Geriatrics

Machine-learning models for diagnosis of rotator cuff tears in osteoporosis patients based on anteroposterior X-rays of the shoulder joint.

OBJECTIVE: This study aims to diagnose Rotator Cuff Tears (RCT) and classify the severity of RCT in ...

Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index.

AIMS: There is a lack of tools for accurately identifying the risk of readmission for heart failure ...

An Ethically Supported Framework for Determining Patient Notification and Informed Consent Practices When Using Artificial Intelligence in Health Care.

Artificial intelligence (AI) is increasingly being used in health care. Without an ethically support...

Epileptic Seizure Prediction Using Spatiotemporal Feature Fusion on EEG.

Electroencephalography (EEG) plays a crucial role in epilepsy analysis, and epileptic seizure predic...

Integration of bioinformatics and machine learning approaches for the validation of pyrimidine metabolism-related genes and their implications in immunotherapy for osteoporosis.

BACKGROUND: Osteoporosis (OP), the "silent epidemic" of our century, poses a significant challenge t...

Development and validation of a reliable method for automated measurements of psoas muscle volume in CT scans using deep learning-based segmentation: a cross-sectional study.

OBJECTIVES: We aimed to develop an automated method for measuring the volume of the psoas muscle usi...

Prediction of Cognitive Impairment Risk among Older Adults: A Machine Learning-Based Comparative Study and Model Development.

INTRODUCTION: The prevalence of cognitive impairment and dementia in the older population is increas...

DMA-HPCNet: Dual Multi-Level Attention Hybrid Pyramid Convolution Neural Network for Alzheimer's Disease Classification.

Computer-aided diagnosis (CAD) plays a crucial role in the clinical application of Alzheimer's disea...

Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning.

BACKGROUND: Alzheimer's disease (AD) is a chronic neurodegenerative disorder that poses a substantia...

Smart diabetic foot ulcer scoring system.

Current assessment methods for diabetic foot ulcers (DFUs) lack objectivity and consistency, posing ...

Philosophy of cognitive science in the age of deep learning.

Deep learning has enabled major advances across most areas of artificial intelligence research. This...

Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer's disease classification based on gradient-weighted class activation mapping.

Currently, numerous studies focus on employing fMRI-based deep neural networks to diagnose neurologi...

Answer to the commentary on the paper "Geriatrics and artificial intelligence in Spain (Ger-IA project): talking to ChatGPT, a nationwide survey".

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the diseas...

A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer's disease using resting-state functional network connectivity.

Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study bo...

Deploying Robot-Led Activities for People with Dementia at Aged Care Facilities: A Feasibility Study.

OBJECTIVES: To explore the feasibility of deploying robot-led activities for people with dementia li...

Alzheimer's disease early screening and staged detection with plasma proteome using machine learning and convolutional neural network.

Alzheimer's disease (AD) stands as the prevalent progressive neurodegenerative disease, precipitatin...

Raman hyperspectroscopy of saliva and machine learning for Sjögren's disease diagnostics.

Sjögren's disease is an autoimmune disorder affecting exocrine glands, causing dry eyes and mouth an...

Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality.

. To improve breast cancer risk prediction for young women, we have developed deep learning methods ...

CKG-IMC: An inductive matrix completion method enhanced by CKG and GNN for Alzheimer's disease compound-protein interactions prediction.

Alzheimer's disease (AD) is one of the most prevalent chronic neurodegenerative disorders globally, ...

Machine-learning classifier models for predicting sarcopenia in the elderly based on physical factors.

AIM: As the size of the elderly population gradually increases, musculoskeletal disorders, such as s...