Longitudinal CT-based finite element analyses provide objective fracture healing measures in an ovine tibia model

Area of Science:

• Biomedical Engineering
• Orthopedic Surgery
• Computational Mechanics

Background:

• Assessing bone fracture healing is crucial for patient care.
• Implantable sensors and virtual biomechanical tests offer objective fracture assessment methods.
• Current methods lack clinical integration, requiring further validation.

Purpose of the Study:

• To correlate continuous implantable sensor data with longitudinal CT-based finite element (FE) simulations.
• To validate the use of in silico methods for objective fracture healing assessment.
• To explore the potential for developing generalizable fracture healing criteria.

Main Methods:

• Utilized data from implantable sensors monitoring load in a sheep tibial osteotomy model.
• Performed longitudinal FE simulations, including virtual torsional rigidity tests and bone-implant construct modeling.
• Compared in silico simulation results with sensor data and applied an empirical healing rule.

Main Results:

• In silico simulations significantly correlated with implantable sensor data.
• The methodology accurately identified delayed healing and nonunion in the animal cohort.
• Virtual mechanical tests objectively measured fracture healing progression.

Conclusions:

• Virtual biomechanical testing integrated with longitudinal CT scans offers a reliable method for objective fracture healing assessment.
• This approach shows potential for translation to clinical settings to establish generalizable healing criteria.
• The combined methodology can improve clinical decision-making for fracture treatment.