Neurology

A method for AI assisted human interpretation of neonatal EEG.

The study proposes a novel method to empower healthcare professionals to interact and leverage AI decision support in an intuitive manner using auditory senses. The method's suitability is assessed through acoustic detection of the presence of neonatal seizures in electroencephalography (EEG). Neurophysiologists use EEG recordings to identify seizures visually. However, neurophysiological expertis...

Effects of robot-assisted therapy on upper limb and cognitive function in patients with stroke: study protocol of a randomized controlled study.

BACKGROUND: Impairments in upper limb motor function and cognitive ability are major health problems experienced by stroke patients, necessitating the development of novel and effective treatment options in stroke care. The aim of this study is to examine the effects of robot-assisted therapy on improving upper limb and cognitive functions in stroke patients.

Prediction Model between Serum Vitamin D and Neurological Deficit in Cerebral Infarction Patients Based on Machine Learning.

OBJECTIVE: Vitamin D is associated with neurological deficits in patients with cerebral infarction. This study uses machine learning to evaluate the p...

Prediction of Lumbar Drainage-Related Meningitis Based on Supervised Machine Learning Algorithms.

BACKGROUND: Lumbar drainage is widely used in the clinic; however, forecasting lumbar drainage-related meningitis (LDRM) is limited. We aimed to estab...

DeepStroke: An efficient stroke screening framework for emergency rooms with multimodal adversarial deep learning.

In an emergency room (ER) setting, stroke triage or screening is a common challenge. A quick CT is usually done instead of MRI due to MRI's slow throu...

Deep learning based low-activity PET reconstruction of [C]PiB and [F]FE-PE2I in neurodegenerative disorders.

PURPOSE: Positron Emission Tomography (PET) can support a diagnosis of neurodegenerative disorder by identifying disease-specific pathologies. Our aim...

Drowsiness Detection Using Ocular Indices from EEG Signal.

Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utiliz...

A Machine Learning Model for Predicting Sit-to-Stand Trajectories of People with and without Stroke: Towards Adaptive Robotic Assistance.

Sit-to-stand and stand-to-sit transfers are fundamental daily motions that enable all other types of ambulation and gait. However, the ability to perf...

Stroke Risk Prediction with Machine Learning Techniques.

A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability...

Multimodal deep learning for Alzheimer's disease dementia assessment.

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed t...

A Pilot Randomized Controlled Trial of Botulinum Toxin Treatment Combined with Robot-Assisted Therapy, Mirror Therapy, or Active Control Treatment in Patients with Spasticity Following Stroke.

Effects of the combined task-oriented trainings with botulinum toxin A (BoNT-A) injection on improving motor functions and reducing spasticity remains...

EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review.

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward develop...

Influences of Magnetic Resonance Imaging Superresolution Algorithm-Based Transition Care on Prognosis of Children with Severe Viral Encephalitis.

OBJECTIVE: Its goal was to see how convolutional neural network- (CNN-) based superresolution (SR) technology magnetic resonance imaging- (MRI-) assis...

Machine learning for comprehensive prediction of high risk for Alzheimer's disease based on chromatic pupilloperimetry.

Currently there are no reliable biomarkers for early detection of Alzheimer's disease (AD) at the preclinical stage. This study assessed the pupil lig...

Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI.

Background Lumbar spine MRI studies are widely used for back pain assessment. Interpretation involves grading lumbar spinal stenosis, which is repetit...

Deep convolutional neural networks for automated scoring of pentagon copying test results.

This study aims to investigate the accuracy of a fine-tuned deep convolutional neural network (CNN) for evaluating responses to the pentagon copying t...

Value of 4D CT Angiography Combined with Whole Brain CT Perfusion Imaging Feature Analysis under Deep Learning in Imaging Examination of Acute Ischemic Stroke.

This study was aimed at investigating the application of deep learning 4D computed tomography angiography (CTA) combined with whole brain CT perfusion...

Deep learning for Alzheimer's disease diagnosis: A survey.

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a progressive decline in cognitive abilities. Since AD starts se...

DXM-TransFuse U-net: Dual cross-modal transformer fusion U-net for automated nerve identification.

Accurate nerve identification is critical during surgical procedures to prevent damage to nerve tissues. Nerve injury can cause long-term adverse effe...

Clinical validation of an AI-based motion correction reconstruction algorithm in cerebral CT.

OBJECTIVES: To evaluate the clinical performance of an artificial intelligence (AI)-based motion correction (MC) reconstruction algorithm for cerebral...