Neurology

Integrating Reinforcement Learning and AI Agents for Adaptive Robotic Interaction and Assistance in Dementia Care

This study explores a novel approach to advancing dementia care by integrating socially assistive robotics, reinforcement learning (RL), large language models (LLMs), and clinical domain expertise within a simulated environment. This integration addresses the critical challenge of limited experimental data in socially assistive robotics for dementia care, providing a dynamic simulation environme...

Sensitivity of Quantitative Susceptibility Mapping in Clinical Brain Research

Background: Quantitative susceptibility mapping (QSM) of the brain is an advanced MRI technique for assessing tissue characteristics based on magnetic susceptibility, which varies with the composition of the tissue, such as iron, calcium, and myelin levels. QSM consists of multiple processing steps, with various choices for each step. Despite its increasing application in detecting and monitorin...

Classification of Mild Cognitive Impairment Based on Dynamic Functional Connectivity Using Spatio-Temporal Transformer

Dynamic functional connectivity (dFC) using resting-state functional magnetic resonance imaging (rs-fMRI) is an advanced technique for capturing the...

Pfungst and Clever Hans: Identifying the unintended cues in a widely used Alzheimer's disease MRI dataset using explainable deep learning

Backgrounds. Deep neural networks have demonstrated high accuracy in classifying Alzheimer's disease (AD). This study aims to enlighten the underl...

Leveraging Video Vision Transformer for Alzheimer's Disease Diagnosis from 3D Brain MRI

Alzheimer's disease (AD) is a neurodegenerative disorder affecting millions worldwide, necessitating early and accurate diagnosis for optimal patien...

Analysis of TEM micrographs with deep learning reveals APOE genotype-specific associations between HDL particle diameter and Alzheimer's dementia.

High-density lipoprotein (HDL) particle diameter distribution is informative in the diagnosis of many conditions, including Alzheimer's disease (AD). ...

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention

Precise characterization of stroke lesions from MRI data has immense value in prognosticating clinical and cognitive outcomes following a stroke. Ma...

Scaling laws for decoding images from brain activity

Generative AI has recently propelled the decoding of images from brain activity. How do these approaches scale with the amount and type of neural re...

Deep Learning in Early Alzheimer's disease's Detection: A Comprehensive Survey of Classification, Segmentation, and Feature Extraction Methods

Alzheimers disease is a deadly neurological condition, impairing important memory and brain functions. Alzheimers disease promotes brain shrinkage, ...

Stroke classification using Virtual Hybrid Edge Detection from in silico electrical impedance tomography data

Electrical impedance tomography (EIT) is a non-invasive imaging method for recovering the internal conductivity of a physical body from electric bou...

A sandbox study proposal for private and distributed health data analysis

This paper presents a sandbox study proposal focused on the distributed processing of personal health data within the Vinnova-funded SARDIN project....

Distinguishing Parkinson's Patients Using Voice-Based Feature Extraction and Classification

Parkinson's disease (PD) is a progressive neurodegenerative disorder that impacts motor functions and speech characteristics This study focuses on d...

Motion-Mimicking Robotic Finger Prosthesis for Burn-induced Partial Hand Amputee: A Case Report.

Burn injuries often result in severe hand complications, including joint contractures and nerve damage, sometimes leading to amputation. Despite early...

Longitudinal Missing Data Imputation for Predicting Disability Stage of Patients with Multiple Sclerosis

Multiple Sclerosis (MS) is a chronic disease characterized by progressive or alternate impairment of neurological functions (motor, sensory, visual,...

A change language for ontologies and knowledge graphs.

Ontologies and knowledge graphs (KGs) are general-purpose computable representations of some domain, such as human anatomy, and are frequently a cruci...

Using Space-Filling Curves and Fractals to Reveal Spatial and Temporal Patterns in Neuroimaging Data

We present a novel method, Fractal Space-Curve Analysis (FSCA), which combines Space-Filling Curve (SFC) mapping for dimensionality reduction with f...

Evaluating AI Models: Performance Validation Using Formal Multiple-Choice Questions in Neuropsychology.

High-quality and accessible education is crucial for advancing neuropsychology. A recent study identified key barriers to board certification in clini...

EVolutionary Independent DEtermiNistiC Explanation

The widespread use of artificial intelligence deep neural networks in fields such as medicine and engineering necessitates understanding their decis...

GL-ICNN: An End-To-End Interpretable Convolutional Neural Network for the Diagnosis and Prediction of Alzheimer's Disease

Deep learning methods based on Convolutional Neural Networks (CNNs) have shown great potential to improve early and accurate diagnosis of Alzheimer'...

ITCFN: Incomplete Triple-Modal Co-Attention Fusion Network for Mild Cognitive Impairment Conversion Prediction

Alzheimer's disease (AD) is a common neurodegenerative disease among the elderly. Early prediction and timely intervention of its prodromal stage, m...