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A scoping review and evaluation of open-source transtibial amputation musculoskeletal models for female populations.

Tess M R CarswellMisha HasanJoshua W Giles
Published in: Prosthetics and orthotics international (2024)
Musculoskeletal modeling is often used to study people with transtibial amputations. Females in this population are of particular interest as they are underrepresented in research, experience unique challenges, and demonstrate gait biomechanics distinct from males. Because generic models often neglect innate variations between populations, it is important to determine whether data used to develop a model are representative of the population studied. The objective of this study was to review and analyze existing transtibial amputation musculoskeletal models, establish a database from the information compiled, and use the database to select the model most relevant for studying female populations. A scoping search was performed and a database was created based on data detailing the eligible models. Models were evaluated through a weighted decision process based on criteria of their representation of females with transtibial amputations, prosthetic functionality, development transparency, overall functionality, and experimental validation methods. The scoping review identified 3 studies, Willson et al., LaPrè et al., and Miller and Esposito. A database detailing these models was established. The Willson model scored highest on all criteria except overall functionality, where the LaPrè model outscored it. Based on the established weightings, the Willson model was classed most appropriate for the stated goals. The created database can be used by other researchers to guide their own modeling studies, irrespective of the population of focus. Of the 3, the Willson model was found most relevant for studying females with transtibial amputations. This model will be used in future work investigating and addressing challenges of females with transtibial amputations.
Keyphrases
  • magnetic resonance
  • public health
  • emergency department
  • machine learning
  • cross sectional
  • decision making
  • artificial intelligence
  • health information