European Heart Journal - Digital Health

BackgroundTraditional heart transplant registries often lack the granularity required for deep phenotyping and rely on labor-intensive manual abstraction. We describe the methodology and validation of a next-generation, automated, multi-source registry designed to address these limitations. MethodsUtilizing a High-Performance Computing environment, we integrated structured data from Epic data warehouses (Clarity and Caboodle), external molecular diagnostics, and verified UNOS survival records. ...

BackgroundLong-term electrocardiogram (ECG) monitoring with wearable devices enables large-scale characterisation of cardiac rhythms, but population-based evidence remains limited. The UK Biobank Cardiac Monitoring Study integrates 14-day patch-based ECG monitoring with accelerometry and detailed phenotypic and lifestyle data. Here, we report the acquisition protocol, data processing, and initial findings from 27,658 participants. MethodsParticipants in the UK Biobank imaging study were invited...

This research presents multimodal deep learning for structural heart disease prediction. We evaluated multiple deep learning architectures, including TCN, Simple CNN, ResNet1d18, Light transformer and Hybrid model. The models were examined across the three seeds to ensure robustness, and bootstrap confidence interval is used to measure performance differences. TCN consistently outperforms other competing architectures, achieving statistically significant improvements with stable performance acro...

BackgroundArtificial intelligence (AI) has emerged as a promising tool for interpreting 12-lead electrocardiograms (ECGs), with the potential to enhance diagnostic accuracy for arrhythmia detection. However, published studies vary widely in methodology and validation strategy, warranting a quantitative synthesis of diagnostic performance. MethodsA systematic review and meta-analysis was conducted according to the PRISMA-DTA 2018 guidelines and registered in PROSPERO (CRD420251027264). Searches ...

BackgroundEchocardiography (echo) is a cornerstone of pediatric cardiology, yet access to expert interpreters is limited worldwide, particularly in low-resource and rural settings. Artificial intelligence (AI) offers a mechanism to broadly deliver expert-level precision and standardize measurements, yet AI for comprehensive automated measurements in pediatric and congenital heart disease (CHD) echo remains underdeveloped. MethodsWe created EchoFocus-Measure, an AI platform that automatically ex...

ObjectivesTo identify unique echocardiographic signatures associated with TTR+ carrier status preceding onset of cardiac amyloidosis. BackgroundCarrier status for the most common pathogenic TTR variant in the United States, Val142Ile (V142I), found in 4% of African Americans (AA) and 1% of Hispanic/Latino (H/L) individuals, confers a 40-60% lifetime risk of developing variant transthyretin amyloidosis (ATTRv), including cardiac amyloidosis (CA) and heart failure (HF). Myocardial amyloid deposit...

BACKGROUNDConventional electrocardiography (ECG) has limited diagnostic accuracy for detecting coronary artery disease (CAD) in patients with stable chest pain. Advanced electrocardiography (A-ECG) may improve diagnostic performance. The study aimed to derive, externally validate, and prognostically validate an explainable A-ECG score for detecting CAD on coronary computed tomography angiography (CCTA). METHODSParticipants attending an outpatient rapid access chest pain clinic (RACC) underwent ...

BackgroundNormative pediatric electrocardiographic (ECG) parameters are standardized, but lack temporal resolution for neonates and infants. These values are clinically important, as they support the diagnosis, risk stratification, and management of cardiovascular diseases (CVD). MethodsFive ECG parameters (heart rate (HR), QRS, PR, QT, QTc intervals) were retrospectively analyzed from 7,346 recordings from 6,967 patients at a large pediatric hospital. Patients were only included if their ECG w...

Heart failure (HF) readmission prediction models often rely on manually curated, cross-sectional features and show limited discrimination and calibration. We evaluated whether automated feature engineering via Deep Feature Synthesis (DFS) improves the clinical applicability of HF readmission prediction from lon-gitudinal electronic health record data. Using 355,217 HF hospitalizations from a large U.S. safety-net health system (2010-2025), we compared a clinician-curated baseline feature set to ...

BackgroundElevated resting heart rate (HR) and atrial cardiopathy are each linked to higher mortality risk, yet their interrelationship and joint prognostic value remain unclear. MethodsWe analyzed 7,326 adults (mean age 59 {+/-} 13 years) without cardiovascular disease from the Third National Health and Nutrition Examination Survey with available electrocardiograms. Atrial cardiopathy was defined by electrocardiogram as abnormal P-wave axis or deep terminal P-wave negativity in V1. Multivariab...

We present a deep learning model that predicts left atrial (LA) volume from standard 12-lead ECG recordings and basic patient data. This approach offers a low-cost, scalable alternative to MRI-based LA volume measurement, which remains the clinical gold standard but is often inaccessible. Our model performs regression directly on LA volume targets and leverages Shapley values to provide interpretable feature importance. Results highlight the predictive value of ECG signals and demonstrate that p...

I.AO_SCPLOWBSTRACTC_SCPLOWCoronary Artery Disease (CAD) is a leading cause of cardiovascular-related mortality and affects 20.5 million people in the United States and approximately 315 million people worldwide in 2022. The asymptomatic and progressive nature of CAD presents challenges for early diagnosis and timely intervention. Traditional diagnostic methods such angiography and stress tests are known to be resource-intensive and prone to human error. This calls for a need for automated and ti...

Sudden cardiac death risk is 2-3-fold higher in athletes than in non-athletes. We classify sports-related cardiac arrhythmias using a novel explainability framework comprising data analysis, model interpretability, post-hoc visualisation, and systematic assessment. Two neural networks--one with interpretable sinc convolution and one with standard convolution--were trained on general-population ECGs (PhysioNet, n=88,253, 30 arrhythmias, three continents) and tested on professional footballers (PF...

ObjectivesArtificial intelligence (AI)-enabled digital stethoscopes combine phonocardiography and electrocardiography to support detection of cardiac rhythm and structural abnormalities. This study evaluated the feasibility and exploratory diagnostic performance of AI-guided cardiac auscultation during routine general practice consultations and home visits. MethodsIn this prospective feasibility study, 50 consecutive patients aged [≥]65 years underwent AI-assisted auscultation using the Eko ...

Background and AimsAccurate classification of mitral stenosis (MS) remains a significant clinical challenge. This study aimed to develop an artificial intelligence (AI) framework to automatically detect clinically significant MS from echocardiography. MethodsWe developed EchoNet-MS, an open-source end-to-end integrated approach combining video based convolutional neural networks to assess MS severity and differentiate rheumatic etiology from echocardiography and validated its performance across...

Large language models (LLMs) are increasingly used in clinical workflows, yet requiring clinician review of every AI output negates the efficiency gains that motivate their adoption. We present SCOUT (Scalable Clinical Oversight via Uncertainty Triangulation), a model-agnostic meta-verification framework that selectively defers unreliable LLM predictions to clinicians by triangulating three orthogonal signals: model heterogeneity, stochastic inconsistency, and reasoning critique. In this retrosp...

This study presents RED-RHD, a machine learning methodology for early detection and classification of Rheumatic Heart Disease (RHD) using heart sound recordings. By leveraging OpenL3 deep acoustic embeddings, cloud-based workflows, and an ensemble of SVM and XGBoost classifiers, RED-RHD achieves an average precision of 95.62% for murmur detection (Normal vs. Abnormal) and 99.00% precision for systolic vs. diastolic murmur classification, demonstrating marked improvements over prior methods with ...

Background: Pressure volume (PV) loop analysis remains the gold standard for assessing the intrinsic global diastolic properties of the left ventricle (LV). Traditional fitting techniques rely on local, phase-constrained fittings and are limited due to their sensitivity to noise, landmark selection, violation of assumptions, and non-convergence. Objective: To develop and validate DIAPINN, a physics-informed neural network (PINN) framework capable of calculating intrinsic diastolic properties of ...

BackgroundGenerative artificial intelligence (AI) systems are increasingly used to produce medical illustrations for education; however, their anatomical accuracy in complex domains such as congenital heart disease (CHD) remains insufficiently validated. MethodsIn an assessor-blinded comparative study, we evaluated AI-generated CHD illustrations from two contemporary text-to-image platforms (ChatGPT-5/ChatGPT-Images and Gemini NanoBanana) against human-modified educational images. Twenty differ...

AimsWe aimed to develop and evaluate fully automated artificial intelligence (AI) system. for detection of mitral valve prolapse (MVP) and mitral regurgitation (MR) from echocardiographic studies. Methods and ResultsWe used a dataset of 24,869 echocardiographic studies from the University of California San Francisco (UCSF) to train a multi-view deep neural network (DNN) to detect MVP using apical 4-chamber, 2-chamber, and parasternal long-axis views. A separate dataset of 27,906 studies from UC...