Neurology

An integrated deep learning model for early and multi-class diagnosis of Alzheimer's disease from MRI scans.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely affects memory, behavior, and cognitive function. Early and accurate diagnosis is crucial for effective intervention, yet detecting subtle changes in the early stages remains a challenge. In this study, we propose a hybrid deep learning-based multi-class classification system for AD using magnetic resonance imaging ...

From Fibers to Cells: Fourier-Based Registration Enables Virtual Cresyl Violet Staining From 3D Polarized Light Imaging

Comprehensive assessment of the various aspects of the brain's microstructure requires the use of complementary imaging techniques. This includes measuring the spatial distribution of cell bodies (cytoarchitecture) and nerve fibers (myeloarchitecture). The gold standard for cytoarchitectonic analysis is light microscopic imaging of cell-body stained tissue sections. To reveal the 3D orientations...

AI-DBS study: protocol for a longitudinal prospective observational cohort study of patients with Parkinson's disease for the development of neuronal fingerprints using artificial intelligence.

INTRODUCTION: Deep brain stimulation (DBS) is a proven effective treatment for Parkinson's disease (PD). However, titrating DBS stimulation parameters...

Classification of internet addiction using machine learning on electroencephalography synchronization and functional connectivity.

BACKGROUND: Internet addiction (IA) refers to excessive internet use that causes cognitive impairment or distress. Understanding the neurophysiologica...

ROIsGAN: A Region Guided Generative Adversarial Framework for Murine Hippocampal Subregion Segmentation

The hippocampus, a critical brain structure involved in memory processing and various neurodegenerative and psychiatric disorders, comprises three k...

A Medical Decision Support System for Automatic Treatment Plan Generation Using Machine Learning Algorithms.

Due to demographic change, health economics is increasingly focused on the quality of life in advanced age and the associated cost aspects. Dementia i...

Does Whole Brain Radiomics on Multimodal Neuroimaging Make Sense in Neuro-Oncology? A Proof of Concept Study.

Employing a whole-brain (WB) mask as a region of interest for extracting radiomic features is a feasible, albeit less common, approach in neuro-oncolo...

Accelerated Biological Aging in Exfoliation Glaucoma Assessed by Fundus-Derived Predicted Age and Advanced Glycation End Products.

Glaucoma is an age-related neurodegenerative disease characterized by progressive optic nerve damage. Accelerated biological aging, assessed using pre...

Co-Designing a "win-win" in Predictive AI: First Results from Interviews and Focus Groups with Persons with Parkinson's Disease.

This study explored the perspectives of people with Parkinson's disease (PwP) involved in the co-design of AI tools for PD care. The aim was to unders...

Identifying Key Factors Associated with Assistive Technology Availability for Dementia Care Using Machine Learning.

This study explores the factors influencing the availability of assistive technology for people with dementia through the application of machine learn...

Transforming Healthcare: The Role of Artificial Intelligence.

The integration of artificial intelligence (AI) into healthcare is revolutionising the industry by enhancing diagnostic accuracy, personalising treatm...

Deep normative modelling reveals insights into early-stage Alzheimer's disease using multi-modal neuroimaging data.

BACKGROUND: Exploring the early stages of Alzheimer's disease (AD) is crucial for timely intervention to help manage symptoms and set expectations for...

Commonality and individuality based graph learning network for EEG emotion recognition.

The interplay between individual differences and shared human characteristics significantly impacts electroencephalogram (EEG) emotion recognition mod...

Electroencephalography estimates brain age in infants with high precision: Leveraging advanced machine learning in healthcare.

Changes in the pace of neurodevelopment are key indicators of atypical maturation during early life. Unfortunately, reliable prognostic tools rely on ...

MIMI-ONET: Multi-Modal image augmentation via Butterfly Optimized neural network for Huntington DiseaseDetection.

Huntington's disease (HD) is a chronic neurodegenerative ailment that affects cognitive decline, motor impairment, and psychiatric symptoms. However, ...

Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease Detection

Imaging and genomic data offer distinct and rich features, and their integration can unveil new insights into the complex landscape of diseases. In ...

Comparative Analysis of Stroke Prediction Models Using Machine Learning

Stroke remains one of the most critical global health challenges, ranking as the second leading cause of death and the third leading cause of disabi...

Spec2VolCAMU-Net: A Spectrogram-to-Volume Model for EEG-to-fMRI Reconstruction based on Multi-directional Time-Frequency Convolutional Attention Encoder and Vision-Mamba U-Net

High-resolution functional magnetic resonance imaging (fMRI) is essential for mapping human brain activity; however, it remains costly and logistica...

Examining Deployment and Refinement of the VIOLA-AI Intracranial Hemorrhage Model Using an Interactive NeoMedSys Platform

Background: There are many challenges and opportunities in the clinical deployment of AI tools in radiology. The current study describes a radiology...

Adaptive control for multi-scale stochastic dynamical systems with stochastic next generation reservoir computing

The rapid advancement of neuroscience and machine learning has established data-driven stochastic dynamical system modeling as a powerful tool for u...