Patient-specific in silico models can quantify primary implant stability in elderly human bone

Area of Science:

• Orthopaedic biomechanics
• Computational modeling
• Materials science

Background:

• Secure implant fixation in osteoporotic bone presents significant challenges due to bone quality variability.
• Ex vivo mechanical testing is time-consuming and material-intensive for evaluating implant performance.
• In silico models offer a potential solution for cost-effective and rapid design of orthopedic implants.

Purpose of the Study:

• To validate a high-resolution, patient-specific micro-finite element model for analyzing multi-screw fracture fixation systems.
• To assess the model's capability in capturing intricate bone microstructure and predicting mechanical behavior.
• To establish the accuracy of in silico predictions against ex vivo mechanical testing data.

Main Methods:

• Micro-computed tomography (micro-CT) scanning of eight human cadaveric humerii.
• Ex vivo mechanical testing to quantify bone stiffness and implant displacements.
• Development and validation of patient-specific micro-finite element models with tissue-level material properties.

Main Results:

• The validated micro-finite element model demonstrated excellent accuracy in predicting bone-implant system behavior.
• Average coefficient of determination of 0.89 and a mean absolute error of 43 μm were achieved when correlating in silico and ex vivo displacements.
• Patient-specific tissue modulus determination was crucial for matching simulated and measured bone stiffness.

Conclusions:

• A patient-specific computational model of the bone-implant system was successfully validated at the tissue level.
• The high-resolution model accurately predicts mechanical responses, offering a valuable tool for orthopedic implant design.
• This approach can significantly reduce the time and cost associated with evaluating implant fixation in challenging bone conditions.