Right and left ventricular interactions, strain, and remodeling in repaired pulmonary stenosis patients with preserved right ventricular ejection fraction: A cardiac magnetic resonance study

2020 ◽  
Vol 52 (1) ◽  
pp. 129-138
Author(s):  
Shi‐yu Wang ◽  
Rong‐zhen OuYang ◽  
Li‐wei Hu ◽  
Wei‐hui Xie ◽  
Ya‐feng Peng ◽  
...  
Keyword(s):  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Ejection Fraction ◽  
Right Ventricular ◽  
Pulmonary Stenosis ◽  
Right Ventricular Ejection Fraction ◽  
Left Ventricular ◽  
Magnetic Resonance Study ◽  
Ventricular Ejection Fraction ◽  
Ventricular Interactions

Abstract 16189: Cardiovascular Risk Prediction Using Fully Automated Artificial Intelligence Algorithms for the Assessment of Right Ventricular Function From Cardiac Magnetic Resonance Images

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
shuo wang ◽  
Hena Patel ◽  
Tamari Miller ◽  
Keith Ameyaw ◽  
Akhil Narang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Right Ventricular ◽  
Roc Analysis ◽  
Right Ventricular Ejection Fraction ◽  
Magnetic Resonance Images ◽  
Major Adverse Cardiovascular Events ◽  
Ventricular Ejection Fraction ◽  
Rv Function

Background: It is unclear whether artificial intelligence (AI) can provide automatic solutions to measure right ventricular ejection fraction (RVEF), due to the complex RV geometry. Although several deep learning (DL) algorithms are available to quantify RVEF from cardiac magnetic resonance (CMR) images, there has been no systematic comparison of these algorithms, and the prognostic value of these automated measurements is unknown. We aimed to determine whether RVEF measurements made using DL algorithms could be used to risk stratify patients similarly to measurements made by an expert. Methods: We identified from a pre-existing registry 200 patients who underwent CMR. RVEF was determined using 3 fully automated commercial DL algorithms (DL-RVEF) and also by a clinical expert (CLIN-RVEF) using conventional methodology. Each of the DL-RVEF approaches was compared against CLIN-RVEF using linear regression and Bland-Altman analyses. In addition, RVEF values were classified according to clinically important cutoffs: <35%, 35-50%, ≥50%, and rates of disagreement with the reference classification were determined. ROC analysis was performed to evaluate the ability of CLIN-RVEF and each of the DL-RVEF based classifications to predict major adverse cardiovascular events (MACE). Results: The CLIN-RVEF and the three DL-RVEFs were obtained in all patients. We found only modest correlations between DL-RVEF and CLIN-RVEF (figure). The DL-RVEF algorithms had accuracy ranging from 0.59 to 0.78 for categorizing RV function. Nevertheless, ROC analysis showed no significant differences between the 4 approaches in predicting MACE, as reflected by respective AUC values of 0.68, 0.69, 0.64 and 0.63. Conclusions: Although the automated algorithms predicted patient outcomes as well as the CLIN-RVEF, the agreement between DL-RVEF and the clinical expert’s measurements was not optimal. DL approaches need further refinements to improve automated assessment of RV function.

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