Neurology

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.

Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promising tool for inspecting the neural correlates of depression and therefore, has the potential to contribute to the diagnosis of depression effectively....

Artificial intelligence-based video monitoring of movement disorders in the elderly: a review on current and future landscapes.

Due to global ageing, the burden of chronic movement and neurological disorders (Parkinson's disease and essential tremor) is rapidly increasing. Current diagnosis and monitoring of these disorders rely largely on face-to-face assessments utilising clinical rating scales, which are semi-subjective and time-consuming. To address these challenges, the utilisation of artificial intelligence (AI) has ...

Machine learning-aided search for ligands of P2Y and other P2Y receptors.

The P2Y receptor, activated by uridine diphosphate (UDP), is a target for antagonists in inflammatory, neurodegenerative, and metabolic disorders, yet...

Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease.

BACKGROUND: Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clini...

A large language model-based generative natural language processing framework fine-tuned on clinical notes accurately extracts headache frequency from electronic health records.

OBJECTIVE: To develop a natural language processing (NLP) algorithm that can accurately extract headache frequency from free-text clinical notes.

Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer's Disease Using Biophysical Modeling and Deep Learning.

Alzheimer's disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progre...

Applying Common Spatial Pattern and Convolutional Neural Network to Classify Movements via EEG Signals.

Developing an electroencephalography (EEG)-based brain-computer interface (BCI) system is crucial to enhancing the control of external prostheses by a...

Utilizing Machine Learning to Identify Biomarkers of Endoplasmic Reticulum Stress and Analyze Immune Cell Infiltration in Parkinson's Disease.

The neurodegenerative disorder known as Parkinson's disease (PD) affects many people. The objective of this investigation was to examine the relations...

Machine learning models help differentiate between causes of recurrent spontaneous vertigo.

BACKGROUND: Vestibular migraine (VM) and Menière's disease (MD) are two common causes of recurrent spontaneous vertigo. Using history, video-nystagmog...

Deep learning approaches for seizure video analysis: A review.

Seizure events can manifest as transient disruptions in the control of movements which may be organized in distinct behavioral sequences, accompanied ...

Machine Learning Analysis Using RNA Sequencing to Distinguish Neuromyelitis Optica from Multiple Sclerosis and Identify Therapeutic Candidates.

This study aims to identify RNA biomarkers distinguishing neuromyelitis optica (NMO) from relapsing-remitting multiple sclerosis (RRMS) and explore po...

Development of Machine Learning Algorithm to Predict the Risk of Incontinence After Robot-Assisted Radical Prostatectomy.

Predicting postoperative incontinence beforehand is crucial for intensified and personalized rehabilitation after robot-assisted radical prostatectom...

Diagnosing glaucoma in primary eye care and the role of Artificial Intelligence applications for reducing the prevalence of undetected glaucoma in Australia.

Glaucoma is the commonest cause of irreversible blindness worldwide, with over 70% of people affected remaining undiagnosed. Early detection is crucia...

The promise of AI Large Language Models for Epilepsy care.

Artificial intelligence (AI) has been supporting our digital life for decades, but public interest in this has exploded with the recognition of large ...

A hybrid EEG classification model using layered cascade deep learning architecture.

The problem of multi-class classification is always a challenge in the field of EEG (electroencephalogram)-based seizure detection. The traditional st...

eDeeplepsy: An artificial neural framework to reveal different brain states in children with epileptic spasms.

OBJECTIVE: Despite advances, analysis and interpretation of EEG still essentially rely on visual inspection by a super-specialized physician. Consider...

Artificial intelligence-driven drug repositioning uncovers efavirenz as a modulator of α-synuclein propagation: Implications in Parkinson's disease.

Parkinson's disease (PD) is a complex neurodegenerative disorder with an unclear etiology. Despite significant research efforts, developing disease-mo...

A deep learning method to identify and localize large-vessel occlusions from cerebral digital subtraction angiography.

BACKGROUND AND PURPOSE: An essential step during endovascular thrombectomy is identifying the occluded arterial vessel on a cerebral digital subtracti...

Kinetic Ensemble of Tau Protein through the Markov State Model and Deep Learning Analysis.

The ordered assembly of Tau protein into filaments characterizes Alzheimer's and other neurodegenerative diseases, and thus, stabilization of Tau prot...

Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human-robot collaboration.

. Brain-computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment whe...