ArticleJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance2022
Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements.
Article in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH).Medicina (Kaunas, Lithuania) · 2025Pooled it
- Quality of reporting in AI cardiac MRI segmentation studies - A systematic review and recommendations for future studies.Frontiers in cardiovascular medicine · 2022Pooled it
- 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
- Article
- Towards a cardiovascular magnetic resonance foundation model for multi-task cardiac image analysis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Cardiac Functional Assessment by Magnetic Resonance Imaging.Cardiology discovery · 2024Review
- Prospective Human Validation of Artificial Intelligence Interventions in Cardiology: A Scoping Review.JACC. Advances · 2024Article
- Development and validation of AI-derived segmentation of four-chamber cine cardiac magnetic resonance.European radiology experimental · 2024Article
- Validation of Left Atrial Volume Correction for Single Plane Method on Four-Chamber Cine Cardiac MRI.Tomography (Ann Arbor, Mich.) · 2024Article
- Establishing minimally important differences for cardiac MRI end-points in pulmonary arterial hypertension.The European respiratory journal · 2023Article
- Assessment of Right Ventricular Function-a State of the Art.Current heart failure reports · 2023Review
- How should studies using AI be reported? lessons from a systematic review in cardiac MRI.Frontiers in radiology · 2023Article
- The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance-A Comprehensive Review.Journal of clinical medicine · 2022Review
- Semi-automatic thresholding of RV trabeculation improves repeatability and diagnostic value in suspected pulmonary hypertension.Frontiers in cardiovascular medicine · 2022Article
- Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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26 authors.
Funding
Abstract
backgroundRight atrial (RA) area predicts mortality in patients with pulmonary hypertension, and is recommended by the European Society of Cardiology/European Respiratory Society pulmonary hypertension guidelines. The advent of deep learning may allow more reliable measurement of RA areas to improve clinical assessments. The aim of this study was to automate cardiovascular magnetic resonance (CMR) RA area measurements and evaluate the clinical utility by assessing repeatability, correlation with invasive haemodynamics and prognostic value.
methodsA deep learning RA area CMR contouring model was trained in a multicentre cohort of 365 patients with pulmonary hypertension, left ventricular pathology and healthy subjects. Inter-study repeatability (intraclass correlation coefficient (ICC)) and agreement of contours (DICE similarity coefficient (DSC)) were assessed in a prospective cohort (n = 36). Clinical testing and mortality prediction was performed in n = 400 patients that were not used in the training nor prospective cohort, and the correlation of automatic and manual RA measurements with invasive haemodynamics assessed in n = 212/400. Radiologist quality control (QC) was performed in the ASPIRE registry, n = 3795 patients. The primary QC observer evaluated all the segmentations and recorded them as satisfactory, suboptimal or failure. A second QC observer analysed a random subcohort to assess QC agreement (n = 1018).
resultsAll deep learning RA measurements showed higher interstudy repeatability (ICC 0.91 to 0.95) compared to manual RA measurements (1st observer ICC 0.82 to 0.88, 2nd observer ICC 0.88 to 0.91). DSC showed high agreement comparing automatic artificial intelligence and manual CMR readers. Maximal RA area mean and standard deviation (SD) DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 is 92.4 ± 3.5 cm
conclusionAutomatic artificial intelligence CMR derived RA size and function are accurate, have excellent repeatability, moderate associations with invasive haemodynamics and predict mortality.
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