Congestive Heart Failure

Comparative Analysis of Artificial Intelligence-Based Quantification versus Visual Rating of Enlarged Perivascular Spaces in the Multi-Ethnic Study of Atherosclerosis Cohort.

BACKGROUND AND PURPOSE: Enlarged perivascular spaces (PVS) are fluid-filled spaces surrounding small cerebral vessels. Current PVS research has been limited by the absence of efficient and scalable quantification tools. We compared visual ratings versus artificial intelligence (AI)-based quantification in identifying associations of PVS with vascular risk factors and cognitive performance. MATERIA...

Machine learning combined with multi-omics analysis: identifying nucleotide metabolism-associated immune genes and validating their functions in cardiomyopathy.

Biobank-Scale Plasma Proteomics Identifies Novel Biomarkers in Hypertrophic Cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is characterized by substantial heterogeneity in both clinical phenotype and risk of adverse outcomes, i...

Comparative study on the predictive value of PNI and GNRI for all-cause mortality rates of elderly patients in cardiac care unit and cardiovascular intensive care unit.

BACKGROUND: The prognostic nutritional index (PNI) and geriatric nutritional risk index (GNRI) are reliable alternative biomarkers of nutrition. Howev...

Interpretable machine learning models based on CMR radiomics for predicting left ventricular diastolic dysfunction in patients with metabolic-associated steatotic liver disease and type 2 diabetes mellitus.

BACKGROUND: Obesity-induced left ventricular diastolic dysfunction (LVDD), associated with ectopic fat and dysfunctional epicardial adipose tissue (EA...

Artificial intelligence-based prediction of radio-cephalic arteriovenous fistula maturation using preoperative duplex examination: a retrospective multicenter cohort study.

Establishing a functional arteriovenous fistula (AVF) is crucial to ensure effective dialysis treatment. However, there are currently no definitive cr...

Visit-to-visit systolic blood pressure variability and risk of severe hypertensive episodes in a multicenter cohort.

BACKGROUND: Visit-to-visit blood pressure variability has been associated with cardiovascular outcomes; however, its role in predicting severe blood p...

Evaluating Artificial Intelligence-generated Patient Education Guides on Cardiomyopathies: A Cross-sectional Comparison of ChatGPT and DeepSeek Artificial Intelligence.

INTRODUCTION: Patient education is essential in the management of cardiomyopathies, including dilated, restrictive, and hypertrophic subtypes, which o...

Generalization of Left Ventricular Segmentation Models to LVNC Patients: A Comparative Study.

Left ventricular non-compaction (LVNC) is a rare cardiomyopathy with distinctive myocardial morphology. Due to its low prevalence, LVNC cases are rare...

Preoperative frailty for predicting in-hospital mortality in patients after cardiac surgery: an interpretable Machine learning model based on a retrospective multicenter cohort study.

BACKGROUND AND OBJECTIVE: Accurate prediction of mortality risk after cardiac surgery is crucial for perioperative management. The laboratory-based fr...

Machine learning-based risk stratification identifies heart failure with preserved ejection fraction as an independent predictor of adverse outcomes in hypertrophic cardiomyopathy.

Heart failure with preserved ejection fraction (HFpEF) is increasingly recognized in hypertrophic cardiomyopathy (HCM); however, its prognostic signif...

Prospective Data Curation Enables High-Performance Artificial Intelligence for Diabetic Retinopathy Screening in a Resource-Limited Setting.

PURPOSE: To determine whether a high-quality, prospectively curated dataset can, by itself, enable the development of robust and clinically effective ...

Malignant Cerebral Edema after Large-Core Anterior Circulation Stroke in the Thrombectomy Era: Prediction and Neurocritical Care Pathways.

Large-core anterior circulation ischemic stroke (LCIS) complicated by malignant cerebral edema (MCE) remains a leading cause of early death and profou...

Decision support systems (DSS) for predicting hypertensive events using real-world telemonitoring data.

OBJECTIVE: This study contributes to the limited literature on applying machine learning (ML) to telemonitoring data for timely decision support syste...

Automatic monitoring of single-wall MAPSE by transesophageal echocardiography for tracking global left ventricular function irrespective of regional hypokinesia: a secondary analysis.

BACKGROUND: Measuring mitral annular plane systolic excursion (MAPSE) serially in a single wall may be an effective method for monitoring global left ...

A novel signal distribution-based approach to characterize atrial cardiomyopathy.

A machine learning approach to predicting dyspnea with noninvasive biomarkers.

Dyspnea is the subjective sensation of breathing discomfort. This symptom is highly prevalent in patients with chronic and critical illness, and its p...

Deep learning based eye disease classification using Optical Coherence Tomography (OCT) images.

BACKGROUND AND OBJECTIVE: Optical Coherence Tomography (OCT) imaging plays a crucial role in the early diagnosis of ocular diseases. This study aims t...

Computed tomography in ARDS, from morphological insights to AI-powered multi-modal analysis: a narrative review.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a critical clinical condition characterized by acute respiratory failure and high mortality....

Robotic-assisted biventricular durable assist device implantation: The first in human experience.

We report a novel approach of robotic-assisted, minimally invasive biventricular assist device (BiVAD) implantation using HeartMate 3 in a 61-year-old...