Deep Learning-Derived Myocardial Strain

Alan C Kwan; Ernest W Chang; Ishan Jain; John Theurer; Xiu Tang; Nadia Francisco; Francois Haddad; David Liang; Alexandra Fábián; Andrea Ferencz; Neal Yuan; Béla Merkely; Robert Siegel; Susan Cheng; Attila Kovács; Márton Tokodi; David Ouyang

doi:10.1016/j.jcmg.2024.01.011

Deep Learning-Derived Myocardial Strain

JACC Cardiovasc Imaging. 2024 Mar 12:S1936-878X(24)00063-9. doi: 10.1016/j.jcmg.2024.01.011. Online ahead of print.

Authors

Alan C Kwan¹, Ernest W Chang², Ishan Jain³, John Theurer³, Xiu Tang⁴, Nadia Francisco⁴, Francois Haddad⁴, David Liang⁴, Alexandra Fábián⁵, Andrea Ferencz⁵, Neal Yuan⁶, Béla Merkely⁵, Robert Siegel³, Susan Cheng³, Attila Kovács⁷, Márton Tokodi⁷, David Ouyang⁸

Affiliations

¹ Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA. Electronic address: alan.kwan@cshs.org.
² Leon H. