ShRed: a machine learning model developed to predict shoulder redislocation.
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, includi...
| Publicado en: | European Journal of Orthopaedic Surgery & Traumatology Vol. 36; no. 1; pp. 1 - 8 |
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| Autores principales: | , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
7/13/2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195316074&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195316074 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16338065 P4L jtl: European Journal of Orthopaedic Surgery & Traumatology issn: 16338065 maglogo: N pubinfo: dt: 7/13/2026 vid: 36 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 195316074 195316074 195316074 10.1007/s00590-026-04861-4 195316074 ppf: 1 ppct: 7 formats: tig: atl: ShRed: a machine learning model developed to predict shoulder redislocation. aug: au: Mahmoud, Mohamed E. Murugaiyan, Rajapriyian Geetala, Rahul Smith, Jed Kessler, Dimitri A. Grainger, Andrew Tytherleigh-Strong, Graham Kaggie, Joshua Chaudhury, Salma affil: https://ror.org/04v54gj93 Department of Orthopaedics, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK sug: subj: Machine Learning Shoulder Dislocation Prognosis Prediction Models Recurrence Cartilage, Articular Radiography Risk Assessment Decision Support Systems, Clinical Magnetic Resonance Imaging Human United Kingdom Male Female Prospective Studies Pilot Studies Tertiary Health Care Adolescence Adult Middle Age Random Forest Correlational Studies Logistic Regression Confidence Intervals Age Factors Shoulder Joint Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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