Ensemble machine-learning model with external validation for pre-collapse ONFH: Retrospective cohort study of modified light bulb or multiple drilling

Background: Non-traumatic osteonecrosis of the femoral head (ONFH) can progress to severe osteoarthritis without early intervention. The modified light bulb (MLB) and multiple drilling (MD) techniques aim to preserve the femoral head in early-stage ONFH. This study develops an ensemble machine learning (ML) model to support orthopedic surgeons in selecting between these techniques based on clinical and radiographic outcomes. Methods: We retrospectively analyzed 38 patients (51 hips) with non-traumatic ONFH treated with MLB (28 hips) or MD (23 hips). Outcomes were assessed using VAS pain scores, Harris Hip Score (HHS), and Hip Disability and Osteoarthritis Outcome Score (HOOS). Radiological staging included Ficat-Arlet, ARCO, and the Combined Necrotic Angle of Kerboul (CNAK). An ensemble ML model, combining LGBM and XGBoost, was developed to predict clinical outcomes, with comparative analyses using t-tests and Welch's t-test. Results: The MLB group showed significantly lower VAS pain scores in 12 months (3.07 vs. 5.17, P = 0.01) and higher HOOS at 6 months (65.88 vs. 52.13, P = 0.04). Clinical success (mHHS >80) was higher for MLB (57.1 %) compared to MD (39.1 %, P = 0.20). The ML model achieved balanced accuracy = 85.7 % (95 % CI 70–95), AUROC = 0.93, F1 score (85.0 %), and AUROC (93.0 %). Key predictors identified by SHAP analysis included patient age, preoperative VAS, and postoperative HHS and HOOS at 6 months. Conclusion: The ensemble ML model provides a valuable decision support tool for predicting outcomes in ONFH patients. The MLB group showed greater 12-month pain reduction and higher HOOS at 6 months; other differences were not statistically significant, underscoring the potential of predictive models to personalize treatment decisions in clinical practice.