Characterising residual limb morphology and prosthetic socket design based on expert clinician practice

Abstract

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: prin-cipal component analysis (PCA) to extract key categories in anatomic and surgical variation, and K-Means Clustering to identify local ‘rectification’ design features. Rectification patterns repre-senting Total Surface Bearing and Patella Tendon Bearing design philosophies are identified au-tomatically 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 rec-tifications 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 de-velopments in CAD/CAM clinical practice and accuracy of biomechanics research.

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Identifiers

ORCID for Alexander Dickinson: orcid.org/0000-0002-9647-1944

ORCID for Joshua Steer: orcid.org/0000-0002-6288-1347

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