Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography
Abstract
Background Screening and early diagnosis of pulmonary hypertension (PH) are critical for managing progression and preventing associated mortality; however, there are no tools for this purpose. We developed and validated an artificial intelligence (AI) algorithm for predicting PH using electrocardiography (ECG). Methods This historical cohort study included data from consecutive patients from 2 hospitals. The patients in one hospital were divided into derivation (56,670 ECGs from 24,202 patients) and internal validation (3,174 ECGs from 3,174 patients) datasets, whereas the patients in the other hospital were included in only an external validation (10,865 ECGs from 10,865 patients) dataset. An AI algorithm based on an ensemble neural network was developed using 12-lead ECG signal and demographic information from the derivation dataset. The end-point was the diagnosis of PH. In addition, the interpretable AI algorithm identified which region had the most significant effect on decision making using a sensitivity map. Results During the internal and external validation, the area under the receiver operating characteristic curve of the AI algorithm for detecting PH was 0.859 and 0.902, respectively. In the 2,939 individuals without PH at initial echocardiography, those patients that the AI defined as having a higher risk had a significantly higher chance of developing PH than those in the low-risk group (31.5% vs 5.9%, p Conclusions The AI algorithm demonstrated high accuracy for…
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Hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary hypertension AI-ECG applications remain earlier or pathway-incomplete.
“hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary hypertension remain earlier or pathway-incomplete applications.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [31]
Evidence for AI-ECG detection of pulmonary hypertension and right ventricular dysfunction remains comparatively early and predominantly retrospective, although reported discrimination has been encouraging with AUROCs of approximately 0.86-0.92 across internal and external validation cohorts.
“Evidence for AI-ECG detection of PH and right ventricular dysfunction remains comparatively early. Published studies are still predominantly retrospective, although reported discrimination has been encouraging, with AUROCs of approximately 0.86–0.92 across internal and external validation cohorts.31–33”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [31]
Among patients without known PH at baseline, those classified as high risk by the AI model had a substantially higher subsequent incidence of PH than those classified as low risk.
“Importantly, among patients without known PH at baseline, those classified as high risk by the AI model had a substantially higher subsequent incidence of PH than those classified as low risk [35].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [35]
An AI-ECG model for pulmonary hypertension using standard 12-lead ECGs and clinical variables achieved AUC values of 0.859 and 0.902 in internal and external validation cohorts.
“The model was trained using more than 56,000 ECGs and demonstrated strong diagnostic performance for PH in both internal and external validation cohorts, with reported AUC values of 0.859 and 0.902, respectively.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [35]
AI-ECG may not only identify existing pulmonary hypertension but may also detect early ECG signatures associated with future disease development.
“This suggested that AI-ECG may not only identify existing pulmonary hypertension but may also detect early ECG signatures associated with future disease development.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [35]
The pulmonary hypertension literature shows consistent discrimination across multiple cohorts and suggests that AI-ECG may identify disease before formal diagnosis.
“Overall, the pulmonary hypertension literature shows consistent discrimination across multiple cohorts and suggests that AI-ECG may identify disease before formal diagnosis.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [35]
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- Authors
- Kwon, J.-M., Kim, K.-H., Medina-Inojosa, J., Jeon, K.-H., Park, J., & Oh, B.-H.
- Affiliations
- Sejong General Hospital (KR)
- Corresponding author
- Kim, Kyung-Hee
- Venue
- The Journal of Heart and Lung Transplantation
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 39
- Issue
- 8
- Pages
- 805-814
- ISSN
- 1053-2498 (ISSN-L)
- OpenAlex
- W3013435687
- Cited by
- 108
- References
- 35
- Citations by year
- 2026: 16 · 2025: 20 · 2024: 18 · 2023: 19 · 2022: 25 · 2021: 8 · 2020: 2
- FWCI
- 8.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Heart and Lung Transplantation (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Pulmonary Hypertension Research and Treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- pulmonary hypertension, electrocardiogram, artificial intelligence, early prediction, neural network ensembles, explainable AI, sensitivity maps, 12-lead ECG, single-lead ECG, risk stratification, echocardiography
- MeSH terms
- Aged, Algorithms, Artificial Intelligence, Electrocardiography, Female, Follow-Up Studies, Humans, Hypertension, Pulmonary, Male, Middle Aged, Retrospective Studies, ROC Curve, Neural Networks, Computer, Early Diagnosis
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