Machine learning-enhanced nonlinear differential equation model for predicting osteoporosis progression using bone density imaging data
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Keywords:
Osteoporosis, nonlinear dynamics, bone remodeling, machine learning, neural differential equations, DXA, QCTAbstract
Osteoporosis refers to a chronic bone disease that is characterised by bone loss, microarchitectural loss and high likelihood of getting fragility fracture. Proper forecasting of disease in order to intervene early and plan therapy is crucial. The current research will develop a hybrid modelling system that combines machine learning with nonlinear differential equations to predict the development of osteoporosis through longitudinal bone density imaging. A model of nonlinear bone remodelling is derived based on the coupled system of osteoclast and osteoblast functions, the parameters of the resorption and formation process are adaptively determined with the help of machine learning. External inputs include imaging biomarkers of DXA, QCT and HR-pQCT scans which are used to calibrate
patient-specific remodelling behaviour. It is also extended to a neural differential equation module that is designed to improve the faithfulness of prediction by learning nonlinearities of higher-order that are not modelled by classical physiology-based equations. On of the longitudinal bone imaging dataset, experiments show that the hybrid model has a high prediction accuracy, which decreases
the mean absolute BMD error by 23% relative to standalone ML models and 31 relative to classical ODE models. Noise, missing modalities and variation in the follow-up interval The robustness testing demonstrates that there is negligible predictive power loss with robustness testing. These results imply the possibility of the machine-learning-enhanced nonlinear models yielding predictions on osteoporosis progression that could be used in practise.
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