Machine learning to predict the occurrence of complications after total shoulder arthroplasty for B2-B3 glenoids
ML can efficiently predict TSA complications, even with a limited dataset. Glenoid retroversion was identified as a critical radiological feature associated with outcomes, as supported by the literature. In addition, younger age is associated with increased complication risks, likely due to increased functional demand. Thus, ML is potentially a valuable tool for forecasting complications in the surgical decision-making process.
Authors
- Alexandre Terrier (ORCID: https://orcid.org/0000-0002-1910-0701)
- Pezhman Eghbali (ORCID: https://orcid.org/0000-0002-2957-2569)
- Fabio Becce (ORCID: https://orcid.org/0000-0001-8444-8504)
- Patrick Goetti (ORCID: https://orcid.org/0000-0001-7243-2118)
- Alain Farron
- Orlando Parmigiani
Institutions
- Centre Hospitalier Universitaire Vaudois (CH)
- École Polytechnique Fédérale de Lausanne (CH)
Publication Details
- Journal
- Frontiers in Surgery
- Published
- 2025-10-10
- DOI
- https://doi.org/10.3389/fsurg.2025.1637419
- Citations
- 1
- Primary Topic
- Shoulder Injury and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.01
Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung