With an aging population, decreased muscle mass from conditions such as sarcopenia can be expected to lead to frequently decreased mobility, lowering patient quality of life. To develop effective treatments to slow down mobility loss, it is essential to obtain robust, objective mobility measurements that ideally do not require patient tasks. In this work, we explore the feasibility of predicting patient mobility by applying a neural network on sagittal knee MR images and accelerometry data from the Osteoarthritis Initiative.
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