ArticleMusculoskeletal surgery2024
Machine learning can predict anterior elevation after reverse total shoulder arthroplasty: A new tool for daily outpatient clinic?
Article in Musculoskeletal surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Artificial intelligence and machine learning: an important new set of tools for clinical shoulder arthroplasty research.JSES international · 2026Review
- Optimizing arthroplasty outcomes: the impact of artificial intelligence and robotic assistance.Annals of medicine and surgery (2012) · 2026Review
- Review
- Preoperative Radiographic Thoracic Kyphosis Relates to Scapular Internal Rotation but Not Anterior Tilt in Candidates for Reverse Shoulder Arthroplasty: A Retrospective Radiographic Analysis from the FP-UCBM Shoulder Study Group.Journal of clinical medicine · 2025Article
- Diagnostic accuracy of the alpha-defensin lateral flow test in periprosthetic shoulder infection: a retrospective study.Clinics in shoulder and elbow · 2025Article
- Assessing the accuracy of a machine learning prediction for 2 different shoulder prostheses: an external validation study.JSES international · 2025Article
- An overview of artificial intelligence and machine learning in shoulder surgery.Clinics in shoulder and elbow · 2025Article
- Using deep learning to predict postoperative pain in reverse shoulder arthroplasty patients.JSES international · 2025Article
- Computer-Assisted Navigation in Shoulder Arthroplasty: A Narrative Review.Journal of clinical medicine · 2025Review
- Comparison of Machine Learning Algorithms and Hybrid Computational Intelligence Algorithms for Rehabilitation Classification and Prognosis in Reverse Total Shoulder Arthroplasty.Bioengineering (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the present study was to individuate and compare specific machine learning algorithms that could predict postoperative anterior elevation score after reverse shoulder arthroplasty surgery at different time points. Data from 105 patients who underwent reverse shoulder arthroplasty at the same institute have been collected with the purpose of generating algorithms which could predict the target. Twenty-eight features were extracted and applied to two different machine learning techniques: Linear regression and support vector regression (SVR). These two techniques were also compared in order to define to most faithfully predictive. Using the extracted features, the SVR algorithm resulted in a mean absolute error (MAE) of 11.6° and a classification accuracy (PCC) of 0.88 on the test-set. Linear regression, instead, resulted in a MAE of 13.0° and a PCC of 0.85 on the test-set. Our machine learning study demonstrates that machine learning could provide high predictive algorithms for anterior elevation after reverse shoulder arthroplasty. The differential analysis between the utilized techniques showed higher accuracy in prediction for the support vector regression. Level of Evidence III: Retrospective cohort comparison; Computer Modeling.
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Registered trials
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