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Background: Corrective shoe insoles are prescribed for a range of foot deformities and are typically designed based on a subjective assessment limiting personalization and potentially leading to sub optimal treatment outcomes. The incorporation of in silico techniques in the design and customization of insoles may improve personalized correction and hence insole efficiency. Methods: We developed an in silico workflow for insole design and customization using a combination of measured motion capture, inverse musculoskeletal modelling as well as forward simulation approaches to predict the kinematic response to specific insole designs. The developed workflow was tested on twenty-seven participants containing a combination of healthy participants (7) and patients with flatfoot deformity (20). Results: Average error between measured and simulated kinematics were 4.7 ± 3.1, 4.5 ± 3.1, 2.3 ± 2.3, and 2.3 ± 2.7° for the chopart obliquity, chopart anterior-posterior axis, tarsometatarsal first ray, and tarsometatarsal fifth ray joints respectively. Discussion : The developed workflow offers distinct advantages to previous modeling workflows such as speed of use, use of more accessible data, use of only open-source software, and is highly automated. It provides a solid basis for future work on improving predictive accuracy by adapting the currently implemented insole model and incorporating additional data such as plantar pressure., Competing Interests: Author SV and FB were employed by the company Materialise. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Killen, Van Rossom, Burg, Vander Sloten and Jonkers.)

Introduction: Knee osteoarthritis (KOA) is characterized by articular cartilage degeneration. It has been widely accepted that the mechanical joint environment plays a significant role in the onset and progression of this disease. In silico models have been used to study the interplay between mechanical loading and cartilage degeneration, hereby relying mainly on two key mechanoregulatory factors indicative of collagen degradation and proteoglycans depletion. These factors are the strain in collagen fibril direction (SFD) and maximum shear strain (MSS) respectively. Methods: In this study, a multi-scale in silico modeling approach was used based on a synergy between musculoskeletal and finite element modeling to evaluate the SFD and MSS. These strains were evaluated during gait based on subject-specific gait analysis data collected at baseline (before a 2-year follow-up) for a healthy and progressive early-stage KOA subject with similar demographics. Results: The results show that both SFD and MSS factors allowed distinguishing between a healthy subject and a KOA subject, showing progression at 2 years follow-up, at the instance of peak contact force as well as during the stance phase of the gait cycle. At the peak of the stance phase, the SFD were found to be more elevated in the KOA patient with the median being 0.82% higher in the lateral and 0.4% higher in the medial compartment of the tibial cartilage compared to the healthy subject. Similarly, for the MSS, the median strains were found to be 3.6% higher in the lateral and 0.7% higher in the medial tibial compartment of the KOA patient compared to the healthy subject. Based on these intersubject SFD and MSS differences, we were additionally able to identify that the tibial compartment of the KOA subject at risk of progression. Conclusion/discussion: We confirmed the mechanoregulatory factors as potential biomarkers to discriminate patients at risk of disease progression. Future studies should evaluate the sensitivity of the mechanoregulatory factors calculated based on this multi-scale modeling workflow in larger patient and control cohorts., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Mohout, Elahi, Esrafilian, Killen, Korhonen, Verschueren and Jonkers.)