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Multiple Sclerosis

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A future of AI-driven personalized care for people with multiple sclerosis.

Frontiers in immunology
Multiple sclerosis (MS) is a devastating immune-mediated disorder of the central nervous system resulting in progressive disability accumulation. As there is no cure available yet for MS, the primary therapeutic objective is to reduce relapses and to...

A machine learning approach to determine the risk factors for fall in multiple sclerosis.

BMC medical informatics and decision making
BACKGROUND: Falls in multiple sclerosis can result in numerous problems, including injuries and functional loss. Therefore, determining the factors contributing to falls in people with Multiple Sclerosis (PwMS) is crucial. This study aims to investig...

Detection of diffusely abnormal white matter in multiple sclerosis on multiparametric brain MRI using semi-supervised deep learning.

Scientific reports
In addition to focal lesions, diffusely abnormal white matter (DAWM) is seen on brain MRI of multiple sclerosis (MS) patients and may represent early or distinct disease processes. The role of MRI-observed DAWM is understudied due to a lack of automa...

An analytical review on the use of artificial intelligence and machine learning in diagnosis, prediction, and risk factor analysis of multiple sclerosis.

Multiple sclerosis and related disorders
Medical research offers potential for disease prediction, like Multiple Sclerosis (MS). This neurological disorder damages nerve cell sheaths, with treatments focusing on symptom relief. Manual MS detection is time-consuming and error prone. Though M...

SLO-Net: Enhancing Multiple Sclerosis Diagnosis Beyond Optical Coherence Tomography Using Infrared Reflectance Scanning Laser Ophthalmoscopy Images.

Translational vision science & technology
PURPOSE: Several machine learning studies have used optical coherence tomography (OCT) for multiple sclerosis (MS) classification with promising outcomes. Infrared reflectance scanning laser ophthalmoscopy (IR-SLO) captures high-resolution fundus ima...

An extensible and unifying approach to retrospective clinical data modeling: the BrainTeaser Ontology.

Journal of biomedical semantics
Automatic disease progression prediction models require large amounts of training data, which are seldom available, especially when it comes to rare diseases. A possible solution is to integrate data from different medical centres. Nevertheless, vari...

Generative artificial intelligence versus clinicians: Who diagnoses multiple sclerosis faster and with greater accuracy?

Multiple sclerosis and related disorders
BACKGROUND: Those receiving the diagnosis of multiple sclerosis (MS) over the next ten years will predominantly be part of Generation Z (Gen Z). Recent observations within our clinic suggest that younger people with MS utilize online generative artif...

Machine learning-driven diagnosis of multiple sclerosis from whole blood transcriptomics.

Brain, behavior, and immunity
Multiple sclerosis (MS) is a neurological disorder characterized by immune dysregulation. It begins with a first clinical manifestation, a clinically isolated syndrome (CIS), which evolves to definite MS in case of further clinical and/or neuroradiol...

Prediction of Expanded Disability Status Scale in patients with MS using deep learning.

Computers in biology and medicine
Multiple sclerosis (MS) is a chronic neurological condition that leads to significant disability in patients. Accurate prediction of disease progression, specifically the Expanded Disability Status Scale (EDSS), is crucial for personalizing treatment...

Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.

Journal of neurology
Multiple sclerosis (MS) is a demyelinating neurological disorder with a highly heterogeneous clinical presentation and course of progression. Disease-modifying therapies are the only available treatment, as there is no known cure for the disease. Car...