Real-time use of artificial intelligence in the evaluation of cancer in Barrett’s oesophagus

  • Based on previous work by our group with manual annotation of visible Barrett oesophagus (BE) cancer images, a real-time deep learning artificial intelligence (AI) system was developed. While an expert endoscopist conducts the endoscopic assessment of BE, our AI system captures random images from the real-time camera livestream and provides a global prediction (classification), as well as a dense prediction (segmentation) differentiating accurately between normal BE and early oesophageal adenocarcinoma (EAC). The AI system showed an accuracy of 89.9% on 14 cases with neoplastic BE.

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Metadaten
Author:Alanna EbigboORCiD, Robert Mendel, Andreas Probst, Johannes Manzeneder, Friederike Prinz, Luis Antonio de SouzaORCiD, João P. PapaORCiD, Christoph PalmORCiDGND, Helmut Messmann
DOI:https://doi.org/10.1136/gutjnl-2019-319460
Parent Title (English):Gut
Publisher:BMJ
Place of publication:London
Document Type:Article
Language:English
Year of first Publication:2020
Release Date:2020/04/30
Tag:Barrett's esophagus; Deep learning; real-time
GND Keyword:Speiseröhrenkrankheit; Diagnose; Maschinelles Lernen
Volume:69
First Page:615
Last Page:616
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Health Sciences and Technology - RCHST
Regensburg Medical Image Computing - ReMIC
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke
6 Technik, Medizin, angewandte Wissenschaften
OpenAccess Publikationsweg:Erstveröffentlichungen