Development of a Deep Learning Model for Automating Implant Position in Total Hip Arthroplasty
We have developed a novel deep-learning model that can automate the identification of clinically relevant pelvic landmarks in real time and provide THA-relevant measurements that are equivalent to those of trained humans. We believe the model could be rapidly incorporated into clinical practice for both surgical and research applications.
Authors
- J Ryan Martin (ORCID: https://orcid.org/0000-0002-4125-2154)
- Peter Y. W. Chan (ORCID: https://orcid.org/0000-0002-3498-6301)
- Daniel Moyer (ORCID: https://orcid.org/0000-0003-4428-5012)
- Yehyun Suh
- Courtney E. Baker (ORCID: https://orcid.org/0000-0002-4088-2947)
Institutions
- Vanderbilt University (US)
- Vanderbilt Health (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- The Journal of Arthroplasty
- Published
- 2025-01-27
- DOI
- https://doi.org/10.1016/j.arth.2025.01.032
- Citations
- 1
- Primary Topic
- Orthopaedic implants and arthroplasty
- Type
- article
- Field-Weighted Citation Impact
- 2.01