Machine learning models for predicting tibial intramedullary nail length
Combining anthropometric measurements with ML models, particularly linear regression, effectively predicts IMN length. This approach can streamline preoperative planning by reducing intraoperative measurements and minimizing surgery time and radiation exposure. Further validation with larger datasets is necessary to confirm these findings across diverse populations.
Authors
- Hakan Cici (ORCID: https://orcid.org/0000-0002-9511-4865)
- Mehmet Akdemir (ORCID: https://orcid.org/0000-0001-9638-4907)
- Mert Kahraman Maraşlı (ORCID: https://orcid.org/0000-0002-1894-3731)
- Sercan Çapkın (ORCID: https://orcid.org/0000-0001-6957-5927)
- Ali İhsan Kılıç (ORCID: https://orcid.org/0000-0001-7491-6044)
Institutions
- Gebze Technical University (TR)
- Izmir University (TR)
- Sivas State Hospital (TR)
Publication Details
- Journal
- BMC Musculoskeletal Disorders
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1186/s12891-025-08657-1
- Citations
- 1
- Primary Topic
- Bone fractures and treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.35