Effect of uncertainties in musculoskeletal modeling inputs on sensitivity of knee joint finite element simulations

• Published in: Medical engineering & physics Vol. 138; p. 104313
• Main Authors: Jahangir, Sana, Bosch, Will, Esrafilian, Amir, Mononen, Mika E., Tanska, Petri, Stenroth, Lauri, Henriksen, Marius, Alkjær, Tine, Korhonen, Rami K.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.04.2025
• Subjects: Adult; Biomechanical Phenomena; Finite Element Analysis; Finite element modeling; Gait; Humans; Knee joint; Knee Joint - physiology; Male; Mechanical Phenomena; Modeling assumptions; Models, Biological; Musculoskeletal modeling; Personalized gait data; Uncertainty; Knee joint; Musculoskeletal modeling; Finite element modeling; Modeling assumptions; Personalized gait data
• ISSN: 1350-4533; 1873-4030; 1873-4030
• DOI: 10.1016/j.medengphy.2025.104313

•Analyzed the impact of various modeling assumptions on knee joint mechanics.•Constructed and tested five musculoskeletal models per subject plus a reference model.•Compared personalized gait inputs with literature-based non-personalized inputs.•Found up to 61% variation in finite element parameters with different modeling assumptions.•Highlighted the critical role of personalized gait data in accurate knee joint simulations. Musculoskeletal finite element modeling is used to estimate mechanical responses of knee joint tissues but involves uncertainties in muscle activations, marker locations, cartilage stiffness, maximum isometric forces, and gait parameter personalization. This study investigates how these uncertainties affect cartilage mechanical responses in knee joint finite element models during walking. We selected three subjects and constructed five musculoskeletal models for each, representing different variations of modeling assumptions, along with a reference model using conventional assumptions. We then ran finite element simulations of knee joints using both personalized gait inputs (motion and loading boundary conditions) and non-personalized gait inputs from literature. Our results demonstrated that varying modeling assumptions, such as optimization function for muscle activation patterns, knee marker position, knee cartilage stiffness, and maximum isometric force, produced highly subject-specific effects. Differences between the reference and altered models ranged from 3% to 30% in musculoskeletal modeling and from 1% to 61% in finite element modeling results. The largest effects occurred with non-personalized gait data, resulting in up to 6- and 2-fold changes in musculoskeletal and finite element modeling results, respectively. This study highlights the sensitivity of knee mechanics to different modeling assumptions and underscores the importance of applying personalized gait parameters for accurate finite element simulations.