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Multiple Sclerosis

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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...

Role of artificial intelligence in multiple sclerosis management.

European review for medical and pharmacological sciences
From a clinical viewpoint, there are enormous obstacles to early detection and diagnosis as well as treatment interventions for multiple sclerosis (MS). With the growing application of methods based on artificial intelligence (AI) to medical problems...

Diagnostic effectiveness of deep learning-based MRI in predicting multiple sclerosis: A meta-analysis.

Neurosciences (Riyadh, Saudi Arabia)
OBJECTIVES: The brain and spinal cord, constituting the central nervous system (CNS), could be impacted by an inflammatory disease known as multiple sclerosis (MS). The convolutional neural networks (CNN), a machine learning method, can detect lesion...

User-Centered Configuration of Soft Hip Flexion Exosuit Designs to Assist Individuals with Multiple Sclerosis Through Simulated Human-in-the-Loop Optimization.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Soft exosuits hold promise as assistive technology for people with gait deficits owing to a variety of causes. A key aspect of providing useful assistance is to keep the human user at the center of all considerations made in the design, configuration...

Deep Learning for Multiple Sclerosis Differentiation Using Multi-Stride Dynamics in Gait.

IEEE transactions on bio-medical engineering
OBJECTIVE: Multiple sclerosis (MS) is a chronic neurological condition of the central nervous system leading to various physical, mental and psychiatric complexities. Mobility limitations are amongst the most frequent and early markers of MS. We eval...

White Matter Lesion Segmentation for Multiple Sclerosis Patients implementing deep learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The aim of this work is to address the problem of White Matter Lesion (WML) segmentation employing Magnetic Resonance Imaging (MRI) images from Multiple Sclerosis (MS) patients through the application of deep learning. A U-net based architecture cont...

[What worries people with multiple sclerosis in Russia? Semantic analysis of patient messages using artificial intelligence tools].

Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova
OBJECTIVE: To study the needs of patients suffering from multiple sclerosis (MS) in Russia.

Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches.

Briefings in bioinformatics
During the course of a viral infection, virus-host protein-protein interactions (PPIs) play a critical role in allowing viruses to replicate and survive within the host. These interspecies molecular interactions can lead to viral-mediated perturbatio...

SGANRDA: semi-supervised generative adversarial networks for predicting circRNA-disease associations.

Briefings in bioinformatics
Emerging research shows that circular RNA (circRNA) plays a crucial role in the diagnosis, occurrence and prognosis of complex human diseases. Compared with traditional biological experiments, the computational method of fusing multi-source biologica...

Deep Learning on Conventional Magnetic Resonance Imaging Improves the Diagnosis of Multiple Sclerosis Mimics.

Investigative radiology
OBJECTIVES: The aims of this study were to present a deep learning approach for the automated classification of multiple sclerosis and its mimics and compare model performance with that of 2 expert neuroradiologists.