Inter-hospital transferability of AI: A case study on phase recognition in cholecystectomy
The results demonstrate that leveraging diverse training data, including institution-specific videos, is crucial to develop robust and transferable AI models for surgical phase recognition, thereby enhancing the potential of automated decision-support systems across different clinical environments.
Authors
- Eric L. Wisotzky (ORCID: https://orcid.org/0000-0001-5731-7058)
- Peter Eisert (ORCID: https://orcid.org/0000-0001-8378-4805)
- Sebastian Lünse (ORCID: https://orcid.org/0000-0002-7611-1802)
- Anna Hilsmann (ORCID: https://orcid.org/0000-0002-2086-0951)
- René Mantke (ORCID: https://orcid.org/0000-0001-5590-6019)
- Lasse Renz-Kiefel
Institutions
- Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute (DE)
- Universitätsklinikum Brandenburg an der Havel (DE)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2025-05-05
- DOI
- https://doi.org/10.1016/j.compbiomed.2025.110235
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00