A Real-Time Mechanical Information Acquisition System and Finite Element Prediction Method for Limb Lengthening: A...

Area of Science:

• Orthopedic Surgery
• Biomedical Engineering
• Regenerative Medicine

Background:

• Mechanical forces critically influence bone regeneration and soft tissue health in distraction osteogenesis (DO).
• Current methods for monitoring distraction forces lack predictive capabilities, risking complications like nerve ischemia and device failure.
• Accurate prediction of distraction resisting forces (DRF) is essential for safe and effective DO.

Purpose of the Study:

• To develop and validate a comprehensive system for monitoring and predicting DRF in DO.
• To engineer a novel sensor interface for accurate force measurement, separating axial forces from bending moments.
• To create a patient-specific finite element (FE) model for predicting non-linear force evolution during distraction.

Main Methods:

• Designed a custom mechanical acquisition system with a "double-ring" sensor interface.
• Developed a patient-specific FE model using CT imaging and the Ogden hyperelastic constitutive law.
• Validated the system in an ovine model (N=1) performing immediate postoperative distraction up to 4 cm.

Main Results:

• The system demonstrated high linearity (R2>0.999) and captured tissue viscoelastic relaxation.
• The FE model accurately predicted peak distraction forces, with improved accuracy at higher distraction magnitudes.
• The integrated approach successfully correlated mechanical sensing with predictive modeling.

Conclusions:

• This framework integrates mechanical sensing with predictive modeling for enhanced DO.
• It provides a foundation for closed-loop, patient-specific control in distraction osteogenesis.
• The developed system promises to improve the safety and efficacy of bone regeneration procedures.