AIMC Topic: Multiple Sclerosis
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Interpretable deep learning as a means for decrypting disease signature in multiple sclerosis.
Journal of neural engineering
Jul 19, 2021
The mechanisms driving multiple sclerosis (MS) are still largely unknown, calling for new methods allowing to detect and characterize tissue degeneration since the early stages of the disease. Our aim is to decrypt the microstructural signatures of t...
Robot-Assisted Gait Training in Patients with Multiple Sclerosis: A Randomized Controlled Crossover Trial.
Medicina (Kaunas, Lithuania)
Jul 14, 2021
Gait disorders represent one of the most disabling aspects in multiple sclerosis (MS) that strongly influence patient quality of life. The improvement of walking ability is a primary goal for rehabilitation treatment. The aim of this study is to eva...
Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones.
Scientific reports
Jul 12, 2021
The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and ...
Lesion probability mapping in MS patients using a regression network on MR fingerprinting.
BMC medical imaging
Jul 8, 2021
BACKGROUND: To develop a regression neural network for the reconstruction of lesion probability maps on Magnetic Resonance Fingerprinting using echo-planar imaging (MRF-EPI) in addition to [Formula: see text], [Formula: see text], NAWM, and GM- proba...
Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression.
Computer methods and programs in biomedicine
May 18, 2021
BACKGROUND AND OBJECTIVES: Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more infor...
Classification Criteria for Multiple Sclerosis-Associated Intermediate Uveitis.
American journal of ophthalmology
May 11, 2021
PURPOSE: The purpose of this study was to determine classification criteria for multiple sclerosis-associated intermediate uveitis.
Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis.
IEEE journal of biomedical and health informatics
May 11, 2021
Falls are a significant problem for persons with multiple sclerosis (PwMS). Yet fall prevention interventions are not often prescribed until after a fall has been reported to a healthcare provider. While still nascent, objective fall risk assessments...
Digital Twins for Multiple Sclerosis.
Frontiers in immunology
May 3, 2021
An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adj...
Prediction of Multiple sclerosis disease using machine learning classifiers: a comparative study.
Journal of preventive medicine and hygiene
Apr 29, 2021
INTRODUCTION: Hamedan Province is one of Iran's high-risk regions for Multiple Sclerosis (MS). Early diagnosis of MS based on an accurate system can control the disease. The aim of this study was to compare the performance of four machine learning te...
Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data.
Nature communications
Apr 6, 2021
Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features ...
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