Latest AI and machine learning research in neurology for healthcare professionals.
OBJECTIVE: Deep learning provides an appealing solution for the ongoing challenge of automatically classifying intracranial interictal epileptiform discharges (IEDs). We report results from an automated method consisting of a template-matching algorithm and convolutional neural network (CNN) for the detection of intracranial IEDs ("AiED").
: Exoskeleton-robot-assisted therapy is known to positively affect the recovery of arm functions in stroke patients. However, there is a lack of evidence regarding which variables might favor a better outcome and how this can be modulated by other factors. : In this within-subject study, we evaluated the efficacy of a robot-assisted rehabilitation system in the recovery of upper limb functions. We...
Analysis of kinematic features related to clinical assessment scales may qualitatively improve the evaluation of upper extremity movements of stroke p...
Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are not based on biological markers but on behavioural tests. ...
To explore the impact of magnetic resonance imaging (MRI) image features based on deep learning algorithms on the neurological rehabilitation of patie...
As a popular machine learning method, neural networks can be used to solve many complex tasks. Their strong generalization ability comes from the repr...
Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic...
Structural magnetic resonance imaging (sMRI) can capture the spatial patterns of brain atrophy in Alzheimer's disease (AD) and incipient dementia. Rec...
Parkinson's disease (PD), Alzheimer's disease (AD) are common neurodegenerative disease, while mild cognitive impairment (MCI) may be happened in the...
Brain responsiveness to stimulation fluctuates with rapidly shifting cortical excitability state, as reflected by oscillations in the electroencephalo...
Sensorimotor adaptation involves the recalibration of the mapping between motor command and sensory feedback in response to movement errors. Although ...
Although pathology of tauopathies is characterized by abnormal tau protein aggregation in both gray and white matter regions of the brain, neuropathol...
In recent years, the research on electroencephalography (EEG) has focused on the feature extraction of EEG signals. The development of convenient and ...
Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between corti...
High accuracy has been reported in deep learning classification for amyloid brain scans, an important factor in Alzheimer's disease diagnosis. However...
OBJECTIVES: Big data analytics can potentially benefit the assessment and management of complex neurological conditions by extracting information that...
AIMS: Although morphological attributes of cells and their substructures are recognised readouts of physiological or pathophysiological states, these ...
BACKGROUND: Ultrasonography for neuraxial anesthesia is increasingly being used to identify spinal structures and the identification of correct point ...
Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification of amyotrophic latera...