Clinical validation of a deep learning-based approach for preoperative decision-making in implant size for total knee arthroplasty
The observed differences and higher agreement levels achieved by the proposed DL model demonstrate its potential as a valuable tool in preoperative decision-making for TKA. By providing accurate predictions of implant size, the proposed DL model has the potential to optimize implant selection, leading to improved surgical outcomes.
Authors
- Young Dae Jeon (ORCID: https://orcid.org/0000-0003-4862-9679)
- Do-Kun Yoon (ORCID: https://orcid.org/0000-0002-0840-235X)
- Ki‐Bong Park (ORCID: https://orcid.org/0000-0002-2978-8300)
- Moo-Sub Kim (ORCID: https://orcid.org/0000-0002-8202-617X)
Institutions
- Ulsan College (KR)
- Yonsei University (KR)
- University of Ulsan (KR)
- Ulsan University Hospital (KR)
Publication Details
- Journal
- Journal of Orthopaedic Surgery and Research
- Published
- 2024-10-08
- DOI
- https://doi.org/10.1186/s13018-024-05128-6
- Citations
- 8
- Primary Topic
- Total Knee Arthroplasty Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 4.58
Funders
- National Research Foundation
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea
- Ulsan University Hospital