Neurology

Latest AI and machine learning research in neurology for healthcare professionals.

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Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.

To transform continuous speech into words, the human brain must resolve variability across utterance...

Grade prediction of lesions in cerebral white matter using a convolutional neural network.

We established a diagnostic method for cerebral white matter lesions using MRI images and examined t...

A deep learning model of dorsal and ventral visual streams for DVSD.

Artificial intelligence (AI) methods attempt to simulate the behavior and the neural activity of the...

Test-Retest Reliability and Responsiveness of the Machine Learning-Based Short-Form of the Berg Balance Scale in Persons With Stroke.

OBJECTIVE: To examine the test-retest reliability, responsiveness, and clinical utility of the machi...

Unveiling the decision making process in Alzheimer's disease diagnosis: A case-based counterfactual methodology for explainable deep learning.

BACKGROUND: The field of Alzheimer's disease (AD) diagnosis is undergoing significant transformation...

QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG signals.

The most cost-effective data collection method is electroencephalography (EEG), which obtains meanin...

Artificial intelligence as a modality to enhance the readability of neurosurgical literature for patients.

OBJECTIVE: In this study the authors assessed the ability of Chat Generative Pretrained Transformer ...

Comparison of machine learning algorithms for automatic prediction of Alzheimer disease.

BACKGROUND: Alzheimer disease is a progressive neurological disorder marked by irreversible memory l...

A hybrid local-global neural network for visual classification using raw EEG signals.

EEG-based brain-computer interfaces (BCIs) have the potential to decode visual information. Recently...

Anchoring temporal convolutional networks for epileptic seizure prediction.

. Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigat...

Comprehensive Morphometric Analysis to Identify Key Neuroimaging Biomarkers for the Diagnosis of Adult Hydrocephalus Using Artificial Intelligence.

BACKGROUND AND OBJECTIVES: Hydrocephalus involves abnormal cerebrospinal fluid accumulation in brain...

The performance of machine learning for predicting the recurrent stroke: a systematic review and meta-analysis on 24,350 patients.

BACKGROUND: Stroke is a leading cause of death and disability worldwide. Approximately one-third of ...

Assessing polyomic risk to predict Alzheimer's disease using a machine learning model.

INTRODUCTION: Alzheimer's disease (AD) is the most common form of dementia in the elderly. Given tha...

Artificial intelligence-based prediction of neurocardiovascular risk score from retinal swept-source optical coherence tomography-angiography.

The recent rise of artificial intelligence represents a revolutionary way of improving current medic...

Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning.

Brain-machine interfaces (BMIs) aim to restore sensorimotor function to individuals suffering from n...

Depression diagnosis: EEG-based cognitive biomarkers and machine learning.

Depression is a complex mental illness that has significant effects on people as well as society. Th...

Random survival forest algorithm for risk stratification and survival prediction in gastric neuroendocrine neoplasms.

This study aimed to construct and assess a machine-learning algorithm designed to forecast survival ...

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