European Heart Journal - Digital Health

BackgroundAtrial cardiomyopathy (AtCM) is both a cause and a consequence of atrial fibrillation and flutter (AF) and can lead to ischemic stroke. Imaging derived left atrial (LA) structure and function are used to diagnose AtCM. Considering the tight coupling of heart structure and rhythm generation, this information might also be derived from 12-lead electrocardiogram (ECG), which is low-cost and readily available. MethodsFirst, we finetuned a deep learning ECG foundational model (ECG-FM) pret...

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...

BackgroundDespite its broadening indications, the implantable cardiac monitor (ICM) records a narrow, nonstandard electrocardiogram (ECG) signal which precludes morphological and functional assessments or the application of 12-lead ECG models. We hypothesize that deep learning can be used to reconstruct 12-lead ECG from a single ICM lead for continuously assessing clinical endpoints outside of rhythm detection alone. ObjectiveTo reconstruct 12-lead ECG from a single ICM lead to detect conductio...

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...

Blood pressure (BP) measurement is crucial for medical care, yet existing BP methods are either invasive, tethered, or suffer from low temporal resolution. Non-invasive continuous BP estimation thus remains a significant challenge. To address these challenges, this work presents a novel, non-invasive, multi-modal sensor designed for continuous blood pressure estimation using multiple biosignal modalities as feature inputs. From these input data, we extract cardiovascular timing intervals (e.g., ...

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 ...

BackgroundPericardial effusion can progress to life-threatening cardiac tamponade when large or rapidly accumulating, yet early diagnosis is frequently delayed without sufficient clinical vigilance. Echocardiography is the gold standard but is not always accessible in emergency or resource-limited settings. Electrocardiographic (ECG) findings, including low QRS voltage and electrical alternans, may prompt clinical suspicion; however, their diagnostic value remains constrained by poor quantitativ...

AimsDespite the availability of clinical risk scores for atherosclerotic cardiovascular disease (ASCVD), their use is limited because the required predictor data are often missing. We developed and validated ECG-ASCVD, a scalable risk prediction paradigm that utilizes ECGs to target ASCVD risk factor assessment. MethodsAdults aged 30-79 who had undergone a clinical ECG were identified in the Yale New Haven Health System (YNNHS) and a state death index. We developed ECG-ASCVD-12, ECG-ASCVD-IMAGE...

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...

BackgroundEarly and accurate identification of cardiac amyloidosis improves patient outcomes, yet relevant evidence is frequently hidden in free-text records. This study assesses whether structured variable extraction or direct free-text analysis more reliably identifies patients with cardiac amyloidosis, with the goal of informing clinical decision support strategies. MethodsWe extracted 21 clinical variables from 432 Italian patient records using supervised and prompt-based methods with both ...

Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is conventionally diagnosed using electrocardiography and serial blood biomarker measurements. We investigated a non-invasive, bloodless, and electrode-free diagnostic strategy using a wrist-worn infrared spectrophotometric biosensor (Infrasensor). In a prospective, multicenter study of 595 patients with suspected NSTE-ACS enrolled across 13 sites in two countries, participants were stratified into five analytical cohorts. With 200 mult...

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 ...

AO_SCPLOWBSTRACTC_SCPLOWDisease management for heart failure with preserved ejection fraction (HFpEF) requires understanding the comparative effectiveness of real-world drug combinations rather than single agents. Standard randomized controlled trials (RCTs) for multi-drug regimens are prohibitively expensive, slow, and often infeasible at scale, motivating the use of causal machine learning methods on large-scale electronic health records (EHRs). However, reliable estimation of treatment effect...

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...

BackgroundEarly identification of arrhythmias in the intensive care unit (ICU) is important to prevent ICU morbidity and mortality. Timely arrhythmia detection relies on bedside providers telemetry interpretation. Machine learning (ML) models can function as clinical support tools to facilitate diagnoses. ML model development requires well-curated training data. The differential performance between labelers of different roles and experience is currently unknown. MethodsThis was a prospective ob...

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...