Neurology

A Convolutional Mixer-Based Deep Learning Network for Alzheimer's Disease Classification from Structural Magnetic Resonance Imaging.

Alzheimer's disease (AD) is a neurodegenerative disorder that severely impairs cognitive function across various age groups, ranging from early to late sixties. It progresses from mild to severe stages, so an accurate diagnostic tool is necessary for effective intervention and treatment planning. This work proposes a novel AD classification architecture that integrates depthwise separable convol...

Advancements in Machine Learning for Precision Diagnostics and Surgical Interventions in Interconnected Musculoskeletal and Visual Systems.

Artificial intelligence (AI) is reshaping precision medicine by revealing diagnostic links between ocular biomarkers and systemic musculoskeletal disorders. This review synthesizes clinical evidence on the associations between optical coherence tomography (OCT)-derived parameters, such as retinal nerve fiber layer (RNFL) thinning and choroidal thickness, and conditions including osteoporosis, cerv...

Development and validation of a machine-learning model for the risk of potentially inappropriate medications in elderly stroke patients.

OBJECTIVE: To construct a risk prediction model for potentially inappropriate medications (PIM) in elderly stroke patients based on multiple machine-l...

From Visualization to Automation: A Narrative Review of Deep Learning's Impact on Ultrasound-based Median Nerve Assessment.

Ultrasound imaging has emerged as a valuable tool in the diagnosis and management of peripheral nerve disorders. The integration of deep learning with...

Brain age prediction from MRI scans in neurodegenerative diseases.

PURPOSE OF REVIEW: This review explores the use of brain age estimation from MRI scans as a biomarker of brain health. With disorders like Alzheimer's...

Multimodal MRI radiomics enhances epilepsy prediction in pediatric low-grade glioma patients.

BACKGROUND: Determining whether pediatric patients with low-grade gliomas (pLGGs) have tumor-related epilepsy (GAE) is a crucial aspect of preoperativ...

Ambient technology in epilepsy clinical practice.

The utilization of large language model-based artificial intelligence (AI) in the field of neurology has gained attention as a viable tool to enhance ...

AlphaGrad: Normalized Gradient Descent for Adaptive Multi-loss Functions in EEG-based Motor Imagery Classification.

In this study, we propose AlphaGrad, a novel adaptive loss blending strategy for optimizing multi-task learning (MTL) models in motor imagery (MI)-bas...

NSSI-Net: A Multi-Concept GAN for Non-Suicidal Self-Injury Detection Using High-Dimensional EEG in a Semi-Supervised Framework.

Non-suicidal self-injury (NSSI) is a serious threat to the physical and mental health of adolescents, significantly increasing the risk of suicide and...

Identification of shared mechanisms between Alzheimer's disease and atherosclerosis by integrated bioinformatics analysis.

Alzheimer's disease (AD) and atherosclerosis (AS) are two interacting diseases mostly affecting aged adults. AD is characterized by the deposition of ...

Novel Voice Signal Segmentation Based on Clark Distance to Improve Intelligent Parkinson Disease Detection.

This paper presents a novel classifier based on the Clark distance for early detection of Parkinson's disease (PD) using voice data. The nonlinear nat...

Assessment of contour accuracy in head and neck replanning: Deep learning trained model compared with deformable image registration propagation technique.

Accurate contouring is crucial for optimal treatment outcomes, whether for nonadaptive radiotherapy with single images or adaptive radiotherapy (ART) ...

Random field image representations speed up binary discrimination of brain scans and estimate a phenotype glioblastoma cancer cell model.

MRI brain scans alone are not a definitive measure of dementia. Deep-learning algorithms (DLA) and professional human opinion are necessary for diagno...

Potential Function-Based Molecular Dynamics Simulation of Al-Cu-Li Alloys and Comparison with Experiments.

Due to their excellent specific strength and lightweight characteristics, Al-Cu-Li alloys are widely used in aerospace applications. The newly develop...

Diagnostic Accuracy of Machine Learning-Assisted MRI for Mild Cognitive Impairment in Parkinson's Disease: A Systematic Review and Meta-Analysis.

To evaluate the diagnostic accuracy of machine learning-assisted magnetic resonance imaging (MRI) in detecting cognitive impairment among Parkinson's ...

Classification of neurodegenerative diseases using brain effective connectivity and machine learning techniques: a systematic review.

BACKGROUND: Effective connectivity (EC) refers to the directional influences or causal relationships between brain regions. In the field of artificial...

Older people and stroke: a machine learning approach to personalize the rehabilitation of gait.

INTRODUCTION: Stroke is a significant global public health challenge, ranking as the second leading cause of death after heart disease. One of the mos...

Genetic Risk Scores in Stroke Research and Care.

Stroke remains a leading cause of death and disability worldwide. While well-established risk factors play a major role, genetic predisposition is a c...

Can machine learning be a reliable tool for predicting hematoma progression following traumatic brain injury? A systematic review and meta-analysis.

BACKGROUND: Predicting hematoma progression in traumatic brain injury (TBI) is crucial for timely interventions and effective clinical management, as ...

The Core Outcome Measures Index (COMI) comes of age: 18 years of efficient and effective patient-reported outcome measurement in the field of spinal disorders. Part 2. COMI item-content, scoring, language versions, and applications.

In 1998, an international and multidisciplinary group of experts (from the fields of spine surgery, physiotherapy, occupational therapy, rheumatology,...