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.
Author: | Alanna EbigboORCiD, Robert Mendel, Andreas Probst, Johannes Manzeneder, Friederike Prinz, Luis Antonio De Souza Jr.ORCiD, João Paulo PapaORCiD, Christoph PalmORCiDGND, Helmut Messmann |
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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 |
Issue: | 4 |
First Page: | 615 |
Last Page: | 616 |
Institutes: | Fakultät Informatik und Mathematik |
Regensburg Center of Health Sciences and Technology - RCHST | |
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC) | |
Begutachtungsstatus: | peer-reviewed |
OpenAccess Publikationsweg: | Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium |
research focus: | Lebenswissenschaften und Ethik |
Licence (German): | ![]() |