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Updated: Jul 15, 2026
Rat Model of Adhesive Capsulitis of the Shoulder
Published on: September 28, 2018
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.
Purpose:
This feasibility study aimed to develop and evaluate machine learning models to predict shoulder redislocation using joint-specific imaging characteristics, in addition to traditional demographic variables.
Methods:
A prospective dataset from a tertiary referral centre was analysed, including cartilage MRI thickness measurements. Six classification algorithms were compared using 10-fold stratified cross-validation as the primary evaluation method. Preprocessing involved one-hot encoding of categorical variables and median imputation for missing values. The dataset was stratified into training (80%) and testing (20%) subsets.
Results:
A total of 42 patients (54.8% redislocation rate) were included. Random Forest demonstrated the highest cross-validated accuracy of 79.0% (± 16.1%), precision of 88.3%, recall of 75.0%, and AUC of 0.84, with a 95% confidence interval of 69.0% to 89.0%. Feature importance analysis identified years since first dislocation as the most influential predictor, followed by age at first dislocation and glenoid cartilage thickness.
Conclusion:
This feasibility study demonstrates that machine learning models can predict shoulder redislocation with moderate accuracy. External validation on larger, multicentre datasets is required.
