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Assessing the accuracy of a machine learning prediction for 2 different shoulder prostheses: an external validation
Gianluca Caprili1, Andrea G Calamita1, Michele Novi1
1Prosthetic Orthopedics, San Pietro Igneo Hospital, Fucecchio, FI, Italy.
JSES International
|September 17, 2025
Summary
This study validates a machine learning tool for predicting outcomes after reverse total shoulder arthroplasty. The tool showed accurate predictions across different implant types, aiding patient expectation management.
Area of Science:
- Orthopedic Surgery
- Machine Learning
- Biomedical Engineering
Background:
- Machine learning integration in orthopedic surgery, particularly shoulder procedures, is a growing area of interest.
- This study focuses on reverse total shoulder arthroplasty (RTSA) for glenohumeral osteoarthritis.
Purpose of the Study:
- To externally validate a predictive analytics platform for RTSA outcomes.
- To assess the platform's generalizability across different implant types.
Main Methods:
- Retrospective analysis of 90 patients undergoing RTSA.
- Comparison of the predictive tool's outcome predictions with actual postoperative results (Visual Analog Scale, range of motion) at 3-6 months, 1 year, and 2 years.
- Calculation and comparison of Mean Absolute Error (MAE) against internal validation data.
Main Results:
- Significant improvements in pain and range of motion were observed post-surgery in both implant groups.
- The predictive tool demonstrated lower or similar MAE compared to internal validation for most outcomes.
- The tool's predictions were generalizable to an implant type not included in its training data.
Conclusions:
- The validated predictive analytics platform is reliable for RTSA outcomes.
- The tool's generalizability suggests its potential utility across various shoulder prostheses.
- This technology can assist clinicians in managing patient expectations effectively.
