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Multiple Sclerosis is a chronic progressive neurological disease characterized by the growth of lesions in the white matter of the brain. Superior visualization and analysis of MS lesions can be obtained by brain magnetic resonance imaging techniques relative to other MRI modalities, according to T2-fluid-attenuated inversion recovery. Longitudinal brain FLAIR MRI in MS, which involves repetitively imaging a patient over time, provides valuable information for clinicians in monitoring disease progression. Predicting future whole brain MRI tests with variable time lag has only been attempted in limited fields, such as healthy age and structural degeneration of Alzheimer's Disease. We discuss novel approaches to deep learning architectures for MS FLAIR image processing in this series in order to enable the prediction of longitudinal images in a scalable manner. Four distinct deep learning architectures have been built.
Multiple Sclerosis is a chronic medical disorder characterized by the onset of lesions in the white matter of the brain. Clinicians seeking to track disease progression can use a follow-up brain FLAIR MRI in MS. In the discriminator, we use controlled guided attention and dilated convolutions, which helps make an informed decision of whether the images are real or not based on attention to the lesion area, which in turn could help ensure the generator can help determine the lesion area of future examinations more accurately.
This report explores the possibility of passively monitoring gait and turning in everyday life in people with multiple sclerosis to help those at risk of falls. PwMS were followed biweekly for a year after gathering gait and turning data in daily life, and then classified as fallers if they experienced more than one fall. At toe-off, the fallers had a smaller pitch of their feet, reflecting less plantarflexion during the push-off phase of walking, which can lead to poor foot clearance and increased metabolic cost of walking. In conclusion, our team of PwMS found that objective tracking of gait and turning in daily life could identify those at risk of falls, and that the pitch at toe-off was the single most influential predictor of future falls.
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