ArticleEuropean heart journal2023
Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy.
Article in European heart journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 44 citations in OpenAlex.
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review.Heart failure reviews · 2024Pooled it
- Serial AI-Enabled Electrocardiogram Trajectories During Heart Failure Treatment: A Proof-of-Concept Case Series.JACC. Case reports · 2026Article
- Interpreting Serial AI-ECG Changes During Heart Failure Therapy: From Disease Detection to Disease Tracking.JACC. Case reports · 2026Article
- Artificial Intelligence Techniques in Cardiac Neuromodulation: Mechanisms, Applications, and Pathways to Clinical Translation.Journal of arrhythmia · 2026Review
- True Left Bundle Branch Block by Strauss Criteria: Impact on CRT Response and Conduction System Pacing Trial Design-A Systematic Evidence Synthesis Toward Personalized Patient Selection.Journal of personalized medicine · 2026Review
- Strauss-Defined LBBB Identifies Patients With Improved Outcomes After CRT-D: A Comparative Cohort Study.Clinical cardiology · 2026Article
- Artificial Intelligence Across the Echocardiographic Workflow: A Narrative Review for Clinicians.Cureus · 2026Review
- Signal or noise? Evaluating commonly used attribution methods for explaining deep neural networks in electrocardiogram classification.European heart journal. Digital health · 2026Article
- Integrating multimodal intelligence in heart failure: AI-driven risk prediction, precision diagnosis, phenotyping, personalized treatment, and prognosis.Chinese medical journal · 2026Review
- Artificial intelligence in heart failure.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2026Review
- Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review.Korean circulation journal · 2026Review
- Rapid prediction of cardiac activation in the left ventricle with geometric deep learning: a step towards cardiac resynchronization therapy planning.NPJ digital medicine · 2026Article
- Clustering in dilated cardiomyopathy at initial evaluation: An effective tool for clinical stratification.European journal of heart failure · 2025Article
- Prognostic Significance of AI-Enhanced ECG for Emergency Department Patients.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.Journal of translational medicine · 2025Review
- Expanding artificial intelligence to understudied populations: congenital heart disease as the next frontier.European heart journal · 2025Article
- Optimizing outcomes from cardiac resynchronization therapy: what do recent data and insights say?Expert review of cardiovascular therapy · 2024Review
- Artificial intelligence estimated electrocardiographic age as a recurrence predictor after atrial fibrillation catheter ablation.NPJ digital medicine · 2024Article
- Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review.Current cardiology reports · 2024Review
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Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
Abstract
aimsThis study aims to identify and visualize electrocardiogram (ECG) features using an explainable deep learning-based algorithm to predict cardiac resynchronization therapy (CRT) outcome. Its performance is compared with current guideline ECG criteria and QRSAREA. METHODS AND
resultsA deep learning algorithm, trained on 1.1 million ECGs from 251 473 patients, was used to compress the median beat ECG, thereby summarizing most ECG features into only 21 explainable factors (FactorECG). Pre-implantation ECGs of 1306 CRT patients from three academic centres were converted into their respective FactorECG. FactorECG predicted the combined clinical endpoint of death, left ventricular assist device, or heart transplantation [c-statistic 0.69, 95% confidence interval (CI) 0.66-0.72], significantly outperforming QRSAREA and guideline ECG criteria [c-statistic 0.61 (95% CI 0.58-0.64) and 0.57 (95% CI 0.54-0.60), P < 0.001 for both]. The addition of 13 clinical variables was of limited added value for the FactorECG model when compared with QRSAREA (Δ c-statistic 0.03 vs. 0.10). FactorECG identified inferolateral T-wave inversion, smaller right precordial S- and T-wave amplitude, ventricular rate, and increased PR interval and P-wave duration to be important predictors for poor outcome. An online visualization tool was created to provide interactive visualizations (https://crt.ecgx.ai).
conclusionRequiring only a standard 12-lead ECG, FactorECG held superior discriminative ability for the prediction of clinical outcome when compared with guideline criteria and QRSAREA, without requiring additional clinical variables. End-to-end automated visualization of ECG features allows for an explainable algorithm, which may facilitate rapid uptake of this personalized decision-making tool in CRT.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.