AIMC Topic: Multiple Sclerosis
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Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.
PloS one
Sep 1, 2021
Gadolinium-enhancing lesions reflect active disease and are critical for in-patient monitoring in multiple sclerosis (MS). In this work, we have developed the first fully automated method to segment and count the gadolinium-enhancing lesions from rou...
Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images.
Frontiers in immunology
Aug 11, 2021
Multiple sclerosis (MS) is one of the most common autoimmune diseases which is commonly diagnosed and monitored using magnetic resonance imaging (MRI) with a combination of clinical manifestations. The purpose of this review is to highlight the main ...
A pipeline to quantify spinal cord atrophy with deep learning: Application to differentiation of MS and NMOSD patients.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Aug 2, 2021
PURPOSE: Quantitative measurement of various anatomical regions of the brain and spinal cord (SC) in MRI images are used as unique biomarkers to consider progress and effects of demyelinating diseases of the central nervous system. This paper present...
Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review.
Computers in biology and medicine
Jul 31, 2021
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed ...
The path to precision medicine for MS, from AI to patient recruitment: an interview with Mauricio Farez and Helen Onuorah.
Communications biology
Jul 22, 2021
This year’s World Brain Day is focused on stopping Multiple Sclerosis (MS). Although amazing progress has resulted in the development of relatively successful MS therapies, access to such therapies is a major problem for most of the world. In additio...
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...
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