Deep learning for tibial plateau fracture detection and classification
This study shows that detection of tibial plateau fractures is a task that a DL algorithm can grasp; further refinement is necessary to enhance their accuracy in fracture classification. Computer vision models might improve using different classification systems, as the current Schatzker classification suffers from a low interobserver agreement on conventional radiographs.
Authors
- Erik Hermans
- Frank F. A. IJpma (ORCID: https://orcid.org/0000-0002-9420-2732)
- Job Nicolaas Doornberg (ORCID: https://orcid.org/0000-0002-1270-2742)
- Prachi Bagave (ORCID: https://orcid.org/0009-0007-2805-1327)
- Jacobien H. F. Oosterhoff (ORCID: https://orcid.org/0000-0002-3782-2791)
- Nick Assink (ORCID: https://orcid.org/0000-0002-0034-6816)
- Nynke van der Gaast (ORCID: https://orcid.org/0000-0003-4610-4107)
- Ruurd Lukas Jaarsma (ORCID: https://orcid.org/0000-0003-4308-7815)
- Michael J. Edwards (ORCID: https://orcid.org/0000-0001-8035-8157)
- Alexander Ding (ORCID: https://orcid.org/0000-0001-6490-3573)
- Sebastiaan Broos
Institutions
- University Medical Center Groningen (NL)
- Radboud University Nijmegen (NL)
- University of Groningen (NL)
- Flinders University (AU)
- Flinders Medical Centre (AU)
- Radboud University Medical Center (NL)
- Radboud Institute for Molecular Life Sciences (NL)
- Delft University of Technology (NL)
Publication Details
- Journal
- The Knee
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1016/j.knee.2025.02.001
- Citations
- 11
- Primary Topic
- Bone fractures and treatments
- Type
- article
- Field-Weighted Citation Impact
- 14.90
Funders
- Radboud Universiteit
- Radboud Universitair Medisch Centrum