Multimodal imaging for detection and segmentation of Barrett’s esophagus-related neoplasia using artificial intelligence

  • The early diagnosis of cancer in Barrett’s esophagus is crucial for improving the prognosis. However, identifying Barrett’s esophagus-related neoplasia (BERN) is challenging, even for experts [1]. Four-quadrant biopsies may improve the detection of neoplasia, but they can be associated with sampling errors. The application of artificial intelligence (AI) to the assessment of Barrett’s esophagus could improve the diagnosis of BERN, and this has been demonstrated in both preclinical and clinical studies [2] [3]. In this video demonstration, we show the accurate detection and delineation of BERN in two patients ([Video 1]). In part 1, the AI system detects a mucosal cancer about 20 mm in size and accurately delineates the lesion in both white-light and narrow-band imaging. In part 2, a small island of BERN with high-grade dysplasia is detected and delineated in white-light, narrow-band, and texture and color enhancement imaging. The video shows the results using a transparent overlay of the mucosal cancer in real time as well as a full segmentation preview. Additionally, the optical flow allows for the assessment of endoscope movement, something which is inversely related to the reliability of the AI prediction. We demonstrate that multimodal imaging can be applied to the AI-assisted detection and segmentation of even small focal lesions in real time.

Export metadata

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:Alanna EbigboORCiD, Robert Mendel, Andreas Probst, Michael Meinikheim, Michael F. Byrne, Helmut Messmann, Christoph PalmORCiDGND
DOI:https://doi.org/10.1055/a-1704-7885
Parent Title (English):Endoscopy
Publisher:Georg Thieme Verlag
Place of publication:Stuttgart
Document Type:Article
Language:English
Year of first Publication:2022
Release Date:2022/01/10
Tag:Artificial Intelligence; Multimodal Imaging; Video
Volume:54
Issue:10
Article Number:E587
Edition:E-Video
Pagenumber:1
Institutes:Fakultät Informatik und Mathematik
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke
6 Technik, Medizin, angewandte Wissenschaften
Begutachtungsstatus:peer-reviewed
research focus:Lebenswissenschaften und Ethik
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG