Congestive Heart Failure

Artificial intelligence-based calcium scoring using 3D transesophageal echocardiography in aortic stenosis: a pilot study.

BACKGROUND: Aortic valve calcium scoring by computed tomography (CT) is an established method for assessing aortic stenosis severity but is limited by radiation exposure and availability. Artificial intelligence (AI)-based calcium detection using transthoracic echocardiography has shown promise but depends on acoustic window quality. Transesophageal echocardiography (TEE), particularly 3D TEE, may...

Automated whole-heart volumetrics and haemodynamics from 4D flow CMR magnitude images: development and validation of a deep learning model.

AIMS: 4D flow cardiovascular magnetic resonance (CMR) offers a comprehensive haemodynamic assessment but is often limited by long acquisition times and complex post-processing. The magnitude images derived from 4D flow sequences contain time-resolved 3D anatomical information. We aimed to validate the anatomical accuracy of these images against standard cine imaging and develop an artificial intel...

A new protein panel for the diagnosis of HFpEF: combining machine-learning and liquid-chromatography mass-spectrometry proteomics.

INTRODUCTION: The diagnosis of heart failure with a preserved left ventricular ejection fraction (HFpEF) remains challenging. Plasma concentrations of...

AI-enhanced electrocardiography as a digital biomarker platform in cardiovascular medicine: clinical applications, validation gaps and future implementation pathways.

Artificial intelligence applied to the ECG is expanding the clinical role of this widely available diagnostic tool beyond conventional waveform interp...

Machine learning for predicting emergency department visits in patients with type 2 diabetes: A real-world, multi-institutional study.

BACKGROUND: Patients with type 2 diabetes mellitus (T2DM) prone to acute diabetic complications are at high risk for emergency department (ED) visits,...

Clinical Triage of Individuals With Warning Symptoms of Imminent Cardiac Arrest.

BACKGROUND: At least 50% of individuals who suffer sudden cardiac arrest (SCA) have warning symptoms before their SCA, but these are not sufficient to...

Genotype-Specific Electrophysiological Remodeling in PLN R14del Cardiomyopathy: Implications for Precision Antiarrhythmic Therapy.

BACKGROUND: Inherited PLN (phospholamban) R14del variants cause dilated cardiomyopathy with a high burden of malignant ventricular arrhythmias. Howeve...

Point-of-care ultrasound in cardiovascular imaging: current status and future prospects.

INTRODUCTION: As ultrasound technology has become more advanced and accessible over the years, point-of-care ultrasound (POCUS) is becoming a tool as ...

Quantitative Cerebrovascular Analysis for Improved Prediction of Post-Stroke Complications.

Endovascular thrombectomy (EVT) has transformed the treatment of acute ischemic stroke (AIS). However, a substantial proportion of AIS patients experi...

Empowering clinical trial design with agentic intelligence and real-world data.

Clinical trial design (CTD) is a time-consuming process that requires substantial domain expertise. Large-scale real-world data (RWD), such as electro...

Comprehensive and low-cost machine learning models for predicting MASLD prevalence: cross-sectional evidence from the amol cohort study (AmolCS).

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a growing global health burden, yet early detection remains difficult, esp...

Computational intelligence using nailfold videocapillaroscopy for the prediction of carotid intima-media thickness in rheumatoid arthritis: a cohort-based study.

Despite continuously evolving medical advances, CVD risk in Rheumatoid Arthritis (RA) remains paradoxically high to date. Carotid intima-media thickne...

Preoperative MRI and clinical indicators for predicting meniscal repairability: a machine learning-based study.

OBJECTIVES: To develop and internally validate a radiology-centered machine-learning model using preoperative MRI and clinical characteristics to pred...

Unlocking the Regulatory Genome: Interpreting the Clinical Impact of Noncoding Variants in Genetic Cardiomyopathies.

Clinical genetic testing is now the standard of care for cardiomyopathy, guiding risk stratification, clinical management, and earlier diagnosis in fa...

Artificial intelligence-based prediction of transthoracic echocardiographic image quality in ICD/CRT-D candidates: A proof-of-concept study.

BACKGROUND: High-quality echocardiography is essential for accurate and reproducible assessment of cardiac functional indices, which are highly depend...

Automated Optic Disc Tilt Classification in Fundus Photographs Using Segmentation and the Elliptical Ratio: External Clinical Validation Study.

BACKGROUND: Optic disc tilt is a morphological change in myopic eyes that complicates clinical interpretation and artificial intelligence (AI)-based a...

Dynamic Beat-to-Beat Blood Pressure Estimation using a Multi-modal Wearable Deep Learning Approach.

Cuffless blood pressure (BP) monitoring technologies, primarily based on pulse transit time (PTT) or photoplethysmography (PPG), frequently suffer fro...

Endomyocardial Gremlin-1 is associated with structural remodeling and adverse clinical outcomes in non-ischemic cardiomyopathy.

BACKGROUND: Risk stratification in non-ischemic cardiomyopathies (NICM) remains challenging despite guideline-based phenotypic classification using mu...

Imaging-Derived Sarcopenic Obesity and Cardiovascular Outcomes: Insights Into Heart Failure Risk and Muscle Biology.

BACKGROUND: The prognostic value of obesity in cardiovascular disease is complex. Measures such as body mass index and waist-to-height ratio show diff...

From Cardioprotection to Trial Design: Rethinking Cardiac Safety in Oncology.

PURPOSE OF REVIEW: Cardio-oncology has emerged as a pivotal discipline aimed at preserving cardiovascular health in patients undergoing contemporary c...