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...
BACKGROUND: The European Cross-Cultural Neuropsychological Test Battery (CNTB) has been proposed as a comprehensive battery for cognitive assessment, reducing the potential impact of cultural variables. In this validation study, we aimed to evaluate ...
Use of techniques derived from generative artificial intelligence (AI), specifically large language models (LLMs), offer a transformative potential on the management of multiple sclerosis (MS). Recent LLMs have exhibited remarkable skills in producin...
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...
Artificial intelligence (AI) models often face performance drops after deployment to external datasets. This study evaluated the potential of a novel data augmentation framework based on generative adversarial networks (GANs) that creates synthetic p...
This review investigated the effectiveness of robotic-assisted gait training (RAGT) in improving gait and balance performance in adults with multiple sclerosis (MS). Databases and registers were searched from inception to December 2023 to identify ra...
Journal of the American Medical Informatics Association : JAMIA
39348270
OBJECTIVES: This article describes the design and evaluation of MS Pattern Explorer, a novel visual tool that uses interactive machine learning to analyze fitness wearables' data. Applied to a clinical study of multiple sclerosis (MS) patients, the t...
Multiple sclerosis (MS) remains a challenging neurological condition for diagnosis and management and is often detected in late stages, delaying treatment. Artificial intelligence (AI) is emerging as a promising approach to extracting MS information ...
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.
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...