Evaluating the Effectiveness of Transtibial Prosthetic Socket Shape Design Using Artificial Intelligence: A Clinical Comparison With Traditional Plaster Cast Socket Designs.

Abstract

Objective: To investigate the feasibility of creating an artificial intelligence (AI) algorithm to enhance prosthetic socket shapes for transtibial prostheses, aiming for a less operator-dependent, standardized approach.

Design: The study comprised 2 phases: first, developing an AI algorithm in a cross-sectional study to predict prosthetic socket shapes. Second, testing the AI-predicted digitally measured and standardized designed (DMSD) prosthetic socket against a manually measured and designed (MMD) prosthetic socket in a 2-week within-subject cross-sectional study.

Setting: The study was done at the rehabilitation department of the Radboud University Medical Center in Nijmegen, the Netherlands.

Participants: The AI algorithm was developed using retrospective data from 116 patients from a Dutch orthopedic company, OIM Orthopedie, and tested on 10 randomly selected participants from Papenburg Orthopedie.

Interventions: Utilization of an AI algorithm to enhance the shape of a transtibial prosthetic socket.

Main outcome measures: The algorithm was optimized to minimize the error in the test set. Participants' socket comfort score and fitting ratings from an independent physiotherapist and prosthetist were collected.

Results: Predicted prosthetic shapes deviated by 2.51 mm from the actual designs. In total, 8 of 10 DMSD and all 10 MMD-prosthetic sockets were satisfactory for home testing. Participants rated DMSD-prosthetic sockets at 7.1 ± 2.2 (n=8) and MMD-prosthetic sockets at 6.6 ± 1.2 (n=10) on average.

Conclusions: The study demonstrates promising results for using an AI algorithm in prosthetic socket design, but long-term effectiveness and refinement for improved comfort and fit in more deviant cases are necessary.