AIMC Topic: Multiple Sclerosis

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Analysis of static plantar pressure data with capsule networks: Diagnosing ataxia in MS patients with a deep learning-based approach.

Multiple sclerosis and related disorders
In this study, it was aimed to detect ataxia in patients with Multiple Sclerosis (MS) by utilizing static plantar pressure data and capsule networks (CapsNet), one of the deep learning (DL) architectures. CapsNet is also equipped with a robust dynami...

scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.

Genome biology
Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) da...

Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls.

Seminars in ophthalmology
Multiple sclerosis (MS) is a complex autoimmune disease characterized by inflammatory processes, demyelination, neurodegeneration, and axonal damage within the central nervous system (CNS). Retinal imaging, particularly Optical coherence tomography (...

Soft Robotics to Enhance Upper Limb Endurance in Individuals with Multiple Sclerosis.

Soft robotics
Multiple sclerosis (MS) is a chronic autoimmune disorder that affects the central nervous system and can result in various symptoms, including muscle weakness, spasticity, and fatigue, ultimately leading to the deterioration of the musculoskeletal sy...

Global and Regional Deep Learning Models for Multiple Sclerosis Stratification From MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The combination of anatomical MRI and deep learning-based methods such as convolutional neural networks (CNNs) is a promising strategy to build predictive models of multiple sclerosis (MS) prognosis. However, studies assessing the effect ...

Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorder using a deep learning model.

Scientific reports
Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are autoimmune inflammatory disorders of the central nervous system (CNS) with similar characteristics. The differential diagnosis between MS and NMOSD is critical for initiat...

The macular retinal ganglion cell layer as a biomarker for diagnosis and prognosis in multiple sclerosis: A deep learning approach.

Acta ophthalmologica
PURPOSE: The macular ganglion cell layer (mGCL) is a strong potential biomarker of axonal degeneration in multiple sclerosis (MS). For this reason, this study aims to develop a computer-aided method to facilitate diagnosis and prognosis in MS.

Boosting multiple sclerosis lesion segmentation through attention mechanism.

Computers in biology and medicine
Magnetic resonance imaging is a fundamental tool to reach a diagnosis of multiple sclerosis and monitoring its progression. Although several attempts have been made to segment multiple sclerosis lesions using artificial intelligence, fully automated ...

Deep learning-based PET/MR radiomics for the classification of annualized relapse rate in multiple sclerosis.

Multiple sclerosis and related disorders
Background Annualized Relapse Rate (ARR) is one of the most important indicators of disease progression in patients with Multiple Sclerosis (MS). However, imaging markers that can effectively predict ARR are currently unavailable. In this study, we d...