European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2026
ShRed: a machine learning model developed to predict shoulder redislocation.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Mahmoud Mohamed E, Murugaiyan Rajapriyian, Geetala Rahul, Smith Jed, Kessler Dimitri A, Grainger Andrew, Tytherleigh-Strong Graham, Kaggie Joshua, Chaudhury Salma
Аннотация
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.
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.