Multi-factorial biomechanical evaluation of plate-screw fixation in femoral shaft fractures using numerical and machine learning approaches.

Plate and screw fixation is a widely used method in the surgical treatment of femoral shaft fractures; however, mechanical performance may vary depending on implant material, fracture gap size and loading conditions. This study aimed to investigate the biomechanical behavior of femoral shaft fractures stabilized with plate and screw fixation by applying finite element analysis (FEA) and to evaluate the predictive performance of machine learning (ML) algorithms based on numerical results. Three different fracture gap sizes (1, 2,and 3 mm) were modeled on a femur geometry, and axial loads ranging from 400 N to 1200 N (in 100 N increments) were applied. Two implant materials, Ti-6Al-4V and 316 L stainless steel (SS), were assessed. The stress distribution on the plate and first screw and the displacements at the femoral head and fracture site were analyzed using two different mesh densities. Subsequently, ML algorithms including Decision Tree (DT), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) were used to predict the stress and displacement values based on the numerical dataset. The finer mesh provided more accurate results. Ti-6Al-4V showed lower von Mises stress values and displacement magnitudes compared to 316 L SS. Among the ML methods, MLP and SVM demonstrated better prediction accuracy than DT. The integration of FEA and ML techniques enables efficient prediction of implant biomechanics, offering a promising approach for preclinical evaluation and optimization of orthopedic fixation systems.