ArticleThe European respiratory journal2024
An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension.
Article in The European respiratory journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in pulmonary hypertension: a systematic review.European journal of medical research · 2025Pooled it
- A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH).Medicina (Kaunas, Lithuania) · 2025Pooled it
- Diagnostic performance of artificial intelligence-enabled electrocardiography for pulmonary hypertension: a systematic review and meta-analysis.European heart journal. Digital health · 2026Review
- Single-view echocardiography-based deep learning for pulmonary hypertension detection: a multi-level supervised and explainable approach.Quantitative imaging in medicine and surgery · 2026Article
- Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence and Multi-Omics Approaches in the Precision Management of Pulmonary Hypertension: From Early Diagnosis to Therapeutic Stratification.International journal of molecular sciences · 2026Review
- Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects.Reviews in cardiovascular medicine · 2026Review
- The Electrocardiogram and Pulmonary Hypertension: When Occam'S Razor Fails.Echocardiography (Mount Kisco, N.Y.) · 2026Article
- Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network.Journal of imaging informatics in medicine · 2026Article
- Machine-Reported Electrocardiographic Right Axis Deviation or Right Ventricular Hypertrophy and Echocardiographic Pulmonary Hypertension Phenotypes and Right-Heart Abnormalities.Echocardiography (Mount Kisco, N.Y.) · 2026Observational
- Temporal Convolutional Neural Network Analysis of Magnetocardiography Signals for Detection of Pulmonary Hypertension.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial Intelligence-Enhanced Electrocardiogram for Longitudinal Evaluation of Right Ventricular Dysfunction After Acute Pulmonary Embolism: Rational and Design of CURES-CARE Study.Pulmonary circulation · 2026Article
- HIV-Associated Pulmonary Arterial Hypertension in Resource-Limited Settings: A "Forgotten" Diagnosis, a Great Imitator, and a Multi-Hit Pulmonary Vasculopathy.Pulmonary circulation · 2026Review
- External validation of ECG artificial intelligence for emergency and cardiac assessment across a large-scale U.S. healthcare system.NPJ digital medicine · 2026Article
- Highlights of the 2026 Pulmonary Vascular Research Institute Conference: Celebrating 20 Years of Progress and Innovation.Pulmonary circulation · 2026Article
- Artificial Intelligence-Driven Integration of ECG and Molecular Biomarkers in Pulmonary Embolism.International journal of molecular sciences · 2026Review
- AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions.International journal of general medicine · 2026Review
- Construction of an early diagnostic model for pulmonary hypertension based on aging-related signature genes and identification of potential therapeutic targets.Scientific reports · 2025Article
- The role of artificial intelligence and mobile health in diagnosis and management of pulmonary arterial hypertension.International journal of cardiology. Congenital heart disease · 2025Review
- Leveraging Artificial Intelligence for the Diagnosis of Systemic Sclerosis Associated Pulmonary Arterial Hypertension: Opportunities, Challenges, and Future Perspectives.Advances in respiratory medicine · 2025Review
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Authors and funding
24 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. We aimed to develop and externally validate an artificial intelligence algorithm that could serve as a PH screening tool, based on analysis of a standard 12-lead ECG.
methodsThe PH Early Detection Algorithm (PH-EDA) is a convolutional neural network developed using retrospective ECG voltage-time data, with patients classified as "PH-likely" or "PH-unlikely" (controls) based on right heart catheterisation or echocardiography. In total, 39 823 PH-likely patients and 219 404 control patients from Mayo Clinic were randomly split into training (48%), validation (12%) and test (40%) sets. ECGs taken within 1 month of PH diagnosis (diagnostic dataset) were used to train the PH-EDA at Mayo Clinic. Performance was tested on diagnostic ECGs within the test sets from Mayo Clinic (n=16 175/87 998 PH-likely/controls) and Vanderbilt University Medical Center (VUMC; n=6045/24 256 PH-likely/controls). In addition, performance was tested on ECGs taken 6-18 months (pre-emptive dataset), and up to 5 years prior to a PH diagnosis at both sites.
resultsPerformance testing yielded an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.88 in the diagnostic test sets at Mayo Clinic and VUMC, respectively, and 0.86 and 0.81, respectively, in the pre-emptive test sets. The AUC remained a minimum of 0.79 at Mayo Clinic and 0.73 at VUMC up to 5 years before diagnosis.
conclusionThe PH-EDA can detect PH at diagnosis and 6-18 months prior, demonstrating the potential to accelerate diagnosis and management of this debilitating disease.
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