A collaborative approach of finite element method and machine learning algorithms for biomechanical analysis of implants used in tibial shaft fractures

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Analysis reveals how different implants impact tibial fractures, indicating machine learning enhances predictive accuracy.

SVM outperformed other models, predicting maximum stress with a mean absolute error of 0.24 for training data.
Implants made from 316 L stainless steel exhibited higher maximum stress compared to Ti–6Al–4 V alloy implants.
Finite element analysis effectively assessed the mechanical effects of implant designs on tibial shaft fractures.
This study underscores the role of data-driven approaches in improving biomechanical assessments.

Halil Burak Mutu (2025) studied this question.

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