Alzheimer's Disease

Evaluating Traditional, Deep Learning and Subfield Methods for Automatically Segmenting the Hippocampus From MRI.

Given the relationship between hippocampal atrophy and cognitive impairment in various pathological conditions, hippocampus segmentation from MRI is an important task in neuroimaging. Manual segmentation, though considered the gold standard, is time-consuming and error-prone, leading to the development of numerous automatic segmentation methods. However, no study has yet independently compared the...

Dynamic and Static Structure-Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease.

The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure-function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to the overall structure-function interaction, whereas dynamic SFC refers to transient coupling variations. In this study, we aimed to assess the potentia...

[Machine learning models established to distinguish OA and RA based on immune factors in the knee joint fluid].

Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning mode...

Improving Diseases Predictions Utilizing External Bio-Banks

Machine learning has been successfully used in critical domains, such as medicine. However, extracting meaningful insights from biomedical data is o...

"Is There Anything Else?'': Examining Administrator Influence on Linguistic Features from the Cookie Theft Picture Description Cognitive Test

Alzheimer's Disease (AD) dementia is a progressive neurodegenerative disease that negatively impacts patients' cognitive ability. Previous studies h...

Early Prediction of Alzheimer's and Related Dementias: A Machine Learning Approach Utilizing Social Determinants of Health Data

Alzheimer's disease and related dementias (AD/ADRD) represent a growing healthcare crisis affecting over 6 million Americans. While genetic factors ...

PHGNN: A Novel Prompted Hypergraph Neural Network to Diagnose Alzheimer's Disease

The accurate diagnosis of Alzheimer's disease (AD) and prognosis of mild cognitive impairment (MCI) conversion are crucial for early intervention. H...

Alzheimer's Disease Classification Using Retinal OCT: TransnetOCT and Swin Transformer Models

Retinal optical coherence tomography (OCT) images are the biomarkers for neurodegenerative diseases, which are rising in prevalence. Early detection...

OPTIMUS: Predicting Multivariate Outcomes in Alzheimer's Disease Using Multi-modal Data amidst Missing Values

Alzheimer's disease, a neurodegenerative disorder, is associated with neural, genetic, and proteomic factors while affecting multiple cognitive and ...

TAU: Modeling Temporal Consistency Through Temporal Attentive U-Net for PPG Peak Detection

Photoplethysmography (PPG) sensors have been widely used in consumer wearable devices to monitor heart rates (HR) and heart rate variability (HRV). ...

Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes

Alzheimer's disease (AD) is a major neurodegenerative condition that affects millions around the world. As one of the main biomarkers in the AD diag...

BrainNet-MoE: Brain-Inspired Mixture-of-Experts Learning for Neurological Disease Identification

The Lewy body dementia (LBD) is the second most common neurodegenerative dementia after Alzheimer's disease (AD). Early differentiation between AD a...

Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion

Dementia is a progressive condition that impairs an individual's cognitive health and daily functioning, with mild cognitive impairment (MCI) often ...

Quantum-Inspired Privacy-Preserving Federated Learning Framework for Secure Dementia Classification

Dementia, a neurological disorder impacting millions globally, presents significant challenges in diagnosis and patient care. With the rise of priva...

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 computin...

MUTATE: a human genetic atlas of multiorgan artificial intelligence endophenotypes using genome-wide association summary statistics.

Artificial intelligence (AI) has been increasingly integrated into imaging genetics to provide intermediate phenotypes (i.e. endophenotypes) that brid...

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...

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...