Neurology

Dense convolution-based attention network for Alzheimer's disease classification.

Recently, deep learning-based medical image classification models have made substantial advancements. However, many existing models prioritize performance at the cost of efficiency, limiting their practicality in clinical use. Traditional Convolutional Neural Network (CNN)-based methods, Transformer-based methods, and hybrid approaches combining these two struggle to balance performance and model ...

Multicenter study on predicting postoperative upper limb muscle strength improvement in cervical spinal cord injury patients using radiomics and deep learning.

Cervical spinal cord injury is often catastrophic, frequently leading to irreversible impairment. MRI has become the gold standard for evaluating spinal cord injuries (SCI). Our study aimed to assess the accuracy of a radiomics approach, based on machine learning and utilizing conventional MRI, in predicting the prognosis of patients with SCI. In a retrospective analysis of 82 SCI patients from th...

A hybrid optimization-enhanced 1D-ResCNN framework for epileptic spike detection in scalp EEG signals.

In order to detect epileptic spikes, this paper suggests a deep learning architecture that blends 1D residual convolutional neural networks (1D-ResCNN...

Boostering diagnosis of frontotemporal lobar degeneration with AI-driven neuroimaging - A systematic review and meta-analysis.

BACKGROUND AND OBJECTIVES: Frontotemporal lobar degeneration (FTLD) as the second most common dementia encompasses a range of syndromes and often show...

Cultural variation in trust and acceptability of artificial intelligence diagnostics for dementia.

Digital health innovations hold diagnostic and therapeutic promise but may be subject to biases for underrepresented groups. We explored perceptions o...

Coupling analysis of diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) with abnormal cerebral blood flow in methamphetamine-dependent patients and its application in machine-learning-based classification.

BACKGROUND: Diffusion tensor imaging (DTI) analysis along the perivascular space (ALPS) index is currently widely employed to evaluate the neurophysio...

AI and early diagnostics: mapping fetal facial expressions through development, evolution, and 4D ultrasound.

The development of facial musculature and expressions in the human fetus represents a critical intersection of developmental biology, neurology, and e...

Reducing inference cost of Alzheimer's disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners.

While multi-modal deep learning approaches trained using magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG PET...

Exploring Potential Medications for Alzheimer's Disease with Psychosis by Integrating Drug Target Information into Deep Learning Models: A Data-Driven Approach.

Approximately 50% of Alzheimer's disease (AD) patients develop psychotic symptoms, leading to a subtype known as psychosis in AD (AD + P), which is as...

Hybrid-RViT: Hybridizing ResNet-50 and Vision Transformer for Enhanced Alzheimer's disease detection.

Alzheimer's disease (AD) is a leading cause of disability worldwide. Early detection is critical for preventing progression and formulating effective ...

VSR-Net: Vessel-Like Structure Rehabilitation Network With Graph Clustering.

The morphologies of vessel-like structures, such as blood vessels and nerve fibres, play significant roles in disease diagnosis, e.g., Parkinson's dis...

A combination of deep learning models and type-2 fuzzy for EEG motor imagery classification through spatiotemporal-frequency features.

Developing a robust and effective technique is crucial for interpreting a user's brainwave signals accurately in the realm of biomedical signal proces...

Taylor-dingo optimized RP-net for segmentation toward Alzheimer's disease detection and classification using deep learning.

Alzheimer's Disease (AD) is a significant cause of mortality in elderly people. The diagnosing and classification of AD using conventional manual oper...

A quantitatively interpretable model for Alzheimer's disease prediction using deep counterfactuals.

Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention in disease progression yet still demands attentive interpr...

Improving reliability of movement assessment in Parkinson's disease using computer vision-based automated severity estimation.

BackgroundClinical assessments of motor symptoms rely on observations and subjective judgments against standardized scales, leading to variability due...

Prediction of cognitive conversion within the Alzheimer's disease continuum using deep learning.

BACKGROUND: Early diagnosis and accurate prognosis of cognitive decline in Alzheimer's disease (AD) is important to timely assignment to optimal treat...

A systematic review of structural neuroimaging markers of psychotherapeutic and pharmacological treatment for obsessive-compulsive disorder.

Identifying individual difference factors associated with treatment response and putative mechanisms of therapeutic change may improve treatment for O...

Automation bias in AI-assisted detection of cerebral aneurysms on time-of-flight MR angiography.

PURPOSE: To determine how automation bias (inclination of humans to overly trust-automated decision-making systems) can affect radiologists when inter...

Prediction models for treatment response in migraine: a systematic review and meta-analysis.

BACKGROUND: Migraine is a complex neurological disorder with significant clinical variability, posing challenges for effective management. Multiple tr...

Hybrid attention-CNN model for classification of gait abnormalities using EMG scalogram images.

This research aimed to develop an algorithm for classifying scalogram images generated from electromyography data of patients with Rheumatoid Arthriti...