Congestive Heart Failure

AMS-HD: Hyperdimensional Computing for Real-Time and Energy-Efficient Acute Mountain Sickness Detection.

OBJECTIVE: Acute mountain sickness (AMS) is the most prevalent altitude illness, affecting unacclimatized individuals ascending above 2,500 m and potentially escalating to life threatening cerebral or pulmonary edema. Conventional machine learning (ML) methods for AMS detection from wearable physi ological signals often fail to meet real-time hardware efficiency requirements of continuous monitori...

Artificial Intelligence for the Detection of Hypertrophic Cardiomyopathy From Standard Electrocardiogram.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is often diagnosed late, increasing avoidable risk and delaying treatment. Artificial intelligence (AI) for 12-lead electrocardiograms (ECGs) may identify undetected HCM earlier. OBJECTIVES: This study aimed to evaluate the performance of an AI algorithm for HCM detection and identify predictors of correct classification. METHODS: Of 314 patients with ...

A machine learning AI model for predicting condylar erosion in patients with temporomandibular joint disorders and analyzing factors contributing to prediction.

OBJECTIVES: In temporomandibular joint osteoarthritis, condylar erosion is considered a sign of disease progression. Few studies have clarified the ex...

Explainable artificial intelligence models in predicting major cardiovascular events: insights from the PolyIran and PolyPars prospective studies.

Cardiovascular diseases are among the leading causes of global mortality and morbidity, underscoring the need for accurate and explainable predictive ...

Integrated bioinformatics analysis and experimental validation reveal circFoxO1 as a regulator of pathological cardiac hypertrophy.

Pathological Cardiac Hypertrophy (CHT) is a maladaptive response that can lead to heart failure and increase the risk of severe cardiovascular events....

Predicting major adverse cardiovascular and cerebrovascular events in chronic heart failure: a machine learning study.

BACKGROUND: Heart failure (HF) is a clinical syndrome characterized by impaired cardiac diastolic and systolic function due to structural or functiona...

Early intensive antihypertensive treatment in high-risk population of intracerebral haemorrhage expansion identified by artificial intelligence (ARCHES): study protocol for a multicentre randomised controlled trial.

BACKGROUNDS: Haematoma expansion (HE) is a significant factor in poor outcomes following intracerebral haemorrhage (ICH). Studies have suggested that ...

Discriminating HFrEF vs HFpEF from chest radiographs: Mitigating demographic performance gaps via augmentation and multimodal fusion.

Deep learning models applied to chest radiography have shown promise for cardiac phenotyping, yet concerns remain regarding demographic performance di...

An artificial intelligence model to detect abnormal ejection fraction from non-contrast chest computed tomography: the CT-LVEF study.

AIMS: Heart failure (HF), a major global health challenge, affects millions worldwide and poses substantial healthcare and economic burdens. It is est...

AI-assisted diagnosis of cardiac amyloidosis using electrocardiograms and echocardiography: a multicenter retrospective study in China.

BACKGROUND: Cardiac amyloidosis (CA) is an under-recognized cause of left-ventricular hypertrophy (LVH) that is often misclassified as hypertrophic ca...

Deformable image registration for self-supervised cardiac phase detection in cardiac magnetic resonance images of patients with various diseases.

Cardiovascular magnetic resonance (CMR) is widely used to assess cardiac function, but individual cardiac cycles complicate automatic temporal compari...

From DNA to Drug Discovery AI Models for Cardiovascular Precision Medicine.

Cardiovascular diseases remain the world's leading cause of death-yet the molecular mechanisms linking genetic variation to clinical outcomes are stil...

Identification of Centrosome Duplication-Related Biomarkers in Hypertrophic Cardiomyopathy Through Integrative Multi-Omics, Single-Cell Transcriptomics, and Experimental Validation.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is a prevalent inherited cardiovascular disorder characterized by ventricular wall thickening and myocar...

Diagnostic accuracy of AI-Based models for pulmonary edema detection: A systematic review and Meta-Analysis.

BACKGROUND: Pulmonary edema is a life-threatening condition caused by fluid accumulation in the lungs that impairs gas exchange. Machine learning mode...

No-reflow phenomenon during percutaneous coronary intervention: Contemporary insights into mechanisms, prediction, and management.

No-reflow phenomenon remains a common and prognostically adverse complication of percutaneous coronary intervention (PCI), characterized by impaired m...

An explainable machine learning method for identifying key atherogenic lipid biomarkers in abdominal obesity among the Southern Chinese population.

BACKGROUND: Abdominal obesity (AO) significantly contributes to cardiometabolic diseases and poses an increasing public health challenge. Composite li...

An interpretable machine learning model for preoperative risk stratification in paediatric congenital heart disease surgery: a retrospective cohort study.

BACKGROUND: Congenital heart disease (CHD), one of the most common birth defects, poses challenges to preoperative risk stratification due to its anat...

PMB-NN: Physiology-centered hybrid AI for personalized hemodynamic monitoring from photoplethysmography.

BACKGROUND AND OBJECTIVE: Continuous monitoring of blood pressure (BP) and hemodynamic parameters such as peripheral resistance (R) and arterial compl...

Deep learning-based arterial waveform analysis for predicting postoperative cerebrovascular events in pediatric patients with Moyamoya disease.

BACKGROUND: Postoperative cerebrovascular events, including transient ischemic attacks, infarctions, and hemorrhages, remain a significant concern in ...

Artificial intelligence for right ventricular assessment: current evidence and future directions.

PURPOSE OF REVIEW: Right ventricular size and function are vital to risk stratification in pulmonary hypertension, valvular disease, and congenital he...