Geriatrics

An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction.

This study presents an innovative end-to-end deep learning arrhythmia diagnosis model that aims to a...

Can the Electronic Health Record Predict Risk of Falls in Hospitalized Patients by Using Artificial Intelligence? A Meta-analysis.

Because of an aging population worldwide, the increasing prevalence of falls and their consequent in...

Optimizing Two-Way Partial AUC With an End-to-End Framework.

The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the ave...

Fully Convolutional Change Detection Framework With Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection.

Deep learning for change detection is one of the current hot topics in the field of remote sensing. ...

Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data.

Late-onset Alzheimer's disease (LOAD) is the most common multifactorial neurodegenerative disease am...

Implementation of a structured robotic colorectal curriculum for general surgery residents.

There is increasing demand for colorectal robotic training for general surgery residents. We impleme...

Editorial: Can artificial intelligence help us overcome challenges in geriatrics?

Gigapixel end-to-end training using streaming and attention.

Current hardware limitations make it impossible to train convolutional neural networks on gigapixel ...

Development of a model for the prediction of biological age.

BACKGROUND AND OBJECTIVE: Rates of aging vary markedly among individuals, and biological age serves ...

Evolving a Pipeline Approach for Abstract Meaning Representation Parsing Towards Dynamic Neural Networks.

Meaning Representation parsing aims to represent a sentence as a structured, Directed, Acyclic Graph...

Precious1GPT: multimodal transformer-based transfer learning for aging clock development and feature importance analysis for aging and age-related disease target discovery.

Aging is a complex and multifactorial process that increases the risk of various age-related disease...

Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: a longitudinal cohort study.

Machine learning (ML) could have advantages over traditional statistical models in identifying risk ...

An end-to-end convolutional neural network for automated failure localisation and characterisation of 3D interconnects.

The advancement in the field of 3D integration circuit technology leads to new challenges for qualit...

Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture.

COVID-19,which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one...

Lite-SeqCNN: A Light-Weight Deep CNN Architecture for Protein Function Prediction.

The short-and-long range interactions amongst amino-acids in a protein sequence are primarily respon...

Comparison of four machine learning algorithms for a pre-impact fall detection system.

In recent years, real-time health monitoring using wearable sensors has been an active area of resea...

Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time.

With an aging population and increased chronic diseases, remote health monitoring has become critica...

Differential diagnosis of benign and malignant vertebral compression fractures: Comparison and correlation of radiomics and deep learning frameworks based on spinal CT and clinical characteristics.

PURPOSE: Differentiating benign from malignant vertebral compression fractures (VCFs) is a diagnosti...

Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality metrics.

Head motion artifacts in magnetic resonance imaging (MRI) are an important confounding factor concer...

msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping.

Quantitative susceptibility mapping (QSM) has been applied to the measurement of iron deposition and...

The effectiveness of a therapeutic robot, 'Paro', on behavioural and psychological symptoms, medication use, total sleep time and sociability in older adults with dementia: A systematic review and meta-analysis.

OBJECTIVE: To evaluate the effectiveness of a therapeutic robot, 'Paro', on anxiety, agitation, depr...