AIMC Topic: Multiple Sclerosis
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A density-based MS disease diagnosis model using the capuchin search algorithm and an ensemble of deep neural networks.
Scientific reports
Dec 30, 2024
Multiple sclerosis (MS) is a severe brain disease that permanently destroys brain cells, impacting vision, balance, muscle control, and daily activity. This research employs a weighted combination of deep neural networks and optimization techniques f...
Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques.
BMC medical imaging
Dec 20, 2024
INTRODUCTION: Gadolinium-based T1-weighted MRI sequence is the gold standard for the detection of active multiple sclerosis (MS) lesions. The performance of machine learning (ML) and deep learning (DL) models in the classification of active and non-a...
Deep Learning techniques to detect and analysis of multiple sclerosis through MRI: A systematic literature review.
Computers in biology and medicine
Dec 17, 2024
Deep learning (DL) techniques represent a rapidly advancing field within artificial intelligence, gaining significant prominence in the detection and analysis of various medical conditions through the analysis of medical data. This study presents a s...
A Context-Dependent CNN-Based Framework for Multiple Sclerosis Segmentation in MRI.
International journal of neural systems
Dec 13, 2024
Despite several automated strategies for identification/segmentation of Multiple Sclerosis (MS) lesions in Magnetic Resonance Imaging (MRI) being developed, they consistently fall short when compared to the performance of human experts. This emphasiz...
Structural-based uncertainty in deep learning across anatomical scales: Analysis in white matter lesion segmentation.
Computers in biology and medicine
Nov 15, 2024
This paper explores uncertainty quantification (UQ) as an indicator of the trustworthiness of automated deep-learning (DL) tools in the context of white matter lesion (WML) segmentation from magnetic resonance imaging (MRI) scans of multiple sclerosi...
Automated assessment of brain MRIs in multiple sclerosis patients significantly reduces reading time.
Neuroradiology
Nov 8, 2024
INTRODUCTION: Assessment of multiple sclerosis (MS) lesions on magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. We evaluate whether assessment of new, expanding, and contrast-enhancing MS lesions can be done more time-eff...
Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.
Neurology
Nov 4, 2024
BACKGROUND AND OBJECTIVES: Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific eff...
Higher effect sizes for the detection of accelerated brain volume loss and disability progression in multiple sclerosis using deep-learning.
Computers in biology and medicine
Oct 18, 2024
PURPOSE: Clinical validation of "BrainLossNet", a deep learning-based method for fast and robust estimation of brain volume loss (BVL) from longitudinal T1-weighted MRI, for the detection of accelerated BVL in multiple sclerosis (MS) and for the disc...
Big data and artificial intelligence applied to blood and CSF fluid biomarkers in multiple sclerosis.
Frontiers in immunology
Oct 18, 2024
Artificial intelligence (AI) has meant a turning point in data analysis, allowing predictions of unseen outcomes with precedented levels of accuracy. In multiple sclerosis (MS), a chronic inflammatory-demyelinating condition of the central nervous sy...
AI-assisted assessment of fall risk in multiple sclerosis: A systematic literature review.
Multiple sclerosis and related disorders
Oct 16, 2024
BACKGROUND: Multiple sclerosis (MS) is an autoimmune disease that can increase the risk of falls in patients due to various factors. Traditional clinical assessments may not effectively identify those at risk of falling.
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