AIMC Topic: Multiple Sclerosis

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Application of Artificial Neural Network for Prediction of Risk of Multiple Sclerosis Based on Single Nucleotide Polymorphism Genotypes.

Journal of molecular neuroscience : MN Mar 9, 2020
The artificial neural network (ANN) is a sort of machine learning method which has been used in determination of risk of human disorders. In the current investigation, we have created an ANN and trained it based on the genetic data of 401 multiple sc...
Polymorphism, Single Nucleotide Genotype Multiple Sclerosis Adult Male Genome-Wide Association Study Humans Neural Networks, Computer Software Female
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CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis.

NMR in biomedicine Mar 3, 2020
The central vein sign (CVS) is an efficient imaging biomarker for multiple sclerosis (MS) diagnosis, but its application in clinical routine is limited by inter-rater variability and the expenditure of time associated with manual assessment. We descr...
Humans Multiple Sclerosis Automation White Matter Veins Machine Learning Software Imaging, Three-Dimensional Magnetic Resonance Imaging
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Multimodal Image Analysis for Assessing Multiple Sclerosis and Future Prospects Powered by Artificial Intelligence.

Seminars in ultrasound, CT, and MR Feb 29, 2020
The purpose of this paper is to serve as a template for greater understanding for the practicing radiologist about key steps to perform multimodality computer analysis of MRI images, specifically in multiple sclerosis patients. With this understandin...
Magnetic Resonance Imaging Humans Multimodal Imaging Artificial Intelligence Multiple Sclerosis Brain Image Interpretation, Computer-Assisted
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Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction.

NeuroImage Jan 22, 2020
Deep learning (DL) is increasingly used to solve ill-posed inverse problems in medical imaging, such as reconstruction from noisy and/or incomplete data, as DL offers advantages over conventional methods that rely on explicit image features and hand ...
Humans Brain Computer Simulation Adult Deep Learning Image Processing, Computer-Assisted Neuroimaging Magnetic Resonance Imaging Cerebral Hemorrhage Multiple Sclerosis
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Antioxidant and Anti-inflammatory Diagnostic Biomarkers in Multiple Sclerosis: A Machine Learning Study.

Molecular neurobiology Jan 22, 2020
An imbalance of inflammatory/anti-inflammatory and oxidant/antioxidant molecules has been implicated in the demyelination and axonal damage in multiple sclerosis (MS). The current study aimed to evaluate the plasma levels of tumor necrosis factor (TN...
Sulfhydryl Compounds Multiple Sclerosis Adiponectin Advanced Oxidation Protein Products Inflammation Support Vector Machine Oxidation-Reduction Nitrosative Stress Receptors, Tumor Necrosis Factor Adult Sensitivity and Specificity Male Middle Aged Oxidative Stress Antioxidants Nitric Oxide Neural Networks, Computer Tumor Necrosis Factor-alpha Biomarkers Female Humans
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Myelin water imaging data analysis in less than one minute.

NeuroImage Jan 21, 2020
PURPOSE: Based on a deep learning neural network (NN) algorithm, a super fast and easy to implement data analysis method was proposed for myelin water imaging (MWI) to calculate the myelin water fraction (MWF).
Adult Body Water Male Neuroimaging Middle Aged Deep Learning Magnetic Resonance Imaging Humans Feasibility Studies Brain Multiple Sclerosis Female Myelin Sheath
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Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis.

European radiology Jan 3, 2020
OBJECTIVES: Patients with multiple sclerosis (MS) regularly undergo MRI for assessment of disease burden. However, interpretation may be time consuming and prone to intra- and interobserver variability. Here, we evaluate the potential of artificial n...
Brain Female Reproducibility of Results Adult Humans Neural Networks, Computer Male Multiple Sclerosis Middle Aged Magnetic Resonance Imaging
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A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis.

NeuroImage. Clinical Dec 28, 2019
INTRODUCTION: Longitudinal magnetic resonance imaging (MRI) has an important role in multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new T2-w lesions on brain MR scans is considered a predictive biomarker for the diseas...
Longitudinal Studies Magnetic Resonance Imaging Humans Multiple Sclerosis Biomarkers Neuroimaging Brain Image Interpretation, Computer-Assisted Male Female Middle Aged Adult Deep Learning
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Deep learning segmentation of orbital fat to calibrate conventional MRI for longitudinal studies.

NeuroImage Dec 9, 2019
In conventional non-quantitative magnetic resonance imaging, image contrast is consistent within images, but absolute intensity can vary arbitrarily between scans. For quantitative analysis of intensity data, images are typically normalized to a cons...
Calibration Demyelinating Autoimmune Diseases, CNS Adipose Tissue Male Orbit Image Processing, Computer-Assisted Deep Learning Multiple Sclerosis Longitudinal Studies Neuroimaging Adolescent Magnetic Resonance Imaging Child Humans Brain Female
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Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence.

NeuroImage. Clinical Dec 9, 2019
The detection of new or enlarged white-matter lesions is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-...
Image Interpretation, Computer-Assisted Deep Learning Neuroimaging Longitudinal Studies Humans Magnetic Resonance Imaging Brain Multiple Sclerosis Adult
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