Neurology

Deep Learning-Driven Exploration of Pyrroloquinoline Quinone Neuroprotective Activity in Alzheimer's Disease.

Alzheimer's disease (AD) is a pressing concern in neurodegenerative research. To address the challenges in AD drug development, especially those targeting Aβ, this study uses deep learning and a pharmacological approach to elucidate the potential of pyrroloquinoline quinone (PQQ) as a neuroprotective agent for AD. Using deep learning for a comprehensive molecular dataset, blood-brain barrier (BBB)...

Detecting cognitive traits and occupational proficiency using EEG and statistical inference.

Machine learning (ML) is widely used in classification tasks aimed at detecting various cognitive states or neurological diseases using noninvasive electroencephalogram (EEG) time series. However, successfully detecting specific cognitive skills in a healthy population, independent of subject, remains challenging. This study compared the subject-independent classification performance of three diff...

Critical evaluation of artificial intelligence as a digital twin of pathologists for prostate cancer pathology.

Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting p...

Evaluating the performance of the cognitive workload model with subjective endorsement in addition to EEG.

The aptitude-oriented exercises from almost all domains impose cognitive load on their operators. Evaluating such load poses several challenges owing ...

Measuring functional hand use in children with unilateral cerebral palsy using accelerometry and machine learning.

AIM: To investigate wearable sensors for measuring functional hand use in children with unilateral cerebral palsy (CP).

Magnetic resonance imaging-based deep learning imaging biomarker for predicting functional outcomes after acute ischemic stroke.

PURPOSE: Clinical risk scores are essential for predicting outcomes in stroke patients. The advancements in deep learning (DL) techniques provide oppo...

Migraine headache (MH) classification using machine learning methods with data augmentation.

Migraine headache, a prevalent and intricate neurovascular disease, presents significant challenges in its clinical identification. Existing technique...

Utilizing imaging parameters for functional outcome prediction in acute ischemic stroke: A machine learning study.

BACKGROUND AND PURPOSE: We aimed to predict the functional outcome of acute ischemic stroke patients with anterior circulation large vessel occlusions...

Fast reconstruction of EEG signal compression sensing based on deep learning.

When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG signals will produce a large amount of data. At t...

Development of a deep learning model to distinguish the cause of optic disc atrophy using retinal fundus photography.

The differential diagnosis for optic atrophy can be challenging and requires expensive, time-consuming ancillary testing to determine the cause. While...

A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure.

INTRODUCTION: Epilepsy is a disease characterized by an excessive discharge in neurons generally provoked without any external stimulus, known as conv...

Data Augmentation Techniques for Accurate Action Classification in Stroke Patients with Hemiparesis.

Stroke survivors with hemiparesis require extensive home-based rehabilitation. Deep learning-based classifiers can detect actions and provide feedback...

Construction of an aerolysin-based multi-epitope vaccine against an machine learning and artificial intelligence-supported approach.

, a gram-negative coccobacillus bacterium, can cause various infections in humans, including septic arthritis, diarrhea (traveler's diarrhea), gastroe...

Development of a microultrasound-based nomogram to predict extra-prostatic extension in patients with prostate cancer undergoing robot-assisted radical prostatectomy.

OBJECTIVES: To develop a microultrasound-based nomogram including clinicopathological parameters and microultrasound findings to predict the presence ...

Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan.

BACKGROUND: The choroid plexus functions as the blood-cerebrospinal fluid (CSF) barrier, plays an important role in CSF production and circulation, an...

Computational discovery of novel FYN kinase inhibitors: a cheminformatics and machine learning-driven approach to targeted cancer and neurodegenerative therapy.

In this study, we explored the potential of novel inhibitors for FYN kinase, a critical target in cancer and neurodegenerative disorders, by integrati...

The Effects of Combined Virtual Reality Exercises and Robot Assisted Gait Training on Cognitive Functions, Daily Living Activities, and Quality of Life in High Functioning Individuals With Subacute Stroke.

Stroke is a global health concern causing significant mortality. Survivors face physical, cognitive, and emotional challenges, affecting their life sa...

Deep learning approach to improve the recognition of hand gesture with multi force variation using electromyography signal from amputees.

Variations in muscular contraction are known to significantly impact the quality of the generated EMG signal and the output decision of a proposed cla...

Multi Degree of Freedom Hybrid FES and Robotic Control of the Upper Limb.

Individuals who have suffered a spinal cord injury often require assistance to complete daily activities, and for individuals with tetraplegia, recove...

Patient portal messages to support an age-friendly health system for persons with dementia.

BACKGROUND: Patient portal secure messaging can support age-friendly dementia care, yet little is known about care partner use of the portal and how m...