AIMC Topic: Multiple Sclerosis

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Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis.

Journal of neuroimaging : official journal of the American Society of Neuroimaging Feb 15, 2021
BACKGROUND AND PURPOSE: Corpus callosum atrophy is a sensitive biomarker of multiple sclerosis (MS) neurodegeneration but typically requires manual 2D or volumetric 3D-based segmentations. We developed a supervised machine learning algorithm, DeepnCC...
Humans Deep Learning Magnetic Resonance Imaging Multiple Sclerosis Neuropsychological Tests Atrophy Corpus Callosum Young Adult Adult Male Prospective Studies Middle Aged Biomarkers Female
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Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging.

Journal of neuroscience methods Feb 11, 2021
BACKGROUND: Deep learning using convolutional neural networks (CNNs) has shown great promise in advancing neuroscience research. However, the ability to interpret the CNNs lags far behind, confounding their clinical translation.
Magnetic Resonance Imaging Multiple Sclerosis Neural Networks, Computer Humans Brain Deep Learning
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What is the impact of robotic rehabilitation on balance and gait outcomes in people with multiple sclerosis? A systematic review of randomized control trials.

European journal of physical and rehabilitation medicine Feb 4, 2021
INTRODUCTION: In recent years, robot-assisted gait training (RAGT) has been proposed as therapy for balance and gait dysfunctions in people with multiple sclerosis (PwMS). Through this systematic review, we aimed to discuss the impact of RAGT on bala...
Humans Randomized Controlled Trials as Topic Multiple Sclerosis Exoskeleton Device Robotics Gait Disorders, Neurologic Disability Evaluation Walk Test Combined Modality Therapy
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Machine learning-based differentiation between multiple sclerosis and glioma WHO II°-IV° using O-(2-[18F] fluoroethyl)-L-tyrosine positron emission tomography.

Journal of neuro-oncology Jan 27, 2021
INTRODUCTION: This study aimed to test the diagnostic significance of FET-PET imaging combined with machine learning for the differentiation between multiple sclerosis (MS) and glioma II°-IV°.
Adult Brain Neoplasms Male Middle Aged Machine Learning Female Aged Multiple Sclerosis Image Interpretation, Computer-Assisted Tyrosine Glioma Radiopharmaceuticals Positron-Emission Tomography Humans
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Effects of Robotic Exoskeleton-Aided Gait Training in the Strength, Body Balance, and Walking Speed in Individuals With Multiple Sclerosis: A Single-Group Preliminary Study.

Archives of physical medicine and rehabilitation Nov 9, 2020
OBJECTIVE: To assess effects of 15 exoskeleton-assisted gait training sessions, reflected by the muscle strength of the lower limbs and by walking speed immediately after the training sessions and at the 6-week follow-up.
Disability Evaluation Multiple Sclerosis Fatigue Walking Speed Exoskeleton Device Gait Disorders, Neurologic Longitudinal Studies Muscle Strength Robotics Postural Balance Adult Male Middle Aged Humans Prospective Studies Female
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The emerging role of artificial intelligence in multiple sclerosis imaging.

Multiple sclerosis (Houndmills, Basingstoke, England) Oct 28, 2020
BACKGROUND: Computer-aided diagnosis can facilitate the early detection and diagnosis of multiple sclerosis (MS) thus enabling earlier interventions and a reduction in long-term MS-related disability. Recent advancements in the field of artificial in...
Prognosis Diagnosis, Computer-Assisted Magnetic Resonance Imaging Multiple Sclerosis Humans Blindness Artificial Intelligence
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Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging.

NeuroImage Oct 16, 2020
We introduce the first-ever statistical framework for estimating the age of Multiple Sclerosis (MS) lesions from magnetic resonance imaging (MRI). Estimating lesion age is an important step when studying the longitudinal behavior of MS lesions and ca...
Humans Machine Learning Contrast Media Magnetic Resonance Imaging Multiple Sclerosis Gadolinium Adult Male Brain Middle Aged Female Time Factors Reproducibility of Results
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Automated thalamic nuclei segmentation using multi-planar cascaded convolutional neural networks.

Magnetic resonance imaging Aug 21, 2020
PURPOSE: To develop a fast and accurate convolutional neural network based method for segmentation of thalamic nuclei.
Essential Tremor Thalamic Nuclei Female Magnetic Resonance Imaging Young Adult Humans Multiple Sclerosis Case-Control Studies Neural Networks, Computer Automation Image Processing, Computer-Assisted
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Occasional essay: Multiple sclerosis in the digital age: 'seeing through a glass darkly'.

Journal of neurology, neurosurgery, and psychiatry Jul 24, 2020
Digital Technology Knowledge Genomics Humans Multiple Sclerosis, Chronic Progressive Multiple Sclerosis, Relapsing-Remitting Artificial Intelligence Magnetic Resonance Imaging Precision Medicine Immunologic Factors Multiple Sclerosis
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Neuro-fuzzy patch-wise R-CNN for multiple sclerosis segmentation.

Medical & biological engineering & computing Jul 17, 2020
The segmentation of the lesion plays a core role in diagnosis and monitoring of multiple sclerosis (MS). Magnetic resonance imaging (MRI) is the most frequent image modality used to evaluate such lesions. Because of the massive amount of data, manual...
Models, Statistical Deep Learning Multiple Sclerosis Magnetic Resonance Imaging Humans Image Interpretation, Computer-Assisted Biomedical Engineering Fuzzy Logic Neuroimaging Neural Networks, Computer Brain
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