Biomechanics is an essential form of measurement in the understanding of the development and progression of osteoarthritis (OA). However, the number of participants in biomechanical studies are often small and there is limited ways to share or combine data from across institutions or studies. This is essential for applying modern machine learning methods, where large, complex datasets can be used to identify patterns in the data. Using these data-driven approaches, it could be possible to better predict the optimal interventions for patients at an early stage, potentially avoiding pain and inappropriate surgery or rehabilitation. In this project we developed a prototype database platform for combining and sharing biomechanics datasets. The database includes methods for importing and standardising data and associated variables, to create a seamless, searchable combined dataset of both healthy and knee OA biomechanics. Data was curated through calls to members of the OATech Network+ (Abstract
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