Automated transtibial prosthesis alignment: A systematic review

• Main Authors: Khamis T., Khamis A.A., Al Kouzbary M., Al Kouzbary H., Mokayed H., Razak N.A.A., Osman N.A.A.
• Other Authors: 57900244500
• Format: Review
• Published: Elsevier B.V. 2025
• Subjects: Algorithms; Artificial Limbs; Humans; Machine Learning; Prosthesis Design; Prosthesis Fitting; Tibia; 'current; Alignment system; Automated alignments; Automated process; Below-knee prosthesis; Machine learning algorithms; Manual identification; Prosthetic alignment; Systematic Review; Trans-tibial prosthesis; algorithm; automation; biomechanics; human; machine learning; prosthetic alignment; Review; systematic review; limb prosthesis; procedures; prosthesis design; prosthetic fitting; surgery; tibia; Adversarial machine learning

This comprehensive systematic review critically analyzes the current progress and challenges in automating transtibial prosthesis alignment. The manual identification of alignment changes in prostheses has been found to lack reliability, necessitating the development of automated processes. Through a rigorous systematic search across major electronic databases, this review includes the highly relevant studies out of an initial pool of 2111 records. The findings highlight the urgent need for automated alignment systems in individuals with transtibial amputation. The selected studies represent cutting-edge research, employing diverse approaches such as advanced machine learning algorithms and innovative alignment tools, to automate the detection and adjustment of prosthesis alignment. Collectively, this review emphasizes the immense potential of automated transtibial prosthesis alignment systems to enhance alignment accuracy and significantly reduce human error. Furthermore, it identifies important limitations in the reviewed studies, serving as a catalyst for future research to address these gaps and explore alternative machine learning algorithms. The insights derived from this systematic review provide valuable guidance for researchers, clinicians, and developers aiming to propel the field of automated transtibial prosthesis alignment forward. ? 2024 Elsevier B.V.