AI Based CMR Assessment of Biventricular Function: Clinical Significance of Intervendor Variability and Measurement Errors.

OBJECTIVES

The aim of this study was to determine whether left ventricular ejection fraction (LVEF) and right ventricular ejection fraction (RVEF) and left ventricular mass (LVM) measurements made using 3 fully automated deep learning (DL) algorithms are accurate and interchangeable and can be used to classify ventricular function and risk-stratify patients as accurately as an expert.

BACKGROUND

Artificial intelligence is increasingly used to assess cardiac function and LVM from cardiac magnetic resonance images.

METHODS

Two hundred patients were identified from a registry of individuals who underwent vasodilator stress cardiac magnetic resonance. LVEF, LVM, and RVEF were determined using 3 fully automated commercial DL algorithms and by a clinical expert (CLIN) using conventional methodology. Additionally, LVEF values were classified according to clinically important ranges: <35%, 35% to 50%, and ≥50%. Both ejection fraction values and classifications made by the DL ejection fraction approaches were compared against CLIN ejection fraction reference. Receiver-operating characteristic curve analysis was performed to evaluate the ability of CLIN and each of the DL classifications to predict major adverse cardiovascular events.

RESULTS

Excellent correlations were seen for each DL-LVEF compared with CLIN-LVEF (r = 0.83-0.93). Good correlations were present between DL-LVM and CLIN-LVM (r = 0.75-0.85). Modest correlations were observed between DL-RVEF and CLIN-RVEF (r = 0.59-0.68). A >10% error between CLIN and DL ejection fraction was present in 5% to 18% of cases for the left ventricle and 23% to 43% for the right ventricle. LVEF classification agreed with CLIN-LVEF classification in 86%, 80%, and 85% cases for the 3 DL-LVEF approaches. There were no differences among the 4 approaches in associations with major adverse cardiovascular events for LVEF, LVM, and RVEF.

CONCLUSIONS

This study revealed good agreement between automated and expert-derived LVEF and similarly strong associations with outcomes, compared with an expert. However, the ability of these automated measurements to accurately classify left ventricular function for treatment decision remains limited. DL-LVM showed good agreement with CLIN-LVM. DL-RVEF approaches need further refinements.

Informations de copyright

Copyright © 2022 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Funding Support and Author Disclosures This project was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health through grant 5UL1TR002389-02, which funds the Institute for Translational Medicine. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Dr A.R. Patel has received research support from Philips, Arterys, CircleCVI, and Neosoft. Dr H. Patel was funded by a T32 Cardiovascular Sciences Training Grant (5T32HL7381). All authors have reported that they have no relationships relevant to the contents of this paper to disclose.