An Automatic Method Framework for Personalized Knee Prosthetic Modeling Based on Kinematic Geometry

Area of Science:

• Biomedical Engineering
• Orthopedic Surgery
• Computational Anatomy

Background:

• Total knee arthroplasty (TKA) outcomes are significantly influenced by knee prosthesis design.
• Personalized prostheses offer advantages over standard designs, but methods for their creation are underdeveloped.
• Existing TKA prostheses often fail to fully address individual patient biomechanics.

Purpose of the Study:

• To present an automated framework for modeling personalized knee prostheses.
• To leverage shape statistics and kinematic geometry for patient-specific implant design.
• To improve the efficacy of total knee arthroplasty through customized prosthesis creation.

Main Methods:

• Established an average healthy knee model using unsupervised learning.
• Calculated the Surgical Transepipondylar Axis (sTEA) for model resizing and kinematic simulation.
• Simulated excising operations on patient and average models to generate initial prosthesis designs.
• Refined prosthesis models based on simulated knee motion analysis.

Main Results:

• Achieved an average maximum error of <2 mm between the resized healthy knee model and the patient's native knee.
• Attained an average maximum error of <3 mm between simulated and actual knee motion.
• Demonstrated the framework's capability to generate patient-specific knee prosthesis models.

Conclusions:

• The developed framework provides an effective method for creating personalized knee prostheses.
• This approach addresses the limitations of standard prostheses in TKA.
• The automated modeling system enhances the potential for improved TKA functional outcomes.