AIMC Topic: Multiple Sclerosis

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Prediction of Multiple Sclerosis Patient Disability from Structural Connectivity using Convolutional Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Prediction of disability progression in multiple sclerosis patients is a critical component of their management. In particular, one challenge is to identify and characterize a patient profile who may benefit of efficient treatments. However, it is no...

Design and Characterization of a Robotic Device for the Assessment of Hand Proprioceptive, Motor, and Sensorimotor Impairments.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Hand function is often impaired after neurological injuries such as stroke. In order to design patient-specific rehabilitation, it is essential to quantitatively assess those deficits. Current clinical scores cannot provide the required level of deta...

Automated Rating of Multiple Sclerosis Test Results Using a Convolutional Neural Network.

Studies in health technology and informatics
This work concerns methods for automated rating of the progression of Multiple Sclerosis (MS). Often, MS patients develop cognitive deficits. The Brief Visuospatial Memory Test-Revised (BVMT-R) is a recognized method to measure optical recognition de...

The role of robotic gait training coupled with virtual reality in boosting the rehabilitative outcomes in patients with multiple sclerosis.

International journal of rehabilitation research. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation
Motor impairment is the most common symptom in multiple sclerosis (MS). Thus, a variety of new rehabilitative strategies, including robotic gait training, have been implemented, showing their effectiveness. The aim of our study was to investigate whe...

Robot-Assisted Body-Weight-Supported Treadmill Training in Gait Impairment in Multiple Sclerosis Patients: A Pilot Study.

Advances in experimental medicine and biology
This study deals with the use of a robot-assisted body-weight-supported treadmill training in multiple sclerosis (MS) patients with gait dysfunction. Twenty MS patients (10 men and 10 women) of the mean of 46.3 ± 8.5 years were assigned to a six-week...

Discriminating sample groups with multi-way data.

Biostatistics (Oxford, England)
High-dimensional linear classifiers, such as distance weighted discrimination (DWD) and versions of the support vector machine (SVM), are commonly used in biomedical research to distinguish groups of subjects based on a large number of features. Howe...

Neuroplasticity-Based Technologies and Interventions for Restoring Motor Functions in Multiple Sclerosis.

Advances in experimental medicine and biology
Motor impairments are very common in multiple sclerosis (MS), leading to a reduced Quality of Life and active participation. In the past decades, new insights into the functional reorganization processes that occur after a brain injury have been intr...

Determining Multiple Sclerosis Phenotype from Electronic Medical Records.

Journal of managed care & specialty pharmacy
BACKGROUND: Multiple sclerosis (MS), a central nervous system disease in which nerve signals are disrupted by scarring and demyelination, is classified into phenotypes depending on the patterns of cognitive or physical impairment progression: relapsi...

Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation.

IEEE journal of biomedical and health informatics
Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that...

Brain tissue segmentation using fuzzy clustering techniques.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Medical image segmentation is an essential step for most consequent image analysis tasks. Medical images can be segmented manually, but the accuracy of image segmentation using the automated segmentation algorithms is more when compared w...