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Cited by 46Pulmonary Hypertension Research and TreatmentsECG Monitoring and AnalysisArtificial Intelligence in Healthcare

Artificial Intelligence-Enabled Electrocardiogram Improves the Diagnosis and Prediction of Mortality in Patients with Pulmonary Hypertension

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Liu, Chih-Min, Shih, Edward S.C., Chen, Jhih-Yu, Huang, Chih-Han, Wu, I-Chien, Chen, Pei-Fen, Higa, Satoshi, Yagi, Nobumori +3 more·2022·JACC: Asia
DOI 10.1016/j.jacasi.2022.02.008PMC PMC9627911
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Abstract

Background: Pulmonary hypertension is a disabling and life-threatening cardiovascular disease. Early detection of elevated pulmonary artery pressure (ePAP) is needed for prompt diagnosis and treatment to avoid detrimental consequences of pulmonary hypertension. Objectives: This study sought to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify patients with ePAP and related prognostic implications. Methods: 50 mm Hg by transthoracic echocardiography). The model was evaluated on independent data sets, including an external cohort of patients from Japan. Results: Tests of 10-fold cross-validation neural-network deep learning showed that the area under the receiver-operating characteristic curve of the AI model was 0.88 (sensitivity 81.0%; specificity 79.6%) for detecting ePAP. The diagnostic performance was consistent across age, sex, and various comorbidities (diagnostic odds ratio >8 for most factors examined). At 6-year follow-up, the patients predicted by the AI model to have ePAP were independently associated with higher cardiovascular mortality (HR: 3.69). Similar diagnostic performance and prediction for cardiovascular mortality could be replicated in the external cohort. Conclusions: The ECG-based AI model identified patients with ePAP and predicted their future risk for cardiovascular mortality. This model could serve as a useful clinical test to identify patients with pulmonary hypertension so that treatment can be initiated early to improve their survival prognosis.

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  • Routine ECGs may contain information reflecting pulmonary vascular load, right ventricular pressure overload, or secondary electrical changes caused by altered cardiopulmonary hemodynamics.

    “These findings support the possibility that routine ECGs may contain information reflecting pulmonary vascular load, right ventricular pressure overload, or secondary electrical changes caused by altered cardiopulmonary hemodynamics.”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [36]

    The Results (page not provided) show that ECG leads V1–V3 helped the AI model predict elevated pulmonary artery pressure and that their signals were consistent with right ventricular hypertrophy. This supports the possibility that ECGs contain relevant information, but the paper does not establish that the signals reflect pulmonary vascular load or electrical changes caused by altered cardiopulmonary hemodynamics. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • The model for elevated estimated pulmonary artery pressure performed consistently across important clinical subgroups and was externally validated in an independent cohort.

    “The model also performed consistently across important clinical subgroups and was externally validated in an independent cohort [36].”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [36]

  • A deep learning ECG model detected elevated estimated pulmonary artery pressure with an AUC of approximately 0.88, sensitivity of about 81%, and specificity of about 80%.

    “Using paired ECG and echocardiographic data from more than 41,000 patients, the model identified elevated estimated pulmonary artery pressure with an AUC of approximately 0.88, along with sensitivity and specificity of about 81% and 80%, respectively.”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [36]

  • 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 [36]

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Authors
Liu, C.-M., Shih, E. S. C., Chen, J.-Y., Huang, C.-H., Wu, I.-C., Chen, P.-F., Higa, S., Yagi, N., Hu, Y.-F., Hwang, M.-J., & Chen, S.-A.
Affiliations
  • National Yang Ming Chiao Tung University (TW)
  • Institute of Biomedical Sciences, Academia Sinica (TW)
  • National Taiwan University (TW)
  • National Health Research Institutes (TW)
  • Nakagami Hospital (JP)
Corresponding author
  • Hu, Yu-Feng
  • Hwang, Ming-Jing
ORCID
Venue
JACC: Asia
Publisher
Elsevier BV
Published
2022-06
Volume
2
Issue
3
Pages
258-270
ISSN
2772-3747 (ISSN-L)
DOI
10.1016/j.jacasi.2022.02.008
PMC
PMC9627911
OpenAlex
W4280579815
Cited by
46
References
4
Citations by year
2026: 10 · 2025: 11 · 2024: 19 · 2023: 4 · 2022: 2
FWCI
5.81
Type
journal article
Language
en
Open access
Licence
Locations
  • JACC Asia (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • Ministry of Science and Technology, Taiwan (MOST 109-2628-B-075-017, MOST 110-2628-B-075-015, 110-2314-B-075-063-MY3, 110-2321-B-075-002, 108-2628-B-075-003)
  • Taipei Veterans General Hospital (V109C-070, V110C-039, VN109-03, VGH108C-019, VN108-12, V110B-043)
  • Academia Sinica (AS-TM-109-01-05, AS-TM-110-01-01)
  • National Health Research Institutes (NHRI-EX108-10513SC, NHRI-109BCC0-MF-202014-02)
Topics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Pulmonary Hypertension Research and Treatments
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
pulmonary hypertension, electrocardiogram, artificial intelligence, deep learning, cardiovascular mortality prediction, transthoracic echocardiography, early diagnosis

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