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Rat Model of Adhesive Capsulitis of the Shoulder

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ShRed: a machine learning model developed to predict shoulder redislocation.

Mohamed E Mahmoud¹, Rajapriyian Murugaiyan², Rahul Geetala²

¹Department of Orthopaedics, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK. mohamed.mahmoud19@nhs.net.

European Journal of Orthopaedic Surgery & Traumatology : Orthopedie Traumatologie

|July 13, 2026

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Machine learning models show moderate accuracy in predicting shoulder redislocation using imaging and demographic data. Years since first dislocation and glenoid cartilage thickness are key predictors.

Area of Science:

Orthopedics
Medical Imaging
Machine Learning

Background:

Shoulder dislocation is a common injury.
Predicting redislocation is crucial for patient management.
Current prediction methods often lack precision.

Purpose of the Study:

To develop and assess machine learning (ML) models for predicting shoulder redislocation.
To incorporate joint-specific imaging characteristics alongside demographic variables.
To evaluate the feasibility of ML in this predictive task.

Main Methods:

A prospective dataset of 42 patients was analyzed.
Six classification algorithms were compared using 10-fold cross-validation.
Cartilage MRI thickness measurements and demographic data were utilized.

Keywords:

MRI Machine learning Predictive model Random Forest Recurrent dislocation Shoulder instability

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Main Results:

Random Forest model achieved 79.0% cross-validated accuracy.
Key predictors included years since first dislocation, age at first dislocation, and glenoid cartilage thickness.
The model showed an AUC of 0.84.

Conclusions:

Machine learning models can predict shoulder redislocation with moderate accuracy.
Further validation on larger, multicenter datasets is necessary.
This approach shows promise for improving patient outcomes.