahren; Maschinelles Lernen
First Page:1143
Last Page:1145
Corresponding authors: Alanna Ebigbo and Christoph Palm
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Health Sciences and Technology - RCHST
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
research focus:Lebenswissenschaften und Ethik
OpenAccess Publikationsweg:Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium
Corresponding author der OTH Regensburg
Licence (German):Creative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International