AIMC Topic: Amyotrophic Lateral Sclerosis
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Artificial intelligence for automatic classification of needle EMG signals: A scoping review.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Jan 3, 2024
OBJECTIVE: This scoping review provides an overview of artificial intelligence (AI), including machine and deep learning techniques, in the interpretation of clinical needle electromyography (nEMG) signals.
A Machine Learning Approach for Highlighting microRNAs as Biomarkers Linked to Amyotrophic Lateral Sclerosis Diagnosis and Progression.
Biomolecules
Dec 29, 2023
Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive loss of motor neurons in the brain and spinal cord. The early diagnosis of ALS can be challenging, as it usually depends on clinical examination...
The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions.
Muscle & nerve
Dec 27, 2023
The rapid advancements in artificial intelligence (AI), including machine learning (ML), and deep learning (DL) have ushered in a new era of technological breakthroughs in healthcare. These technologies are revolutionizing the way we utilize medical ...
A Brain-Controlled Quadruped Robot: A Proof-of-Concept Demonstration.
Sensors (Basel, Switzerland)
Dec 22, 2023
Coupling brain-computer interfaces (BCIs) and robotic systems in the future can enable seamless personal assistant systems in everyday life, with the requests that can be performed in a discrete manner, using one's brain activity only. These types of...
A deep learning-based telemonitoring application to automatically assess oral diadochokinesis in patients with bulbar amyotrophic lateral sclerosis.
Computer methods and programs in biomedicine
Oct 5, 2023
BACKGROUND AND OBJECTIVES: Timely identification of dysarthria progression in patients with bulbar-onset amyotrophic lateral sclerosis (ALS) is relevant to have a comprehensive assessment of the disease evolution. To this goal literature recognized t...
Restoring arm function with a soft robotic wearable for individuals with amyotrophic lateral sclerosis.
Science translational medicine
Feb 1, 2023
Despite promising results in the rehabilitation field, it remains unclear whether upper limb robotic wearables, e.g., for people with physical impairments resulting from neurodegenerative disease, can be made portable and suitable for everyday use. W...
Deep learning methods to predict amyotrophic lateral sclerosis disease progression.
Scientific reports
Aug 12, 2022
Amyotrophic lateral sclerosis (ALS) is a highly complex and heterogeneous neurodegenerative disease that affects motor neurons. Since life expectancy is relatively low, it is essential to promptly understand the course of the disease to better target...
eyeSay: Brain Visual Dynamics Decoding With Deep Learning & Edge Computing.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 11, 2022
Brain visual dynamics encode rich functional and biological patterns of the neural system, and if decoded, are of great promise for many applications such as intention understanding, cognitive load quantization and neural disorder measurement. We her...
Robot-assisted training using hybrid assistive limb ameliorates gait ability in patients with amyotrophic lateral sclerosis.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Mar 10, 2022
OBJECTIVE: The Hybrid Assistive Limb (HAL; CYBERDYNE, Inc., Japan) is a wearable robot device that provides effective gait assistance according to voluntary intention by detecting weak bioelectrical signals of neuromuscular activity on the surface of...
Image-based deep learning reveals the responses of human motor neurons to stress and VCP-related ALS.
Neuropathology and applied neurobiology
Oct 18, 2021
AIMS: Although morphological attributes of cells and their substructures are recognised readouts of physiological or pathophysiological states, these have been relatively understudied in amyotrophic lateral sclerosis (ALS) research.
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