Neurology

BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identification

Neurological conditions, such as Alzheimer's Disease, are challenging to diagnose, particularly in the early stages where symptoms closely resemble healthy controls. Existing brain network analysis methods primarily focus on graph-based models that rely solely on imaging data, which may overlook important non-imaging factors and limit the model's predictive power and interpretability. In this pa...

REMEMBER: Retrieval-based Explainable Multimodal Evidence-guided Modeling for Brain Evaluation and Reasoning in Zero- and Few-shot Neurodegenerative Diagnosis

Timely and accurate diagnosis of neurodegenerative disorders, such as Alzheimer's disease, is central to disease management. Existing deep learning models require large-scale annotated datasets and often function as "black boxes". Additionally, datasets in clinical practice are frequently small or unlabeled, restricting the full potential of deep learning methods. Here, we introduce REMEMBER -- ...

Hemodynamic Markers: CFD-Based Prediction of Cerebral Aneurysm Rupture Risk

This study investigates the influence of aneurysm evolution on hemodynamic characteristics within the sac region. Using computational fluid dynamics...

The SERENADE project: Sensor-Based Explainable Detection of Cognitive Decline

Mild Cognitive Impairment (MCI) affects 12-18% of individuals over 60. MCI patients exhibit cognitive dysfunctions without significant daily functio...

An Empirical Investigation of Reconstruction-Based Models for Seizure Prediction from ECG Signals

Epileptic seizures are sudden neurological disorders characterized by abnormal, excessive neuronal activity in the brain, which is often associated ...

Adaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectories

Longitudinal biomedical studies monitor individuals over time to capture dynamics in brain development, disease progression, and treatment effects. ...

Beyond Feature Importance: Feature Interactions in Predicting Post-Stroke Rigidity with Graph Explainable AI

This study addresses the challenge of predicting post-stroke rigidity by emphasizing feature interactions through graph-based explainable AI. Post-s...

Brain Signatures of Time Perception in Virtual Reality

Achieving a high level of immersion and adaptation in virtual reality (VR) requires precise measurement and representation of user state. While extr...

Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis

Focal cortical dysplasia (FCD) type II is a major cause of drug-resistant epilepsy, often curable only by surgery. Despite its clinical importance, ...

MoEDiff-SR: Mixture of Experts-Guided Diffusion Model for Region-Adaptive MRI Super-Resolution

Magnetic Resonance Imaging (MRI) at lower field strengths (e.g., 3T) suffers from limited spatial resolution, making it challenging to capture fine ...

Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentation

The segmentation of lesions in Moderate to Severe Traumatic Brain Injury (msTBI) presents a significant challenge in neuroimaging due to the diverse...

Applying Data Mining to Predict Perceived Benefits Risks of Robotics at Home for Dementia Caregiving Among African American Families.

We used data mining to predict the attitudes of 527 caregivers towards the pros and cons of using robotics and artificial intelligence (AI) for dement...

The Impact of Artificial Intelligence in Reducing the Cost of Dementia: A Scoping Review.

INTRODUCTION: Dementia is a major cause of disability among the elderly, imposing significant financial burdens on healthcare systems. Traditional car...

Using C4.5 Algorithm to Gain Insights on Stakeholder Engagement and Use of Artificial Intelligence on Social Media in Dementia Caregiving Disparity Research.

We applied machine learning techniques to build models that predict perceived risks and benefits of using artificial intelligence (AI) algorithms to r...

The Pepper Robot in Healthcare: A Scoping Review.

Humanoid robots are increasingly being used in a number of domains. This paper focuses on reviewing the use of the Pepper humanoid robot in healthcare...

Comparative analysis of unsupervised clustering techniques using validation metrics: Study on cognitive features from the Canadian Longitudinal Study on Aging (CLSA)

Purpose: The primary goal of this study is to explore the application of evaluation metrics to different clustering algorithms using the data provid...

Going beyond explainability in multi-modal stroke outcome prediction models

Aim: This study aims to enhance interpretability and explainability of multi-modal prediction models integrating imaging and tabular patient data. ...

Classification of ADHD and Healthy Children Using EEG Based Multi-Band Spatial Features Enhancement

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in children, characterized by difficulties in attention, hyp...

Perils of Label Indeterminacy: A Case Study on Prediction of Neurological Recovery After Cardiac Arrest

The design of AI systems to assist human decision-making typically requires the availability of labels to train and evaluate supervised models. Freq...

Leveraging Gait Patterns as Biomarkers: An attention-guided Deep Multiple Instance Learning Network for Scoliosis Classification

Scoliosis is a spinal curvature disorder that is difficult to detect early and can compress the chest cavity, impacting respiratory function and car...