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Prediction models for retinopathy of prematurity occurrence based on artificial neural network.

BMC ophthalmology
INTRODUCTION: Early prediction and timely treatment are essential for minimizing the risk of visual loss or blindness of retinopathy of prematurity, emphasizing the importance of ROP screening in clinical routine.

The impact of deep learning based- psychological capital with ideological and political education on entrepreneurial intentions.

Scientific reports
The aim of this study is to investigate the influence of psychological capital on college students' entrepreneurial intentions. Through a combination of relevant analysis and linear regression, the primary focus is on exploring the relationship betwe...

An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design.

Journal of medical Internet research
BACKGROUND: Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors-based FDSs have become the mainstream of research, w...

Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics.

Biomedical engineering online
BACKGROUND: Timely prevention of major adverse cardiovascular events (MACEs) is imperative for reducing cardiovascular diseases-related mortality. Perivascular adipose tissue (PVAT), the adipose tissue surrounding coronary arteries, has attracted inc...

Machine learning of brain-specific biomarkers from EEG.

EBioMedicine
BACKGROUND: Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnes...

Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks.

IEEE transactions on neural networks and learning systems
Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and can be applied to characterize information propagation patterns for different emotional states. To reveal these inherent spatial graph features and i...

Rationale and design of the artificial intelligence scalable solution for acute myocardial infarction (ASSIST) study.

Journal of electrocardiology
BACKGROUND: Acute coronary syndrome (ACS), specifically ST-segment elevation myocardial infarction is a major cause of morbidity and mortality throughout Europe. Diagnosis in the acute setting is mainly based on clinical symptoms and physician's inte...

Linked Color Imaging with Artificial Intelligence Improves the Detection of Early Gastric Cancer.

Digestive diseases (Basel, Switzerland)
INTRODUCTION: Esophagogastroduodenoscopy is the most important tool to detect gastric cancer (GC). In this study, we developed a computer-aided detection (CADe) system to detect GC with white light imaging (WLI) and linked color imaging (LCI) modes a...

Automatic pipeline for segmentation of LV myocardium on quantitative MR T1 maps using deep learning model and computation of radial T1 and ECV values.

NMR in biomedicine
Native T1 mapping is a non-invasive technique used for early detection of diffused myocardial abnormalities, and it provides baseline tissue characterization. Post-contrast T1 mapping enhances tissue differentiation, enables extracellular volume (ECV...