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@article{Alabed:2022:10.1148/radiol.212929,
author = {Alabed, S and Alandejani, F and Dwivedi, K and Karunasaagarar, K and Sharkey, M and Garg, P and de, Koning PJH and Tóth, A and Shahin, Y and Johns, C and Mamalakis, M and Stott, S and Capener, D and Wood, S and Metherall, P and Rothman, AMK and Condliffe, R and Hamilton, N and Wild, JM and O'Regan, DP and Lu, H and Kiely, DG and van, der Geest RJ and Swift, AJ},
doi = {10.1148/radiol.212929},
journal = {Radiology},
title = {Validation of artificial intelligence cardiac MRI measurements: relationship to heart catheterization and mortality prediction},
url = {http://dx.doi.org/10.1148/radiol.212929},
volume = {305},
year = {2022}
}

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TY  - JOUR
AB  - Background Cardiac MRI measurements have diagnostic and prognostic value in the evaluation of cardiopulmonary disease. Artificial intelligence approaches to automate cardiac MRI segmentation are emerging but require clinical testing. Purpose To develop and evaluate a deep learning tool for quantitative evaluation of cardiac MRI functional studies and assess its use for prognosis in patients suspected of having pulmonary hypertension. Materials and Methods A retrospective multicenter and multivendor data set was used to develop a deep learning-based cardiac MRI contouring model using a cohort of patients suspected of having cardiopulmonary disease from multiple pathologic causes. Correlation with same-day right heart catheterization (RHC) and scan-rescan repeatability was assessed in prospectively recruited participants. Prognostic impact was assessed using Cox proportional hazard regression analysis of 3487 patients from the ASPIRE (Assessing the Severity of Pulmonary Hypertension In a Pulmonary Hypertension Referral Centre) registry, including a subset of 920 patients with pulmonary arterial hypertension. The generalizability of the automatic assessment was evaluated in 40 multivendor studies from 32 centers. Results The training data set included 539 patients (mean age, 54 years ± 20 [SD]; 315 women). Automatic cardiac MRI measurements were better correlated with RHC parameters than were manual measurements, including left ventricular stroke volume (r = 0.72 vs 0.68; P = .03). Interstudy repeatability of cardiac MRI measurements was high for all automatic measurements (intraclass correlation coefficient range, 0.79-0.99) and similarly repeatable to manual measurements (all paired t test P > .05). Automated right ventricle and left ventricle cardiac MRI measurements were associated with mortality in patients suspected of having pulmonary hypertension. Conclusion An automatic cardiac MRI measurement approach was developed and tested in a large cohort of pa
AU  - Alabed,S
AU  - Alandejani,F
AU  - Dwivedi,K
AU  - Karunasaagarar,K
AU  - Sharkey,M
AU  - Garg,P
AU  - de,Koning PJH
AU  - Tóth,A
AU  - Shahin,Y
AU  - Johns,C
AU  - Mamalakis,M
AU  - Stott,S
AU  - Capener,D
AU  - Wood,S
AU  - Metherall,P
AU  - Rothman,AMK
AU  - Condliffe,R
AU  - Hamilton,N
AU  - Wild,JM
AU  - O'Regan,DP
AU  - Lu,H
AU  - Kiely,DG
AU  - van,der Geest RJ
AU  - Swift,AJ
DO  - 10.1148/radiol.212929
PY  - 2022///
SN  - 0033-8419
TI  - Validation of artificial intelligence cardiac MRI measurements: relationship to heart catheterization and mortality prediction
T2  - Radiology
UR  - http://dx.doi.org/10.1148/radiol.212929
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35699578
UR  - https://pubs.rsna.org/doi/10.1148/radiol.212929
UR  - http://hdl.handle.net/10044/1/98957
VL  - 305
ER  -

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