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

• Published in: Archives of physical medicine and rehabilitation Vol. 106; no. 2; pp. 239 - 246
• Main Authors: van der Stelt, Merel, Berends, Bo, Papenburg, Marco, Langenhuyzen, Tom, Maal, Thomas, Brouwers, Lars, de Jong, Guido, Leijendekkers, Ruud
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2025
• Subjects: 3D printing; 3D scanning; Adult; Aged; Algorithms; Amputees - rehabilitation; Artificial Intelligence; Artificial Limbs; Cross-Sectional Studies; Design; Feasibility Studies; Female; Humans; Male; Middle Aged; Netherlands; Prosthesis Design - methods; Prosthesis Fitting - methods; Prosthetic socket; Retrospective Studies; Standardized; Tibia - surgery; Transtibial; Transtibial amputation; Netherlands; Prosthetic socket; SCS; FEA; MMD; AI; Transtibial; Standardized; Design; Transtibial amputation; 3D scanning; DMSD; Artificial intelligence; 3D printing
• ISSN: 0003-9993; 1532-821X; 1532-821X
• DOI: 10.1016/j.apmr.2024.08.026

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. 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. The study was done at the rehabilitation department of the Radboud University Medical Center in Nijmegen, the Netherlands. 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. Utilization of an AI algorithm to enhance the shape of a transtibial prosthetic socket. 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. 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. 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.