Neurology

Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach.

Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional measures and increase diagnostic confidence. Yet, widespread application of these tools cannot be recommended unless they are proven to perform consistent...

A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method.

Electroencephalography (EEG) signals have been widely used to diagnose brain diseases for instance epilepsy, Parkinson's Disease (PD), Multiple Skleroz (MS), and many machine learning methods have been proposed to develop automated disease diagnosis methods using EEG signals. In this method, a multilevel machine learning method is presented to diagnose epilepsy disease. The proposed multilevel EEG...

Using machine learning models to improve stroke risk level classification methods of China national stroke screening.

BACKGROUND: With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in Chi...

Deep learning segmentation of orbital fat to calibrate conventional MRI for longitudinal studies.

In conventional non-quantitative magnetic resonance imaging, image contrast is consistent within images, but absolute intensity can vary arbitrarily b...

Perspectives on the current developments with neuromodulation for the treatment of epilepsy.

: As deep brain stimulation revolutionized the treatment of movement disorders in the late 80s, neuromodulation in the treatment of epilepsy will undo...

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greates...

Robot-assisted versus manual navigated stereoelectroencephalography in adult medically-refractory epilepsy patients.

OBJECTIVE: Stereoelectroencephalography (SEEG) has experienced a recent growth in adoption for epileptogenic zone (EZ) localization. Advances in robot...

A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images.

Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis a...

Detecting glaucoma based on spectral domain optical coherence tomography imaging of peripapillary retinal nerve fiber layer: a comparison study between hand-crafted features and deep learning model.

PURPOSE: To develop a deep learning (DL) model for automated detection of glaucoma and to compare diagnostic capability against hand-craft features (H...

Development of a Data Logger for Capturing Human-Machine Interaction in Wheelchair Head-Foot Steering Sensor System in Dyskinetic Cerebral Palsy.

The use of data logging systems for capturing wheelchair and user behavior has increased rapidly over the past few years. Wheelchairs ensure more inde...

The NAD-mitophagy axis in healthy longevity and in artificial intelligence-based clinical applications.

Nicotinamide adenine dinucleotide (NAD) is an important natural molecule involved in fundamental biological processes, including the TCA cycle, OXPHOS...

Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches.

Aβ-amyloid deposition is a key feature of Alzheimer's disease, but Consortium to Establish a Registry for Alzheimer's Disease (CERAD) assessment, base...

Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson's Disease.

Early diagnosis of Parkinson's diseases (PD) is challenging; applying machine learning (ML) models to gait characteristics may support the classificat...

Machine learning: assessing neurovascular signals in the prefrontal cortex with non-invasive bimodal electro-optical neuroimaging in opiate addiction.

Chronic and recurrent opiate use injuries brain tissue and cause serious pathophysiological changes in hemodynamic and subsequent inflammatory respons...

Machine Learning to Detect Alzheimer's Disease from Circulating Non-coding RNAs.

Blood-borne small non-coding (sncRNAs) are among the prominent candidates for blood-based diagnostic tests. Often, high-throughput approaches are appl...

Recurrent ischemic stroke in patients with atrial fibrillation ablation and prior stroke: A study based on etiological classification.

BACKGROUND: Different subtypes of ischemic stroke may have different risk factors, clinical features, and prognoses. This study investigated the incid...

Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network-Feasibility Study.

Electroencephalography (EEG) has relatively poor spatial resolution and may yield incorrect brain dynamics and distort topography; thus, high-density ...

Computer-Aided Diagnosis of Multiple Sclerosis Using a Support Vector Machine and Optical Coherence Tomography Features.

The purpose of this paper is to evaluate the feasibility of diagnosing multiple sclerosis (MS) using optical coherence tomography (OCT) data and a sup...

A fluoroscopic assessment of brachial plexus block by the supraclavicular approach: Have we been overmedicating?

BACKGROUND: Ultrasonography-guided supraclavicular brachial plexus block has demonstrated safety as compared with landmark or nerve stimulation techni...

Deep Learning Approach for Epileptic Focus Localization.

The task of epileptic focus localization receives great attention due to its role in an effective epileptic surgery. The clinicians highly depend on t...