ArticleEuropean heart journal. Cardiovascular Imaging2021
A machine learning cardiac magnetic resonance approach to extract disease features and automate pulmonary arterial hypertension diagnosis.
Article in European heart journal. Cardiovascular Imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 6 of them syntheses that pooled it.
What it found
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The trial behind it
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Who cites it
41 citing papers in PubMed, 6 syntheses or guidelines pooled it, 86 citations in OpenAlex.
- 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
- The Role of Artificial Intelligence in Predicting Outcomes by Cardiovascular Magnetic Resonance: A Comprehensive Systematic Review.Medicina (Kaunas, Lithuania) · 2022Pooled it
- Diagnostic test accuracy of artificial intelligence analysis of cross-sectional imaging in pulmonary hypertension: a systematic literature review.The British journal of radiology · 2021Pooled it
- Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects.Reviews in cardiovascular medicine · 2026Review
- From Promise to Practice: Real-World Readiness of Artificial Intelligence for Cardiac Magnetic Resonance Imaging.Korean journal of radiology · 2026Article
- ERS Congress 2025: highlights from the Clinical Techniques, Imaging and Endoscopy Assembly.ERJ open research · 2026Article
- Advances in the imaging of pulmonary hypertension.International journal of cardiology. Congenital heart disease · 2025Review
- Diagnostic value of fully automated CT pulmonary angiography in patients with chronic thromboembolic pulmonary hypertension and chronic thromboembolic disease.European radiology · 2025Article
- Leveraging Artificial Intelligence for the Diagnosis of Systemic Sclerosis Associated Pulmonary Arterial Hypertension: Opportunities, Challenges, and Future Perspectives.Advances in respiratory medicine · 2025Review
- Targeted nanosponge-based treatment for pulmonary hypertension: A novel approach.Journal of food and drug analysis · 2025Review
- Investigating Patients with Pulmonary Hypertension Under Detector-Based Spectral Computed Tomography.Diagnostics (Basel, Switzerland) · 2025Article
- Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension.NPJ digital medicine · 2025Article
- Review of the Current State of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging.Children (Basel, Switzerland) · 2025Review
- Development and validation of a mortality predictive model for ICU patients with primary pulmonary hypertension.Scientific reports · 2024Article
- Pathophysiology of the right ventricle and its pulmonary vascular interaction.The European respiratory journal · 2024Review
- Emerging multimodality imaging techniques for the pulmonary circulation.The European respiratory journal · 2024Review
- Imaging in chronic thromboembolic pulmonary disease: Current practice and advances.International journal of cardiology. Congenital heart disease · 2024Review
Corrections and comments
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Authors and funding
14 authors at 4 institutions in 1 country.
Funding
Abstract
aimsPulmonary arterial hypertension (PAH) is a progressive condition with high mortality. Quantitative cardiovascular magnetic resonance (CMR) imaging metrics in PAH target individual cardiac structures and have diagnostic and prognostic utility but are challenging to acquire. The primary aim of this study was to develop and test a tensor-based machine learning approach to holistically identify diagnostic features in PAH using CMR, and secondarily, visualize and interpret key discriminative features associated with PAH. METHODS AND
resultsConsecutive treatment naive patients with PAH or no evidence of pulmonary hypertension (PH), undergoing CMR and right heart catheterization within 48 h, were identified from the ASPIRE registry. A tensor-based machine learning approach, multilinear subspace learning, was developed and the diagnostic accuracy of this approach was compared with standard CMR measurements. Two hundred and twenty patients were identified: 150 with PAH and 70 with no PH. The diagnostic accuracy of the approach was high as assessed by area under the curve at receiver operating characteristic analysis (P < 0.001): 0.92 for PAH, slightly higher than standard CMR metrics. Moreover, establishing the diagnosis using the approach was less time-consuming, being achieved within 10 s. Learnt features were visualized in feature maps with correspondence to cardiac phases, confirming known and also identifying potentially new diagnostic features in PAH.
conclusionA tensor-based machine learning approach has been developed and applied to CMR. High diagnostic accuracy has been shown for PAH diagnosis and new learnt features were visualized with diagnostic potential.
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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.