AIMC Topic: Multiple Sclerosis

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Artificial intelligence applied to MRI data to tackle key challenges in multiple sclerosis.

Multiple sclerosis (Houndmills, Basingstoke, England) May 13, 2024
Artificial intelligence (AI) is the branch of science aiming at creating algorithms able to carry out tasks that typically require human intelligence. In medicine, there has been a tremendous increase in AI applications thanks to increasingly powerfu...
Magnetic Resonance Imaging Multiple Sclerosis Neuroimaging Humans Artificial Intelligence
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The diagnostic performance of AI-based algorithms to discriminate between NMOSD and MS using MRI features: A systematic review and meta-analysis.

Multiple sclerosis and related disorders May 11, 2024
BACKGROUND: Magnetic resonance imaging [MRI] findings in Neuromyelitis optica spectrum disorder [NMOSD] and Multiple Sclerosis [MS] patients could lead us to discriminate toward them. For instance, U-fiber and Dawson's finger-type lesions are suggest...
Humans Diagnosis, Differential Multiple Sclerosis Neuromyelitis Optica Artificial Intelligence Algorithms Magnetic Resonance Imaging
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Improving the classification of multiple sclerosis and cerebral small vessel disease with interpretable transfer attention neural network.

Computers in biology and medicine May 1, 2024
As an autoimmune-mediated inflammatory demyelinating disease of the central nervous system, multiple sclerosis (MS) is often confused with cerebral small vessel disease (cSVD), which is a regional pathological change in brain tissue with unknown path...
Cerebral Small Vessel Diseases Middle Aged Deep Learning Magnetic Resonance Imaging Female Multiple Sclerosis Adult Image Interpretation, Computer-Assisted Humans Male Neuroimaging Neural Networks, Computer
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LST-AI: A deep learning ensemble for accurate MS lesion segmentation.

NeuroImage. Clinical Apr 29, 2024
Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segment...
Female Deep Learning Humans Multiple Sclerosis Image Processing, Computer-Assisted Adult Brain Neuroimaging Male Magnetic Resonance Imaging White Matter
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Deep learning for discrimination of active and inactive lesions in multiple sclerosis using non-contrast FLAIR MRI: A multicenter study.

Multiple sclerosis and related disorders Apr 21, 2024
BACKGROUND: Within the domain of multiple sclerosis (MS), the precise discrimination between active and inactive lesions bears immense significance. Active lesions are enhanced on T1-weighted MRI images after administration of gadolinium-based contra...
Brain Image Interpretation, Computer-Assisted Female Adult Humans Deep Learning Sensitivity and Specificity Magnetic Resonance Imaging Neural Networks, Computer Male Multiple Sclerosis Middle Aged
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Improving multiple sclerosis lesion segmentation across clinical sites: A federated learning approach with noise-resilient training.

Artificial intelligence in medicine Apr 17, 2024
Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic resonance imaging (MRI) critically informs understanding of disease progression and helps to direct therapeutic strategy. Deep learning models have shown promise for automati...
Magnetic Resonance Imaging Multiple Sclerosis Image Interpretation, Computer-Assisted Humans Deep Learning Image Processing, Computer-Assisted
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Robotic assisted and exoskeleton gait training effect in mental health and fatigue of multiple sclerosis patients. A systematic review and a meta-analysis.

Disability and rehabilitation Apr 14, 2024
PURPOSE: Robotic and Exoskeleton Assisted Gait Training (REAGT) has become the mainstream gait training module. Studies are investigating the psychosocial effects of REAGT mostly as secondary outcomes. Our systematic review and meta-analysis aims to ...
Gait Disorders, Neurologic Robotics Gait Humans Exercise Therapy Multiple Sclerosis Mental Health Fatigue Quality of Life Exoskeleton Device
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Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping.

Magma (New York, N.Y.) Apr 10, 2024
OBJECTIVE: Quantitative susceptibility mapping (QSM) provides an estimate of the magnetic susceptibility of tissue using magnetic resonance (MR) phase measurements. The tissue magnetic susceptibility (source) from the measured magnetic field distribu...
Multiple Sclerosis Magnetic Fields Magnetic Resonance Imaging Brain Mapping Humans Neural Networks, Computer Algorithms Brain Deep Learning Image Processing, Computer-Assisted
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Predicting the conversion from clinically isolated syndrome to multiple sclerosis: An explainable machine learning approach.

Multiple sclerosis and related disorders Apr 9, 2024
INTRODUCTION: Predicting the conversion of clinically isolated syndrome (CIS) to clinically definite multiple sclerosis (CDMS) is critical to personalizing treatment planning and benefits for patients. The aim of this study is to develop an explainab...
Male Middle Aged Disease Progression Mexico Follow-Up Studies Multiple Sclerosis Machine Learning Humans Magnetic Resonance Imaging Demyelinating Diseases Female Adult
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Artificial intelligence in multiple sclerosis management: Challenges in a new era.

Multiple sclerosis and related disorders Apr 8, 2024
Multiple sclerosis poses diagnostic and therapeutic challenges for healthcare professionals, with a high risk of misdiagnosis and difficulties in assessing therapeutic effectiveness. Artificial intelligence, particularly machine learning and deep neu...
Artificial Intelligence Machine Learning Multiple Sclerosis Humans
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