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Patient-specific Hip Arthroplasty Dislocation Risk Calculator: An Explainable Multimodal Machine Learning-based
Bardia Khosravi1, Pouria Rouzrokh1, Hilal Maradit Kremers1
1Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopedic Surgery (B.K., P.R., H.M.K., D.R.L., Q.J.J., M.J.T., C.C.W.), Radiology Informatics Laboratory, Department of Radiology (B.K., P.R., S.F., B.J.E.), Department of Quantitative Health Sciences (H.M.K., D.R.L., W.K.K.), Alix School of Medicine (Q.J.J.), Department of Orthopedic Surgery (R.J.S., M.J.T., C.C.W.), and Department of Clinical Anatomy (C.C.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905.
Purpose:
To develop a multimodal machine learning-based pipeline to predict patient-specific risk of dislocation following primary total hip arthroplasty (THA).
Materials And Methods:
This study retrospectively evaluated 17 073 patients who underwent primary THA between 1998 and 2018. A test set of 1718 patients was held out. A hybrid network of EfficientNet-B4 and Swin-B transformer was developed to classify patients according to 5-year dislocation outcomes from preoperative anteroposterior pelvic radiographs and clinical characteristics (demographics, comorbidities, and surgical characteristics). The most informative imaging features, extracted by the mentioned model, were selected and concatenated with clinical features. A collection of these features was then used to train a multimodal survival XGBoost model to predict the individualized hazard of dislocation within 5 years. C index was used to evaluate the multimodal survival model on the test set and compare it with another clinical-only model trained only on clinical data. Shapley additive explanation values were used for model explanation.
Results:
The study sample had a median age of 65 years (IQR: 18 years; 52.1% [8889] women) with a 5-year dislocation incidence of 2%. On the holdout test set, the clinical-only model achieved a C index of 0.64 (95% CI: 0.60, 0.68). The addition of imaging features boosted multimodal model performance to a C index of 0.74 (95% CI: 0.69, 0.78; P = .02).
Conclusion:
Due to its discrimination ability and explainability, this risk calculator can be a potential powerful dislocation risk stratification and THA planning tool.Keywords: Conventional Radiography, Surgery, Skeletal-Appendicular, Hip, Outcomes Analysis, Supervised Learning, Convolutional Neural Network (CNN), Gradient Boosting Machines (GBM) Supplemental material is available for this article. © RSNA, 2022.
