Cardiovascular

Parallel convolutional neural networks for non-invasive cardiac hemodynamic estimation: integrating uncalibrated PPG signals with nonlinear feature analysis.

Understanding cardiac hemodynamic status (CHS) is essential for accurate cardiovascular health asses...

Deep Learning and Radiomics Discrimination of Coronary Chronic Total Occlusion and Subtotal Occlusion using CTA.

RATIONALE AND OBJECTIVES: Coronary chronic total occlusion (CTO) and subtotal occlusion (STO) pose d...

Handwriting strokes as biomarkers for Alzheimer's disease prediction: A novel machine learning approach.

In recent years, machine learning-based handwriting analysis has emerged as a valuable tool for supp...

A cross-sectional study comparing machine learning and logistic regression techniques for predicting osteoporosis in a group at high risk of cardiovascular disease among old adults.

BACKGROUND: Osteoporosis has become a significant public health concern that necessitates the applic...

Pathophysiological mechanisms of exertional dyspnea in people with cardiopulmonary disease: Recent advances.

Physical activity is a leading trigger of dyspnea in chronic cardiopulmonary diseases. Recently, the...

Quantifying the Unknowns of Plaque Morphology: The Role of Topological Uncertainty in Coronary Artery Disease.

This article aimed to explore topological uncertainty in medical imaging, particularly in assessing ...

Artificial-intelligence-enhanced ECG: a potential solution to limitations of ECG in detecting left ventricular dysfunction after chemotherapy.

A prediction model based on machine learning: prognosis of HBV-induced HCC male patients with smoking and drinking habits after local ablation treatment.

BACKGROUND: Liver cancer, particularly hepatocellular carcinoma (HCC), is a major health concern glo...

Artificial Intelligence-Enhanced Analysis of Echocardiography-Based Radiomic Features for Myocardial Hypertrophy Detection and Etiology Differentiation.

BACKGROUND: While echocardiography is pivotal for detecting left ventricular hypertrophy (LVH), it s...

DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods.

Drug-induced cardiotoxicity (DICT) is a significant challenge in drug development and public health....

MultiCTox: Empowering Accurate Cardiotoxicity Prediction through Adaptive Multimodal Learning.

Cardiotoxicity refers to the inhibitory effects of drugs on cardiac ion channels. Accurate predictio...

Enhancing prediction and stratifying risk: machine learning and bayesian-learning models for catheter-related thrombosis in chemotherapy patients.

BACKGROUND: Catheter-related thrombosis (CRT) is a serious complication in cancer patients undergoin...

Machine learning combined with infrared spectroscopy for detection of hypertension pregnancy: towards newborn and pregnant blood analysis.

Biochemical changes in the cervix during labor are not well understood. This gap in knowledge is sig...

Construction and validation of a predictive model for intracardiac thrombus risk in patients with dilated cardiomyopathy: a retrospective study.

BACKGROUND: Systemic embolic events due to exfoliation of intracardiac thrombus (ICT) are one of the...

Developing a Multisensor-Based Machine Learning Technology (Aidar Decompensation Index) for Real-Time Automated Detection of Post-COVID-19 Condition: Protocol for an Observational Study.

BACKGROUND: Post-COVID-19 condition is emerging as a new epidemic, characterized by the persistence ...

False Crisis Alarms in Cardiopulmonary Monitoring:: Identification, Causes, and Clinical Implications.

The systematic annotation of crisis alarms reveals a high number of false alarms for both ventricula...

Predicting hemorrhagic transformation in acute ischemic stroke: a systematic review, meta-analysis, and methodological quality assessment of CT/MRI-based deep learning and radiomics models.

Acute ischemic stroke (AIS) is a major cause of mortality and morbidity, with hemorrhagic transforma...

Role of physics-informed constraints in real-time estimation of 3D vascular fluid dynamics using multi-case neural network.

Numerical simulations of fluid dynamics in tube-like structures are important to biomedical research...

Preliminary phantom study of four-dimensional computed tomographic angiography for renal artery mapping: Low-tube voltage and low-contrast volume imaging with deep learning-based reconstruction.

INTRODUCTION: A hybrid angio-CT system with 320-row detectors and deep learning-based reconstruction...

Machine Learning Identification of Patient Phenoclusters in Aortic Regurgitation.

BACKGROUND: Current treatment paradigms assume aortic regurgitation (AR) patients to be a homogenous...

GraphDeep-hERG: Graph Neural Network PharmacoAnalytics for Assessing hERG-Related Cardiotoxicity.

PURPOSE: The human Ether-a-go-go Related-Gene (hERG) encodes rectifying potassium channels that play...