A collaborative approach of finite element method and machine learning algorithms for biomechanical analysis of implants used in tibial shaft fractures
Abstract
BACKGROUND: Tibial fractures are among the most common complex orthopedic injuries. The mechanical strength and biomaterial properties of implants used in the treatment of such fractures directly affect the healing process. In this study, the mechanical effects of different implant designs and biomaterials on obliquely fractured tibia were analyzed. In addition, it was aimed to evaluate the data obtained from finite element analysis (FEA) with machine learning (ML) algorithms. METHODS: Seven implant models for tibial shaft fractures were analyzed using static structural simulations in Ansys Workbench. Implants and cortical screws were made of Ti–6Al–4 V alloy or 316 L stainless steel (SS), and axial loads of 600, 800, and 1000 N simulated single-leg stance. A dataset of 1008 points, including maximum stress and total displacement, was generated and used to train Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Decision Tree (DT) models in WEKA. RESULTS: The mechanical behaviors of different implant and biomaterial combinations were compared, and the maximum stress value in implants with 316 L SS material properties was higher than the maximum stress value in Ti-6Al-4 V alloy implants. When the total displacement values in the tibia fracture region were examined, 316 L SS implants gave better results. In the machine learning estimations, the SVM model outperformed the MLP and DT algorithms. For maximum stress prediction, SVM achieved an mean absolute error…
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