Neurology

CQ CNN: A Hybrid Classical Quantum Convolutional Neural Network for Alzheimer's Disease Detection Using Diffusion Generated and U Net Segmented 3D MRI

The detection of Alzheimer disease (AD) from clinical MRI data is an active area of research in medical imaging. Recent advances in quantum computing, particularly the integration of parameterized quantum circuits (PQCs) with classical machine learning architectures, offer new opportunities to develop models that may outperform traditional methods. However, quantum machine learning (QML) remains...

Toward Scalable Access to Neurodevelopmental Screening: Insights, Implementation, and Challenges

Children with neurodevelopmental disorders require timely intervention to improve long-term outcomes, yet early screening remains inaccessible in many regions. A scalable solution integrating standardized assessments with physiological data collection, such as electroencephalogram (EEG) recordings, could enable early detection in routine settings by non-specialists. To address this, we introdu...

The order in speech disorder: a scoping review of state of the art machine learning methods for clinical speech classification

Background:Speech patterns have emerged as potential diagnostic markers for conditions with varying etiologies. Machine learning (ML) presents an op...

OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labels

Accurately discriminating progressive stages of Alzheimer's Disease (AD) is crucial for early diagnosis and prevention. It often involves multiple i...

Modality-Agnostic Style Transfer for Holistic Feature Imputation

Characterizing a preclinical stage of Alzheimer's Disease (AD) via single imaging is difficult as its early symptoms are quite subtle. Therefore, ma...

A comprehensive and reliable protocol for manual segmentation of the human claustrum using high-resolution MRI

The claustrum is a thin gray matter structure in each brain hemisphere, characterized by exceptionally high connectivity with nearly all brain regio...

M-SCAN: A Multistage Framework for Lumbar Spinal Canal Stenosis Grading Using Multi-View Cross Attention

The increasing prevalence of lumbar spinal canal stenosis has resulted in a surge of MRI (Magnetic Resonance Imaging), leading to labor-intensive in...

Learning Covariance-Based Multi-Scale Representation of Neuroimaging Measures for Alzheimer Classification

Stacking excessive layers in DNN results in highly underdetermined system when training samples are limited, which is very common in medical applica...

Dementia Insights: A Context-Based MultiModal Approach

Dementia, a progressive neurodegenerative disorder, affects memory, reasoning, and daily functioning, creating challenges for individuals and health...

Multimodal Artificial Intelligence Models Predicting Glaucoma Progression Using Electronic Health Records and Retinal Nerve Fiber Layer Scans.

PURPOSE: The purpose of this study was to develop models that predict which patients with glaucoma will progress to require surgery, combining structu...

Cross-Attention Fusion of MRI and Jacobian Maps for Alzheimer's Disease Diagnosis

Early diagnosis of Alzheimer's disease (AD) is critical for intervention before irreversible neurodegeneration occurs. Structural MRI (sMRI) is wide...

NeuroSymAD: A Neuro-Symbolic Framework for Interpretable Alzheimer's Disease Diagnosis

Alzheimer's disease (AD) diagnosis is complex, requiring the integration of imaging and clinical data for accurate assessment. While deep learning h...

SBERO: Skill Al-Biruni Earth Radius Optimization for Alzheimer's Disease Classification Using Magnetic Resonance Image.

Alzheimer's disease (AD) is the most prevalent form of dementia, characterized by progressive memory loss and cognitive decline, often affecting behav...

Autism Spectrum Disorder Detection Using Prominent Connectivity Features from Electroencephalography.

Autism Spectrum Disorder (ASD) is a disorder of brain growth with great variability whose clinical presentation initially shows up during early stages...

Developing and validating a prediction tool for cerebral amyloid angiopathy neuropathological severity.

INTRODUCTION: Cerebral amyloid angiopathy (CAA) is a cerebrovascular condition, the severity of which can only be determined post mortem. Here, we dev...

Enhancing single-cell classification accuracy using image conversion and deep learning.

Single-cell transcriptome sequencing (scRNA-seq) is widely used in the fields of animal and plant developmental biology and important trait analysis b...

Foundation-Model-Boosted Multimodal Learning for fMRI-based Neuropathic Pain Drug Response Prediction

Neuropathic pain, affecting up to 10% of adults, remains difficult to treat due to limited therapeutic efficacy and tolerability. Although resting-s...

Exploring the Potential of QEEGNet for Cross-Task and Cross-Dataset Electroencephalography Encoding with Quantum Machine Learning

Electroencephalography (EEG) is widely used in neuroscience and clinical research for analyzing brain activity. While deep learning models such as E...

ALVI Interface: Towards Full Hand Motion Decoding for Amputees Using sEMG

We present a system for decoding hand movements using surface EMG signals. The interface provides real-time (25 Hz) reconstruction of finger joint a...

Hypergraph Multi-Modal Learning for EEG-based Emotion Recognition in Conversation

Emotional Recognition in Conversation (ERC) is an important method for diagnosing health conditions such as autism or depression, as well as underst...