Neurology

Factors Associated With Nonunion After Cervical Fusion Surgery.

Background Bony fusion is a critical factor in the outcome of cervical spinal fusion surgery. While ...

Development of a Deep-Learning Model for Diagnosing Lumbar Spinal Stenosis Based on CT Images.

STUDY DESIGN: Retrospective study.

Bilateral upper limb robot-assisted rehabilitation improves upper limb motor function in stroke patients: a study based on quantitative EEG.

BACKGROUND: Upper limb dysfunction after stroke seriously affects quality of life. Bilateral trainin...

A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients.

BACKGROUND: Haemorrhage transformation (HT) is a serious complication of intravenous thrombolysis (I...

The perils and promises of generative artificial intelligence in neurointerventional surgery.

Generative artificial intelligence (AI) holds great promise in neurointerventional surgery by provid...

Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT.

BACKGROUND: Automated measurement of the Alberta Stroke Program Early Computed Tomography Score (ASP...

Co-existence of Type 1 Diabetes Mellitus and Myasthenia Gravis: A Case Report and Review of the Literature.

BACKGROUND/OBJECTIVE: Type 1 diabetes (T1D) and myasthenia gravis (MG) are autoimmune conditions tha...

scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.

Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learnin...

Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals.

Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their ...

Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms.

The clinical applications of brain age prediction have expanded, particularly in anticipating the on...

A Short Review on the Impact of Artificial Intelligence in Diagnosis Diseases: Role of Radiomics In Neuro-Oncology.

Artificial Intelligence (AI) is rapidly transforming various aspects of healthcare, including the fi...

Artificial intelligence in glaucoma detection using color fundus photographs.

PURPOSE: To explore the potential of artificial intelligence (AI) for glaucoma detection using deep ...

Artificial intelligence-powered intraoperative nerve monitoring: a visionary method to reduce facial nerve palsy in parotid surgery: an editorial.

Electrophysiological brain imaging based on simulation-driven deep learning in the context of epilepsy.

Identifying the location, the spatial extent and the electrical activity of distributed brain source...

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data.

Large omics datasets are becoming increasingly available for research into human health. This paper ...

Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?

In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signal...

An overview of machine learning and deep learning techniques for predicting epileptic seizures.

Epilepsy is a neurological disorder (the third most common, following stroke and migraines). A key a...

Automating General Movements Assessment with quantitative deep learning to facilitate early screening of cerebral palsy.

The Prechtl General Movements Assessment (GMA) is increasingly recognized for its role in evaluating...

Artificial intelligence in neuro-oncology.

Artificial intelligence (AI) describes the application of computer algorithms to the solution of pro...

Stroke classification and treatment support system artificial intelligence for usefulness of stroke diagnosis.

BACKGROUND AND AIMS: It is important to diagnose cerebral infarction at an early stage and select an...

Deep Unsupervised Representation Learning for Feature-Informed EEG Domain Extraction.

In electroencephalography (EEG) classification paradigms, data from a target subject is often diffic...