Geriatrics

Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach.

BACKGROUND: The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points.

Implemented classification techniques for osteoporosis using deep learning from the perspective of healthcare analytics.

BACKGROUND: Osteoporosis is a medical disorder that causes bone tissue to deteriorate and lose density, increasing the risk of fractures. Applying Neural Networks (NN) to analyze medical imaging data and detect the presence or severity of osteoporosis in patients is known as osteoporosis classification using Deep Learning (DL) algorithms. DL algorithms can extract relevant information from bone im...

Prediction of LncRNA-protein Interactions Using Auto-Encoder, SE-ResNet Models and Transfer Learning.

BACKGROUND: Long non-coding RNA (lncRNA) plays a crucial role in various biological processes, and mutations or imbalances of lncRNAs can lead to seve...

A Video-based Automated Tracking and Analysis System of Plaque Burden in Carotid Artery Using Deep Learning: A Comparison with Senior Sonographers.

BACKGROUND AND OBJECTIVE: The incidence of stroke is rising, and it is the second major cause of mortality and the third leading cause of disability a...

Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer's Disease.

BACKGROUND: There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and...

Simplifying Alzheimer's Disease Monitoring: A Novel Machine-Learning Approach to Estimate the Clinical Dementia Rating Sum of Box Changes Using the Mini-Mental State Examination and Functional Activities Questionnaire.

BACKGROUND: Primary outcome measure in the clinical trials of disease modifying therapy (DMT) drugs for Alzheimer's disease (AD) has often been evalua...

Prognostic Power? Do the Plasma Biomarkers, Neurofilament Light and Phospho-Tau 181, Improve Prediction of Progression to Alzheimer's Disease Using a Machine Learning Approach in the ADNI Cohort?

With the advent of therapeutics with potential to slow Alzheimer's disease progression the necessity of understanding the diagnostic value of plasma b...

Personalized prediction of diabetic foot ulcer recurrence in elderly individuals using machine learning paradigms.

BACKGROUND: This study utilizes machine learning to analyze the recurrence risk of diabetic foot ulcers (DFUs) in elderly diabetic patients, aiming to...

Epistatic Features and Machine Learning Improve Alzheimer's Disease Risk Prediction Over Polygenic Risk Scores.

BACKGROUND: Polygenic risk scores (PRS) are linear combinations of genetic markers weighted by effect size that are commonly used to predict disease r...

hdWGCNA and Cellular Communication Identify Active NK Cell Subtypes in Alzheimer's Disease and Screen for Diagnostic Markers through Machine Learning.

BACKGROUND: Alzheimer's disease (AD) is a recognized complex and severe neurodegenerative disorder, presenting a significant challenge to global healt...

Artificial Intelligence in Eye Movements Analysis for Alzheimer's Disease Early Diagnosis.

As the world's population ages, Alzheimer's disease is currently the seventh most common cause of death globally; the burden is anticipated to increas...

Enhancing Alzheimer's Disease Classification with Transfer Learning: Finetuning a Pre-trained Algorithm.

OBJECTIVE: The increasing longevity of the population has made Alzheimer's disease (AD) a significant public health concern. However, the challenge of...

Updated Models of Alzheimer's Disease with Deep Neural Networks.

BACKGROUND: In recent years, researchers have focused on developing precise models for the progression of Alzheimer's disease (AD) using deep neural n...

Classifying Alzheimer's Disease Neuropathology Using Clinical and MRI Measurements.

BACKGROUND: Computer-aided machine learning models are being actively developed with clinically available biomarkers to diagnose Alzheimer's disease (...

Screening Targets and Therapeutic Drugs for Alzheimer's Disease Based on Deep Learning Model and Molecular Docking.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder caused by a complex interplay of various factors. However, a satisfactory cure fo...

Identifying best fall-related balance factors and robotic-assisted gait training attributes in 105 post-stroke patients using clinical machine learning models.

BACKGROUND: Despite the promising effects of robot-assisted gait training (RAGT) on balance and gait in post-stroke rehabilitation, the optimal predic...

Alzheimer's Disease Prediction Using Fly-Optimized Densely Connected Convolution Neural Networks Based on MRI Images.

Alzheimer's is a degenerative brain cell disease that affects around 5.8 million people globally. The progressive neurodegenerative disease known as A...

Identification of Dementia & Mild Cognitive Impairment in Chinese Elderly Using Machine Learning.

OBJECTIVE: To assess the role of Machine Learning (ML) in identification critical factors of dementia and mild cognitive impairment.

A Study of Assisted Screening for Alzheimer's Disease Based on Handwriting and Gait Analysis.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease that is not easily detected in the early stage. Handwriting and walkin...

Importance of Serum Albumin in Deep Learning-Based Prediction of Cognitive Function Data in the Aged Using a Basic Blood Test.

BACKGROUND: Recently, a method using deep learning has been developed to estimate the risk of developing dementia. This method uses general blood test...