Cardiovascular

Hypertension

Latest AI and machine learning research in hypertension for healthcare professionals.

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Echo-Vision-FM: A Pre-training and Fine-tuning Framework for Echocardiogram Video Vision Foundation Model

Echocardiograms provide essential insights into cardiac health, yet their complex, multidimensional ...

Predicting mortality in critically ill patients with hypertension using machine learning and deep learning models

Accurate prediction of mortality in critically ill patients with hypertension admitted to the Intens...

Development and Application of Natural Language Processing on Unstructured Data in Hypertension: A Scoping Review

Hypertension is a global health concern with a vast body of unstructured data, such as clinical note...

Feature Extraction Tool Using Temporal Landmarks in Arterial Blood Pressure and Photoplethysmography Waveforms

Arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms both contain vital physiologi...

MRI-Derived Variables Combined with Machine Learning for Pulmonary Hypertension Risk Prediction: A Retrospective Analysis

Pulmonary hypertension (PH) is a severe and progressive vascular disease for which early diagnosis a...

PanEcho: Complete AI-enabled echocardiography interpretation with multi-task deep learning

Echocardiography is a cornerstone of cardiovascular care but relies on expert interpretation and man...

Integrating AI-ECG and Point-of-Care Cardiac Ultrasound for Screening Structural Heart Disease: A Proof-of-Concept Study

Early structural heart disease (SHD) detection is crucial for improving prognostic outcomes, but wid...

Hypertension Screening via Awake-Sleep Differences in Photoplethysmogram Signals

Hypertension is a major risk factor for cardiovascular diseases. This study proposes a novel hyperte...

DeepDrug2: A Germline-focused Graph Neural Network Framework for Alzheimer’s Drug Repurposing Validated by Electronic Health Records

Alzheimer’s disease (AD) is a complex neurodegenerative disorder with limited therapeutic options. T...

Automated Deep Learning Pipeline for Characterizing Left Ventricular Diastolic Function

Left ventricular diastolic dysfunction (LVDD) is most commonly evaluated by echocardiography. Howeve...

Machine Learning Identifies Microbiome and Clinical Predictors of Sustained Weight Loss Following Prolonged Fasting

Prolonged fasting may benefit metabolic health, but data in healthy individuals remain limited. We p...

Integrative Machine Learning Approach to Risk Prediction for Dementia and Alzheimer’s Disease

Dementia, especially Alzheimer’s disease (AD), is a major global health challenge marked by progress...

Target Trial Emulation Applications in Hypertension Research: A Scoping Review

Target Trial Emulation (TTE) has emerged as a rigorous framework for causal inference using observat...

Development of Computational Pipeline for Right Ventricular Hemodynamic Single-Beat Analysis

Load-independent indices of right ventricular (RV) dysfunction aid in the prognosis of patients with...

Wearable-Echo-FM: An ECG-echo foundation model for single lead electrocardiography

Artificial intelligence (AI) models can now detect patterns of structural heart diseases (SHDs) from...

Application of Machine Learning Approaches to Develop Predictive Models for Diabetes and Hypertension among Bangladesh Adults

With rapid urbanization, lifestyle changes, and an aging population, non-communicable diseases (NCDs...

Cardiac Function Assessment with Deep-Learning-Based Automatic Segmentation of Free-Running 4D Whole-Heart CMR

Free-running (FR) cardiac MRI enables free-breathing ECG-free fully dynamic 5D (3D spatial+cardiac+r...

Myocardial Native T1 Mapping in the German National Cohort (NAKO): Associations with Age, Sex, and Cardiometabolic Risk Factors

In cardiovascular magnetic resonance (CMR), myocardial native T1 mapping enables quantitative, non-i...

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