Characterising Residual Limb Morphology and Prosthetic Socket Design Based on Expert Clinician Practice

Functional, comfortable prosthetic limbs depend on personalised sockets, currently designed using an iterative, expert-led process, which can be expensive and inconvenient. Computer-aided design and manufacturing (CAD/CAM) offers enhanced repeatability, but far more use could be made from clinicians' extensive digital design records. Knowledge-based socket design using smart templates could collate successful design features and tailor them to a new patient. Based on 67 residual limb scans and corresponding sockets, this paper develops a method of objectively analysing personalised design approaches by expert prosthetists, using machine learning: principal component analysis (PCA) to extract key categories in anatomic and surgical variation, and k-means clustering to identify local 'rectification' design features. Rectification patterns representing Total Surface Bearing and Patella Tendon Bearing design philosophies are identified automatically by PCA, which reveals trends in socket design choice for different limb shapes that match clinical guidelines. Expert design practice is quantified by measuring the size of local rectifications identified by k-means clustering. Implementing smart templates based on these trends requires clinical assessment by prosthetists and does not substitute training. This study provides methods for population-based socket design analysis, and example data, which will support developments in CAD/CAM clinical practice and accuracy of biomechanics research.