Prosthetist-Specific Rectification Templates Based on Artificial Intelligence for the Digital Fabrication of Custom Transtibial Sockets

Abstract

The socket is the most important, patient-specific element of a prosthesis. Conventionally, the process of making a custom socket involves manually rectifying a plaster model of the residual limb. This process is time-consuming and often inconsistent among prosthetists because it is based on implicit knowledge. Hence, the aim of this work was to describe a novel process of generating a prosthetist-specific, digital “global” template and to illustrate that it can be automatically applied to rectify the shape of a transtibial residual limb. The process involved (1) the acquisition of a “training” dataset of unrectified and rectified positive models through manual data collection and digital 3D scanning, and (2) the unsupervised learning of the prosthetist’s rectifications by an artificial intelligence (AI) algorithm. The assessment of the process involved (1) evaluating whether the rectification rule learned by the AI was consistent with the prosthetist’s expectations, and (2) evaluating the template feasibility by applying the AI rectification process to a new residual limb and comparing the results to the prosthetist’s manual rectification for the same residual limb. The results suggest that the AI-rectified positive was consistent with the approach described by the prosthetist, with only small radial and angle errors and similar dimensions (volume and cross-sectional perimeters) as the hand-rectified positive. This study provides a proof-of-concept of the ability to integrate an AI algorithm into the fabrication process for transtibial prosthetic sockets. Once refined, this approach may provide a time-saving tool for prosthetists by automatically implementing typical rectifications and providing a good starting socket fit for individuals with amputation.