Optimisation of orthopaedic implant design using statistical shape space analysis based on level sets

Area of Science:

• Medical imaging and biomechanics
• Statistical shape analysis
• Computational anatomy

Background:

• Statistical shape models (SSMs) are crucial for understanding anatomical variability and are used in medical image segmentation.
• Current methods often seek the single best-fit shape instance, limiting the assessment of broader population variability.
• Assessing anatomical criteria across the full spectrum of shape variability is challenging but essential for robust applications.

Purpose of the Study:

• To develop a method for assessing specific anatomical/morphological criteria across population shape variability.
• To create a framework for evidence-based orthopaedic implant design that considers population-wide anatomical diversity.
• To optimize implant design by identifying key bone variability patterns influencing implant fitting.

Main Methods:

• Utilized a level set segmentation approach within the parametric space of a statistical shape model.
• Employed a multi-level narrow-band approach for computational efficiency in solving the segmentation problem.
• Developed a virtual fitting framework to evaluate implant suitability across diverse virtual patient populations derived from the SSM.

Main Results:

• Demonstrated a method to assess anatomical criteria across the shape variability of a population.
• Successfully applied the framework to virtually fit orthopaedic implants to a statistical shape model of the proximal human tibia.
• Identified specific patterns of bone variability crucial for successful implant fitting, guiding design improvements.

Conclusions:

• The developed method enables comprehensive assessment of anatomical criteria across diverse populations.
• The framework facilitates evidence-based orthopaedic implant design, moving beyond limited cadaveric validation.
• This approach enhances implant design by ensuring better fit and suitability for a larger patient demographic, as shown in proximal tibia implant optimization.