ArticlePulmonary circulation2023
Automatic echocardiographic evaluation of the probability of pulmonary hypertension using machine learning.
Article in Pulmonary circulation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 4 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 4 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
- Comparative accuracy of artificial intelligence versus manual interpretation in detecting pulmonary hypertension across chest imaging modalities: a diagnostic test accuracy meta-analysis.Frontiers in artificial intelligence · 2025Pooled it
- Assessing the precision of machine learning for diagnosing pulmonary arterial hypertension: a systematic review and meta-analysis of diagnostic accuracy studies.Frontiers in cardiovascular medicine · 2024Pooled it
- 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-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- Artificial intelligence enhanced contemporary pulmonary hypertension care.American heart journal plus : cardiology research and practice · 2025Review
- Personalized Medicine in Pulmonary Arterial Hypertension: Utilizing Artificial Intelligence for Death Prevention.Journal of clinical medicine · 2025Article
- Leveraging Artificial Intelligence for the Diagnosis of Systemic Sclerosis Associated Pulmonary Arterial Hypertension: Opportunities, Challenges, and Future Perspectives.Advances in respiratory medicine · 2025Review
- Clinical research on echocardiographic screening for pulmonary hypertension: TRV >2.8 m/s and mPAP >20 mmHg.Quantitative imaging in medicine and surgery · 2025Article
- Technical Challenges in Studying Infectious Disease-Associated Pulmonary Hypertension in Low- and Middle-Income Countries with Limited Resources.Infectious disease reports · 2025Review
- Automatic echocardiographic evaluation of the probability of pulmonary hypertension using machine learning.Pulmonary circulation · 2023Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Echocardiography, a simple and noninvasive tool, is the first choice for screening pulmonary hypertension (PH). However, accurate assessment of PH, incorporating both the pulmonary artery pressures and additional signs for PH remained unsatisfied. Thus, this study aimed to develop a machine learning (ML) model that can automatically evaluate the probability of PH. This cohort included data from 346 (275 for training set and internal validation set and 71 for external validation set) patients with suspected PH patients and receiving right heart catheterization. Echocardiographic images on parasternal short axis-papillary muscle level (PSAX-PML) view from all patients were collected, labeled, and preprocessed. Local features from each image were extracted and subsequently integrated to build a ML model. By adjusting the parameters of the model, the model with the best prediction effect is finally constructed. We used receiver-operating characteristic analysis to evaluate model performance and compared the ML model with the traditional methods. The accuracy of the ML model for diagnosis of PH was significantly higher than the traditional method (0.945 vs. 0.892,
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Registered trials
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 Socratic graph.