Automation in tibial implant loosening detection using deep-learning segmentation
The conclusion is that a full automation in knee implant displacement assessment is feasible by utilizing a DL-based segmentation model while maintaining the capability of distinguishing between fixed and loose implants.
Authors
- George S. Buijs (ORCID: https://orcid.org/0000-0003-3550-3380)
- Leendert Blankevoort (ORCID: https://orcid.org/0000-0002-7810-1659)
- Arthur J. Kievit (ORCID: https://orcid.org/0000-0003-1166-5115)
- Matthias U. Schafroth (ORCID: https://orcid.org/0000-0002-9969-0480)
- Geert J. Streekstra (ORCID: https://orcid.org/0000-0002-5590-7633)
- Johannes G. G. Dobbe (ORCID: https://orcid.org/0000-0002-9454-6471)
- Maaike A Ter Wee
- Caroline Magg (ORCID: https://orcid.org/0009-0008-5592-2586)
- Clara I. Sánchez
Institutions
- Amsterdam Neuroscience (NL)
- GGD Amsterdam (NL)
- Amsterdam University Medical Centers (NL)
- Health & Safety in Sports (NL)
- Amsterdam University of Applied Sciences (NL)
- University of Amsterdam (NL)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2025-06-27
- DOI
- https://doi.org/10.1007/s11548-025-03459-1
- Citations
- 1
- Primary Topic
- Total Knee Arthroplasty Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.01
Funders
- Health~Holland
- Amsterdam University Medical Centers
- Universiteit van Amsterdam