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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
PubMed
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.

Keywords:
Artificial intelligenceExternal validationGlenohumeral osteoarthritisMachine learningOutcome predictionReverse shoulder

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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.