A Scoping Review of Machine Learning Approaches for Predicting Lower Extremity Joint Contact Loads: Current Trends,...

Human gait analysis quantifies locomotion and assesses gait performance, particularly for patients with musculoskeletal disorders. While instrumented 3D gait analysis is the gold standard, advancements in physics based musculoskeletal modeling offer deeper insights into body mechanics. However, its complexity and resource demands limit clinical use, prompting interest in machine learning (ML) as a surrogate for traditional simulations. This scoping review synthesizes ML approaches for estimating joint contact forces in the lower extremities. A systematic search was conducted according to PRISMA-ScR guidelines, covering English language publications from January 2014 to August 2024 across PubMed, IEEE Xplore, Scopus, and SpringerLink. Studies were eligible if they applied ML techniques to estimate lower extremity joint contact forces in human participants and provided sufficient methodological details. Data extraction used a standardized charting form capturing study populations, movement types, input data, ML methods, validation procedures, and performance metrics. 27 studies met the inclusion criteria. The studies showed variability in populations, movement types, input data, ML methods, validation procedures, and performance metrics. Small datasets, often underrepresenting females, limit model generalizability. Inconsistencies in validation approaches and performance metrics, along with the lack of published data and code, hinder reproducibility and comparability. Despite challenges, ML models show potential in accurately predicting joint contact loads and forces. Future research should focus on expanding and diversifying datasets, standardizing methodologies, embracing open science practices, and integrating physics-informed approaches to enhance clinical applicability.