Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty
- DOI
- 10.1016/j.jse.2024.07.006
- Published
- 2025-03
- Container
- Journal of Shoulder and Elbow Surgery
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jse.2024.07.006,
title = {Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty},
author = {Daniel Ritter and Patrick J. Denard and Patric Raiss and Coen A. Wijdicks and Brian C. Werner and Asheesh Bedi and Peter E. Müller and Samuel Bachmaier},
year = {2025},
journal = {Journal of Shoulder and Elbow Surgery},
doi = {10.1016/j.jse.2024.07.006},
url = {https://doi.org/10.1016/j.jse.2024.07.006}
}RIS
TY - JOUR TI - Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty AU - Daniel Ritter AU - Patrick J. Denard AU - Patric Raiss AU - Coen A. Wijdicks AU - Brian C. Werner AU - Asheesh Bedi AU - Peter E. Müller AU - Samuel Bachmaier PY - 2025 JO - Journal of Shoulder and Elbow Surgery DO - 10.1016/j.jse.2024.07.006 UR - https://doi.org/10.1016/j.jse.2024.07.006 ER -
APA
Ritter, D., Denard, P. J., Raiss, P., Wijdicks, C. A., Werner, B. C., Bedi, A., Müller, P. E., & Bachmaier, S. (2025). Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty. Journal of Shoulder and Elbow Surgery. https://doi.org/10.1016/j.jse.2024.07.006
Source records
- crossref · retrieved 2026-09-26T14:27:36.871Z