Neurology

Stroke prediction in elderly patients with atrial fibrillation using machine learning combined clinical and left atrial appendage imaging phenotypic features.

BACKGROUND: Atrial fibrillation (AF) is one of the primary etiologies for ischemic stroke, and it is of paramount importance to delineate the risk phenotypes among elderly AF patients and to investigate more efficacious models for predicting stroke risk.

Benchmarking Early Agitation Prediction in Community-Dwelling People with Dementia Using Multimodal Sensors and Machine Learning

Agitation is one of the most common responsive behaviors in people living with dementia, particularly among those residing in community settings without continuous clinical supervision. Timely prediction of agitation can enable early intervention, reduce caregiver burden, and improve the quality of life for both patients and caregivers. This study aimed to develop and benchmark machine learning ...

ConnectomeDiffuser: Generative AI Enables Brain Network Construction from Diffusion Tensor Imaging

Brain network analysis plays a crucial role in diagnosing and monitoring neurodegenerative disorders such as Alzheimer's disease (AD). Existing appr...

How We Won the ISLES'24 Challenge by Preprocessing

Stroke is among the top three causes of death worldwide, and accurate identification of stroke lesion boundaries is critical for diagnosis and treat...

Weakly-supervised Mamba-Based Mastoidectomy Shape Prediction for Cochlear Implant Surgery Using 3D T-Distribution Loss

Cochlear implant surgery is a treatment for individuals with severe hearing loss. It involves inserting an array of electrodes inside the cochlea to...

Multi-Modal Spectral Parametrization Method (MMSPM) for analyzing EEG activity with distinct scaling regimes

Aperiodic neural activity has been the subject of intense research interest lately as it could reflect on the cortical excitation/inhibition ratio, ...

Multi-Modal Spectral Parametrization Method (MMSPM) for analyzing EEG activity with distinct scaling regimes

Aperiodic neural activity has been the subject of intense research interest lately as it could reflect on the cortical excitation/inhibition ratio, ...

MR-EEGWaveNet: Multiresolutional EEGWaveNet for Seizure Detection from Long EEG Recordings

Feature engineering for generalized seizure detection models remains a significant challenge. Recently proposed models show variable performance dep...

Reverse-Speech-Finder: A Neural Network Backtracking Architecture for Generating Alzheimer's Disease Speech Samples and Improving Diagnosis Performance

This study introduces Reverse-Speech-Finder (RSF), a groundbreaking neural network backtracking architecture designed to enhance Alzheimer's Disease...

Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis.

BACKGROUND: Hemorrhagic transformation (HT) is commonly detected in acute ischemic stroke (AIS) and often leads to poor outcomes. Currently, there is ...

Cog-TiPRO: Iterative Prompt Refinement with LLMs to Detect Cognitive Decline via Longitudinal Voice Assistant Commands

Early detection of cognitive decline is crucial for enabling interventions that can slow neurodegenerative disease progression. Traditional diagnost...

Simulation-Guided Approximate Logic Synthesis Under the Maximum Error Constraint

Approximate computing is an effective computing paradigm for improving energy efficiency of error-tolerant applications. Approximate logic synthesis...

Machine learning for clinical outcome prediction in cerebrovascular and endovascular neurosurgery: systematic review and meta-analysis.

BACKGROUND: Machine learning (ML) may be superior to traditional methods for clinical outcome prediction. We sought to systematically review the liter...

Predicting seizure onset zones from interictal intracranial EEG using functional connectivity and machine learning.

Functional connectivity (FC) analyses of intracranial EEG (iEEG) signals can potentially improve the mapping of epileptic networks in drug-resistant f...

Facial expression deep learning algorithms in the detection of neurological disorders: a systematic review and meta-analysis.

BACKGROUND: Neurological disorders, ranging from common conditions like Alzheimer's disease that is a progressive neurodegenerative disorder and remai...

Evaluating machine- and deep learning approaches for artifact detection in infant EEG: classifier performance, certainty, and training size effects.

Electroencephalography (EEG) is essential for studying infant brain activity but is highly susceptible to artifacts due to infants' movements and phys...

AI-Driven Design and Development of Nontoxic DYRK1A Inhibitors.

Dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) is implicated in several human diseases, including DYRK1A syndrome, cancer, and...

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation

Pretrained generative models have opened new frontiers in brain decoding by enabling the synthesis of realistic texts and images from non-invasive b...

Multi-modal Integration Analysis of Alzheimer's Disease Using Large Language Models and Knowledge Graphs

We propose a novel framework for integrating fragmented multi-modal data in Alzheimer's disease (AD) research using large language models (LLMs) and...

On the Relevance of Clinical Assessment Tasks for the Automatic Detection of Parkinson's Disease Medication State from Speech

The automatic identification of medication states of Parkinson's disease (PD) patients can assist clinicians in monitoring and scheduling personaliz...