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

ABSTRACT

OBJECTIVE: This study investigates the feasibility of creating an AI algorithm to enhance prosthetic socket shapes for transtibial prostheses, aiming for a less operator-dependent, standardized approach. DESIGN: The study comprised two 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 two-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 ten 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 (SCS) and fitting ratings from an independent physiotherapist and prosthetist were collected. RESULTS: Predicted prosthetic shapes deviated by 2.51 mm from the actual designs. 8/10 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. CONCLUSION: 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.