Neurology

Multidimensional Feature Analysis of Meniere's Disease and Vestibular Migraine: Insights from Machine Learning and Vestibular Testing.

OBJECTIVE: Differentiating between Meniere's disease (MD) and vestibular migraine (VM) is challenging due to overlapping symptoms and limited diagnostic tools. Traditional statistical methods often rely on physician judgment and struggle with complex, high-dimensional data. This study applies the random forest (RF) machine learning algorithm to enhance the clinical differentiation between MD and V...

GeneDX-PBMC: An adversarial autoencoder framework for unlocking Alzheimer's disease biomarkers using blood single-cell RNA sequencing data.

OBJECTIVE: To identify blood-based biomarkers and therapeutic targets for Alzheimer's disease (AD) by leveraging single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) and advanced deep learning techniques.

Postoperative self-care ability of continuous nursing based on artificial intelligence for stroke patients with neurological injury.

According to the statistics of relevant data, stroke is a relatively common cerebrovascular disease, and its incidence rate is as high as 185/100,000 ...

Deep-Diffeomorphic Networks for Conditional Brain Templates.

Deformable brain templates are an important tool in many neuroimaging analyses. Conditional templates (e.g., age-specific templates) have advantages o...

Neuroimaging and machine learning in eating disorders: a systematic review.

PURPOSE: Eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED), are complex psychiatric condi...

Optimizing predictive model performance in adult spinal deformity surgery: a comparative head-to-head analysis of learning models for perioperative complications.

OBJECTIVE: The aim of this study was to develop and compare 4 predictive algorithms, including logistic regression (LR), random forest (RF), gradient ...

Beyond accuracy: The need for explainable AI in biomedical voice technology.

Speech and voice have emerged as valuable non-invasive biomarkers for detecting and monitoring a range of medical conditions, from neurodegenerative a...

Detection of emergency department patients at risk of dementia through artificial intelligence.

INTRODUCTION: The study aimed to develop and validate the Emergency Department Dementia Algorithm (EDDA) to detect dementia among older adults (65+) a...

Radiomics of PET Using Neural Networks for Prediction of Alzheimer's Disease Diagnosis.

Positron emission tomography (PET) imaging technology is widely used for diagnosing Alzheimer's disease (AD) in people with dementia. Although various...

ML-Driven Alzheimer's disease prediction: A deep ensemble modeling approach.

Alzheimer's disease (AD) is a progressive neurological disorder characterized by cognitive decline due to brain cell death, typically manifesting late...

Use of artificial intelligence to support quality of life of people with dementia: A scoping review.

BACKGROUND: Dementia has an impact on the quality of life (QoL) of people with dementia. Tailored services are crucial for improving their QoL. Advanc...

Computational screening of natural products as tryptophan 2,3-dioxygenase inhibitors: Insights from CNN-based QSAR, molecular docking, ADMET, and molecular dynamics simulations.

Parkinson's disease (PD) is characterised by a complex array of motor, psychiatric, and gastrointestinal symptoms, many of which are linked to disrupt...

A new lightweight deep learning model optimized with pruning and dynamic quantization to detect freezing gait on wearable devices.

Freezing of gait (FoG) is a debilitating symptom of Parkinson's disease that severely impacts patients' mobility and quality of life. To minimize the ...

Exploring trade-offs in equitable stroke risk prediction with parity-constrained and race-free models.

A recent analysis of common stroke risk prediction models showed that performance differs between Black and White subgroups, and that applying standar...

Resting-state functional MRI metrics to detect freezing of gait in Parkinson's disease: a machine learning approach.

Among the symptoms that can occur in Parkinson's disease (PD), Freezing of Gait (FOG) is a disabling phenomenon affecting a large proportion of patien...

Sharing patient technology preferences with care networks: Stakeholders' views of the "Let's Talk Tech" decision aid for dementia care.

BackgroundLet's Talk Tech (LTT) is a self-administered web intervention for people with memory loss and their care partners that supports decision-mak...

Assessing amyloid fibrils and amorphous aggregates: A review.

Protein misfolding and aggregation play a central role in the progression of neurodegenerative diseases such as Alzheimer's and Parkinson's. These agg...

Multimodal fusion architectures for Alzheimer's disease diagnosis: An experimental study.

OBJECTIVE: In the attempt of early diagnosis of Alzheimer's Disease, varying forms of medical records of multiple modalities are gathered to seize the...

Getting More from Less: Transfer Learning Improves Sleep Stage Decoding Accuracy in Peripheral Wearable Devices

Transfer learning, a technique commonly used in generative artificial intelligence, allows neural network models to bring prior knowledge to bear wh...

DuAL-Net: A Hybrid Framework for Alzheimer's Disease Prediction from Whole-Genome Sequencing via Local SNP Windows and Global Annotations

Alzheimer's disease (AD) dementia is the most common form of dementia. With the emergence of disease-modifying therapies, predicting disease risk be...