A Novel Approach by Integrating CT-Based Imaging Data and Machine Learning to Predict Patient-Specific Young's Modulus Values.
biomechanical · Level V
Where this comes from
- Record sourced from PubMed, PMID 40852617.
- Also identified by DOI 10.1155/aort/6257188 and PMC identifier 12370390.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Finite element analysis (FEA) stands as a cornerstone in preclinical investigations for implant therapy, particularly in orthopaedics and biomechanics. Accurate modelling of bone properties is crucial for meaningful FEA outcomes, considering the complex nature of bone tissue. This study proposes a novel approach by integrating CT-based imaging data and machine learning to predict patient-specific Young's modulus values. A back propagation neural network (BPNN), incorporating texture properties extracted from CT images, demonstrates robustness in predicting Young's modulus. Validation against three-point bending experiments on rabbit femur bones shows promising results, with stress values within 13% of those from FEA. The proposed methodology holds the potential for enhancing preclinical evaluations of implant therapy and fostering the development of patient-specific implants for improved clinical outcomes.