AIMC Topic: Multiple Sclerosis

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Computer-Aided Diagnosis of Multiple Sclerosis Using a Support Vector Machine and Optical Coherence Tomography Features.

Sensors (Basel, Switzerland) Dec 3, 2019
The purpose of this paper is to evaluate the feasibility of diagnosing multiple sclerosis (MS) using optical coherence tomography (OCT) data and a support vector machine (SVM) as an automatic classifier. Forty-eight MS patients without symptoms of op...
Adult Male Case-Control Studies Diagnosis, Computer-Assisted Humans Machine Learning Female Sex Factors Multiple Sclerosis Normal Distribution Optic Neuritis Support Vector Machine Area Under Curve Tomography, Optical Coherence Retina
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Editorial for "Deep-Learning-Based Neural Tissue Segmentation of MRI in Multiple Sclerosis: Effect of Training Set Size".

Journal of magnetic resonance imaging : JMRI Nov 27, 2019
Humans Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Multiple Sclerosis
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Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Nov 27, 2019
We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), for quantitatively assessing white matter hyperinte...
Sensitivity and Specificity Disease Progression Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans White Matter Unsupervised Machine Learning Brain Mapping Multiple Sclerosis Image Interpretation, Computer-Assisted
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Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures: A Machine Learning Approach.

Scientific reports Nov 6, 2019
Numerous postural sway metrics have been shown to be sensitive to balance impairment and fall risk in individuals with MS. Yet, there are no guidelines concerning the most appropriate postural sway metrics to monitor impairment. This investigation im...
Sensation Disorders Adult Male Middle Aged Accidental Falls Machine Learning Risk Assessment Postural Balance Algorithms Multiple Sclerosis Humans Female Entropy Aged
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The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology Oct 28, 2019
Machine learning (ML) applied to patient-reported (PROs) and clinical-assessed outcomes (CAOs) could favour a more predictive and personalized medicine. Our aim was to confirm the important role of applying ML to PROs and CAOs of people with relapsin...
Disease Progression Severity of Illness Index Female Patient Reported Outcome Measures Outcome Assessment, Health Care Adult Humans Multiple Sclerosis Proof of Concept Study Machine Learning Male Middle Aged Prognosis
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SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis.

NeuroImage. Clinical Oct 22, 2019
Machine learning classification is an attractive approach to automatically differentiate patients from healthy subjects, and to predict future disease outcomes. A clinically isolated syndrome (CIS) is often the first presentation of multiple sclerosi...
Gray Matter Disease Progression Young Adult White Matter Follow-Up Studies Adult Adolescent Humans Magnetic Resonance Imaging Image Processing, Computer-Assisted Male Retrospective Studies Multiple Sclerosis Support Vector Machine Middle Aged Female Neuroimaging
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Deep-Learning-Based Neural Tissue Segmentation of MRI in Multiple Sclerosis: Effect of Training Set Size.

Journal of magnetic resonance imaging : JMRI Oct 18, 2019
BACKGROUND: The dependence of deep-learning (DL)-based segmentation accuracy of brain MRI on the training size is not known.
Image Processing, Computer-Assisted Magnetic Resonance Imaging Multiple Sclerosis Humans Brain Retrospective Studies Deep Learning
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Granger causality analysis in combination with directed network measures for classification of MS patients and healthy controls using task-related fMRI.

Computers in biology and medicine Oct 13, 2019
Several studies have already assessed brain network variations in multiple sclerosis (MS) patients and healthy controls (HCs). The underlying neural system's functioning is apparently too complicated, however. Therefore, the neural time series' analy...
Female Adult Male Support Vector Machine Magnetic Resonance Imaging Humans Nerve Net Models, Neurological Brain Brain Mapping Multiple Sclerosis
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Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches.

Computers in biology and medicine Oct 9, 2019
BACKGROUND: Although some studies show that there could be a genetic predisposition to develop Multiple Sclerosis (MS), attempts to find genetic signatures related to MS diagnosis and development are extremely rare.
Gene Expression Regulation Oligonucleotide Array Sequence Analysis Databases, Nucleic Acid Models, Immunological Signal Transduction Drug Repositioning Metabolic Networks and Pathways Multiple Sclerosis Retrospective Studies Female Male Humans Machine Learning
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Automated Assessment of Oral Diadochokinesis in Multiple Sclerosis Using a Neural Network Approach: Effect of Different Syllable Repetition Paradigms.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 23, 2019
Slow and irregular oral diadochokinesis represents an important manifestation of spastic and ataxic dysarthria in multiple sclerosis (MS). We aimed to develop a robust algorithm based on convolutional neural networks for the accurate detection of syl...
Neural Networks, Computer Algorithms Speech Articulation Tests Female Middle Aged Aged Humans Psychomotor Performance Reproducibility of Results Articulation Disorders Deep Learning Acoustic Stimulation Young Adult Adolescent Adult Dysarthria Multiple Sclerosis Male
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