A novel methodology for calibration and experimental validation of cervical spine biomechanics models using ANFIS and genetic algorithm

Abstract

The cervical spine is an essential structure in human physiology but is vulnerable to diseases, such as lesions, disc herniation, and vertebral fractures. Finite element (FE) modeling represents a powerful approach for predicting the biomechanics of the cervical spine under various loading conditions. Conventional methods usually do not take into account the consistency of the material properties, which potentially restricts their capability to realistically represent biomechanical behavior. This study presents a novel calibration methodology aimed at enhancing the predictive biofidelity of FE models for cervical spine biomechanics. The proposed approach systematically identifies optimal material properties to better replicate in vitro biomechanical responses. To achieve this, the cervical spine geometry was reconstructed from computed tomography (CT) scans and validated against cadaveric morphometric data to ensure anatomical accuracy. Mechanical properties of both hard and soft tissues were collected based on a comprehensive review of the literature. A dataset of biomechanical responses was generated through FE simulations using a range of material properties. The calibration process integrated an adaptive neuro-fuzzy inference system (ANFIS) framework with genetic algorithms, followed by a rigorous validation step against experimental benchmarks to ensure precise replication of in vitro test outcomes. The results show that this calibrated FE model significantly improves cervical spine biomechanics predictions, accurately matching intervertebral disc mechanics and ligament behavior. This research provides a robust framework, integrating numerical modeling with experimental studies, guiding future biomechanical research to potentially improve clinical and surgical outcomes. Some of the prominent applications are injury analysis, degenerative disease modeling, and the prediction of spinal deformity.