European Heart Journal - Digital Health

Background and AimsArtificial intelligence-enhanced electrocardiograms (AI-ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions lack actionability at an individual patient level, explainability and biological plausibility. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform. Methods and ResultsThe AIRE platform was developed in a se...

IntroductionArtificial intelligence (AI)-enhanced electrocardiogram (ECG) models are designed to detect specific anatomical and functional cardiac abnormalities. Understanding the selectivity of their phenotypic associations is essential to inform their clinical use. Here, we sought to assess whether AI-ECG models function as condition-specific classifiers or broader cardiovascular risk markers. MethodsWe included four distinct study populations, drawn from both electronic health records (EHR) ...

BackgroundDue to the lack of a feasible screening strategy, aortic stenosis (AS) is often diagnosed after the development of clinical symptoms, representing advanced stages of disease. Portable and wearable devices capable of recording electrocardiograms (ECGs) can be used for scalable screening for AS, if the diagnosis can be made with a single-lead ECG, despite potentially noisy acquisition. MethodsUsing electronic health records and imaging data from a large, diverse hospital system (2015-20...

Structured AbstractO_ST_ABSBackgroundC_ST_ABSThe dire consequences of heart failure (HF) patient non-response to guideline directed medical therapy often fuel early, non-selective referral for surgical intervention (ventricular assist device [VAD] or transplant). The high-risk associated with these interventions mandates precision in directing them only toward those patients who would otherwise suffer severe near-term deterioration. We previously reported a 52,265-patient deep learning model tha...

BACKGROUND AND AIMSDiastolic dysfunction is a precursor to heart failure with preserved ejection fraction (HFpEF), and early detection by electrocardiography (ECG) would be valuable. We hypothesised that an explainable advanced ECG (A-ECG) score could accurately detect diastolic dysfunction with clinically meaningful diagnostic and prognostic performance. METHODSA derivation cohort was included after standard 12-lead ECG and echocardiography demonstrating normal systolic function, and either th...

Low ejection fraction (EF), an indicator of impaired heart function, often goes undiagnosed and can lead to avoidable heart failure and arrhythmias. We developed and externally validated a deep learning model for detecting low EF from 12-lead electrocardiograms. The model achieved 85.8% sensitivity and 83.0% specificity on an independent validation cohort, with consistent results across demographic subgroups. These findings supported FDA 510(k) clearance of the model. Clinical net benefit analys...

BackgroundIdentifying structural heart diseases (SHDs) early can change the course of the disease, but their diagnosis requires cardiac imaging, which is limited in accessibility. ObjectiveTo leverage images of 12-lead ECGs for automated detection and prediction of multiple SHDs using an ensemble deep learning approach. MethodsWe developed a series of convolutional neural network models for detecting a range of individual SHDs from images of ECGs with SHDs defined by transthoracic echocardiogr...

BackgroundTimely and accurate assessment of electrocardiograms (ECGs) is crucial for diagnosing, triaging, and clinically managing patients. Current workflows rely on computerized ECG interpretation tools built into ECG signal acquisition systems, which use rule-based algorithms that are unreliable and frequently not available in low-resource settings. We developed and validated a format-independent vision encoder-decoder model - ECG-GPT - that can generate free-text, expert-level interpretation...

IntroductionAsymptomatic left ventricular dysfunction (ALVD) carries an increased risk for overt heart failure and mortality, yet treatable to mitigate disease progression. An artificial intelligence (AI)-enabled 12-lead electrocardiogram (ECG) model demonstrated promise in ALVD screening but an unexpected drop in performance was observed in external validation. We thus sought to train a de novo model for ALVD detection and investigate its performance across multiple institutions and across a br...

Cardiovascular Diseases (CVDs) are the leading cause of mortality worldwide, necessitating early and accurate diagnosis to prevent severe outcomes such as Heart Failure (HF). Despite the widespread use of Electrocardiogram (ECG) for cardiac monitoring, traditional methods often miss subtle preclinical changes. In this paper, we present an automated digital biomarker discovery pipeline that leverages explainable artificial intelligence (XAI) to enhance the interpretability and clinical applicabil...

