| Title: |
Machine learning in shoulder arthroplasty: a systematic review of predictive analytics applications |
| Authors: |
Tim Schneller; Moritz Kraus; Jan Schätz; Philipp Moroder; Markus Scheibel; Asimina Lazaridou |
| Source: |
Bone & Joint Open, Vol 6, Iss 2, Pp 126-134 (2025) |
| Publisher Information: |
The British Editorial Society of Bone & Joint Surgery, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Orthopedic surgery |
| Subject Terms: |
machine learning; systematic review; total shoulder arthroplasty; clinical outcomes; image analysis; predictive analytics; shoulder arthroplasty; rtsa; shoulder surgeries; imaging studies; clinicians; shoulder prosthesis; orthopaedic surgery; augmentation; Orthopedic surgery; RD701-811 |
| Description: |
Aims: Machine learning (ML) holds significant promise in optimizing various aspects of total shoulder arthroplasty (TSA), potentially improving patient outcomes and enhancing surgical decision-making. The aim of this systematic review was to identify ML algorithms and evaluate their effectiveness, including those for predicting clinical outcomes and those used in image analysis. Methods: We searched the PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases for studies applying ML algorithms in TSA. The analysis focused on dataset characteristics, relevant subspecialties, specific ML algorithms used, and their performance outcomes. Results: Following the final screening process, 25 articles satisfied the eligibility criteria for our review. Of these, 60% focused on tabular data while the remaining 40% analyzed image data. Among them, 16 studies were dedicated to developing new models and nine used transfer learning to leverage existing pretrained models. Additionally, three of these models underwent external validation to confirm their reliability and effectiveness. Conclusion: ML algorithms used in TSA demonstrated fair to good performance, as evidenced by the reported metrics. Integrating these models into daily clinical practice could revolutionize TSA, enhancing both surgical precision and patient outcome predictions. Despite their potential, the lack of transparency and generalizability in many current models poses a significant challenge, limiting their clinical utility. Future research should prioritize addressing these limitations to truly propel the field forward and maximize the benefits of ML in enhancing patient care. Cite this article: Bone Jt Open 2025;6(2):126–134. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2633-1462 |
| Relation: |
https://doaj.org/toc/2633-1462 |
| DOI: |
10.1302/2633-1462.62.BJO-2024-0234.R1 |
| Access URL: |
https://doaj.org/article/2ff0924aa73f4dae829bebbb2d31e87d |
| Accession Number: |
edsdoj.2ff0924aa73f4dae829bebbb2d31e87d |
| Database: |
Directory of Open Access Journals |