Statistical prediction of bone microstructure degradation to study patient dependency in osteoporosis

Numerical prediction of osteoporosis evolution is a challenging objective in medicine, particularly when one desires to account for patient dependency. The use statistical methods reconstruct bone microstructure distribution could be helpful tool this prediction, as they are able provide different types microstructures that can optimized fit with each patient. An initial sample was obtained from high-resolution X-ray computed tomography (HR?CT). Its time using previously developed degradation model used the ground truth. Statistical were reconstructed at stages two-point correlation functions (TPCFs). A blind search approach find microstructures, and coefficient showed less than 2% TPCF error between reconstruction degraded model. models also 13% corresponding mechanical properties. results good method extrapolated physical characterization dependency predict density loss over time.