BackgroundMany ECG-AI models have been developed to predict a wide range of cardiovascular outcomes. The underrepresentation of women in cardiovascular disease studies has raised concerns if these models are equally predictive in women as compared to men. We tested the effect of sex-imbalance in training datasets on predictive performance of ECG-AI models, investigating imbalance in representation (ratio women-to-men), as well as in outcome prevalence, and percentage of misclassification. Metho...

BackgroundThree-dimensional (3D) electrocardiography (ECG) is a recent methodological advance that extends the dimensionality of the standard ECG, enabling geometric descriptors that capture acute ischemia. Integrating these descriptors with deep learning (DL) may improve the discrimination between ischemic and non-ischemic states and promote the clinical translation of 3D ECG analysis. MethodsECGs from seventeen patients with acute left anterior descending (LAD) artery stenosis (>50 %) were ob...

BackgroundPrevious research has demonstrated acceptable diagnostic accuracy of AI-enabled sinus rhythm (SR) electrocardiogram (ECG) interpretation for predicting paroxysmal or incident atrial fibrillation (AF). However, interethnic validations of these AI algorithms remain limited. We aimed to develop and comprehensively evaluate our AI model for predicting AF based on standard 12Dlead SR ECG images in a Korean population, and to validate its performance in Brazilian patient cohorts. MethodsWe ...

BackgroundBig data has the potential to revolutionize echocardiography by enabling novel research and rigorous, scalable quality improvement. Text reports are a critical part of such analyses, and ontology is a key strategy for promoting interoperability of heterogeneous data through consistent tagging. Currently, echocardiogram reports include both structured and free text and vary across institutions, hampering attempts to mine text for useful insights. Natural language processing (NLP) can he...

BackgroundHeart failure (HF) is a major cause of death globally. Prediction of HF risk and early initiation of treatment could mitigate disease progression. ObjectivesThe study aimed to improve the prediction accuracy of HF by integrating genome-wide association studies (GWAS)- and electronic health records (EHR)-derived risk scores. MethodsWe previously performed the largest HF GWAS to date within the Global Biobank Meta-analysis Initiative to create a polygenic risk score (PRS). To extract c...

We investigate whether post-hoc calibration improves the clinical trustworthiness of heart-disease predictions beyond conventional accuracy metrics. Using a structured clinical dataset (1,025 records; 85/15 train-test split), we benchmarked six classifiers logistic regression, SVM, k-nearest neighbors, naive Bayes, random forest, and XGBoost on accuracy, ROC-AUC, precision, recall, and F1, and then evaluated probability quality before and after Platt (sigmoid) and isotonic calibration using Brie...

BackgroundThe 12-lead electrocardiogram (ECG) remains a cornerstone of cardiac diagnostics, yet existing artificial intelligence (AI) solutions for automated interpretation often lack generalizability, remain closed-source, and are primarily trained using supervised learning, limiting their adaptability across diverse clinical settings. To address these challenges, we developed and compared two open-source foundational ECG models: DeepECG-SSL, a self-supervised learning model, and DeepECG-SL, a ...

BackgroundIdentifying patients with low left ventricular ejection fraction (LVEF) in the emergency department using an electrocardiogram (ECG) may optimize acute heart failure (AHF) management. We aimed to assess the efficacy of 527 automated 12-lead ECG features for estimating LVEF among patients with AHF. MethodMedical records of patients >18 years old and AHF-related ICD codes, demographics, LVEF %, comorbidities, and medication were analyzed. Least Absolute Shrinkage and Selection Operator ...

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...

BackgroundObjective assessment of left ventricular function remains a key prognosticator that is used to guide therapeutic decisions for patients with heart failure (HF). However, the left ventricular ejection fraction (LVEF) is dynamic, with worsening LVEF linked to increased morbidity and mortality. Identifying patients at risk of LVEF decline would improve prognostication and enable timely therapeutic intervention. MethodsWe developed a deep learning model to Predict changes in left ventricU...