Deep Learning-Based Detection of Malignant Bile Duct Stenosis in Fluoroscopy Images of Endoscopic Retrograde Cholangiopancreatography
By using a deep learning model, we were able to discriminate malignant BS from benign biliary conditions. The application of artificial intelligence enhances the diagnostic yield of malignant BS and should be validated in a prospective design.
Authors
- Thomas Karlas (ORCID: https://orcid.org/0000-0002-8109-8526)
- Gregory Patrick Veldhuizen (ORCID: https://orcid.org/0000-0001-7364-8498)
- Patrick Michl (ORCID: https://orcid.org/0000-0002-0401-5600)
- Jakob Garbe (ORCID: https://orcid.org/0000-0001-8043-763X)
- Kien Vu Trung
- Jonas Rosendahl (ORCID: https://orcid.org/0000-0003-4513-0506)
- Sebastian Krug (ORCID: https://orcid.org/0000-0003-1672-7995)
- Albrecht Hoffmeister (ORCID: https://orcid.org/0000-0002-9913-5313)
- Jakob Nikolas Kather (ORCID: https://orcid.org/0000-0002-3730-5348)
- Oliver Lester Saldanha (ORCID: https://orcid.org/0000-0002-3594-7590)
- Jochen Hampe (ORCID: https://orcid.org/0000-0002-2421-6127)
- Jürgen Feisthammel
- Marcus Hollenbach
Institutions
- Philipps University of Marburg (DE)
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- University Hospital Carl Gustav Carus (DE)
- Martin Luther University Halle-Wittenberg (DE)
- Leipzig University (DE)
Publication Details
- Journal
- Digestion
- Published
- 2024-12-13
- DOI
- https://doi.org/10.1159/000543049
- Citations
- 1
- Primary Topic
- Gallbladder and Bile Duct Disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.42
Funders
- Deutscher Akademischer Austauschdienst
- Bundesministerium für Bildung und Forschung
- Bundesministerium für Gesundheit
- Deutsche Krebshilfe