Computer methods and programs in biomedicine · 2026
Prediction of femoral shaft fracture healing and clinical decision support through integration of mechano-biological modeling and machine learning.
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Ni Zhiyong, Zhang Junxia, Zhang Peng
Аннотация
BACKGROUND AND OBJECTIVES: Current clinical practice in preoperative planning for femoral shaft fractures lacks tools capable of quantitatively predicting outcomes across different treatment options, which significantly hinders the implementation of personalized precision treatment. This study aims to develop an integrated fracture healing prediction framework that combines mechano-biological modeling with machine learning.
METHODS: First, a comprehensive mechano-biological model was constructed, incorporating four key modules: mechanical stimulus computation, angiogenesis prediction, cell migration and differentiation, and callus modulus updating, to dynamically simulate femoral shaft fracture healing under bone plate fixation. Subsequently, the model generated 729 datasets using bone plate modulus, fracture gap size, and loading conditions across four rehabilitation stages as input features, with cortical callus modulus at 16 weeks postoperation as the output target. Four machine learning algorithms-Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN)-were systematically compared.
RESULTS: The mechano-biological model demonstrated consistent trends with animal experimental data. XGBoost achieved optimal predictive performance (R² = 0.969, MSE = 0.045, RMSE = 0.211, MAE = 0.178, MAPE = 5.795%). Feature importance analysis revealed that bone plate modulus (28%) and fracture gap size (25%) were the most critical factors influencing healing quality, while Stage 3 loading (weeks 9-12 postoperation, 18%) represented a critical window for mechanical intervention.
CONCLUSIONS: Optimizing implant stiffness and mechanical stimulation during critical phases can effectively improve healing outcomes. The integrated framework provides a reliable theoretical tool to support personalized clinical decision-making.
METHODS: First, a comprehensive mechano-biological model was constructed, incorporating four key modules: mechanical stimulus computation, angiogenesis prediction, cell migration and differentiation, and callus modulus updating, to dynamically simulate femoral shaft fracture healing under bone plate fixation. Subsequently, the model generated 729 datasets using bone plate modulus, fracture gap size, and loading conditions across four rehabilitation stages as input features, with cortical callus modulus at 16 weeks postoperation as the output target. Four machine learning algorithms-Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN)-were systematically compared.
RESULTS: The mechano-biological model demonstrated consistent trends with animal experimental data. XGBoost achieved optimal predictive performance (R² = 0.969, MSE = 0.045, RMSE = 0.211, MAE = 0.178, MAPE = 5.795%). Feature importance analysis revealed that bone plate modulus (28%) and fracture gap size (25%) were the most critical factors influencing healing quality, while Stage 3 loading (weeks 9-12 postoperation, 18%) represented a critical window for mechanical intervention.
CONCLUSIONS: Optimizing implant stiffness and mechanical stimulation during critical phases can effectively improve healing outcomes. The integrated framework provides a reliable theoretical tool to support personalized clinical decision-making.