Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements

作者：Faisal Alandejani, Samer Alabed, Pankaj Garg, Ze Ming Goh, Kavita Karunasaagarar, Michael Sharkey, Mahan Salehi, Ziad Aldabbagh, Krit Dwivedi, Michail Mamalakis, Pete Metherall, Johanna Uthoff, Christopher Johns, Alexander Rothman, Robin Condliffe, Abdul Hameed, Athanasios Charalampoplous, Haiping Lu, Sven Plein, John P. Greenwood, Allan Lawrie, Jim M. Wild, Patrick J. H. de Koning, David G. Kiely, Rob J. van der Geest, Andrew J. Swift · 发表于：Journal of Cardiovascular Magnetic Resonance · 年份：2022 · DOI：10.1186/s12968-022-00855-3 · 被引用次数：24 · 研究领域：Pulmonary Hypertension Research and Treatments、Cardiovascular Function and Risk Factors、Atrial Fibrillation Management and Outcomes

Right 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. A 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). All deep learning RA measurements showed higher interstudy repeatability (...