AI Medical Compendium Journal:
Multiple sclerosis and related disorders

Showing 11 to 20 of 21 articles

Predicting the conversion from clinically isolated syndrome to multiple sclerosis: An explainable machine learning approach.

Multiple sclerosis and related disorders
INTRODUCTION: Predicting the conversion of clinically isolated syndrome (CIS) to clinically definite multiple sclerosis (CDMS) is critical to personalizing treatment planning and benefits for patients. The aim of this study is to develop an explainab...

Artificial intelligence in multiple sclerosis management: Challenges in a new era.

Multiple sclerosis and related disorders
Multiple sclerosis poses diagnostic and therapeutic challenges for healthcare professionals, with a high risk of misdiagnosis and difficulties in assessing therapeutic effectiveness. Artificial intelligence, particularly machine learning and deep neu...

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...

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...

Reliability, validity and clinical usability of a robotic assessment of finger proprioception in persons with multiple sclerosis.

Multiple sclerosis and related disorders
BACKGROUND: Multiple sclerosis often leads to proprioceptive impairments of the hand. However, it is challenging to objectively assess such deficits using clinical methods, thereby also impeding accurate tracking of disease progression and hence the ...

Do people with multiple sclerosis perceive upper limb improvements from robotic-mediated therapy? A mixed methods study.

Multiple sclerosis and related disorders
BACKGROUND: Robot-mediated training is increasingly considered as a rehabilitation intervention targeting upper limb disability. However, experiences of such an intervention have been rarely explored in the multiple sclerosis population. This mixed m...

Artificial intelligence in the diagnosis of multiple sclerosis: A systematic review.

Multiple sclerosis and related disorders
BACKGROUND: In recent years Artificial intelligence (AI) techniques are rapidly evolving into clinical practices such as diagnosis and prognosis processes, assess treatment effectiveness, and monitoring of diseases. The previous studies showed intere...

Detection of ataxia in low disability MS patients by hybrid convolutional neural networks based on images of plantar pressure distribution.

Multiple sclerosis and related disorders
BACKGROUND: This study aimed to detect ataxia in patients with multiple sclerosis (PwMS) with a deep learning-based approach based on images showing plantar pressure distribution of the patients. The secondary aim of the study was to investigate an a...

Functional recovery in multiple sclerosis patients undergoing rehabilitation programs is associated with plasma levels of hemostasis inhibitors.

Multiple sclerosis and related disorders
BACKGROUND: Increasing evidence for contribution of hemostasis components in multiple sclerosis (MS) has been reported. Hemostasis protein inhibitors display key regulatory roles, extending to regulation of innate immune response and inflammation, an...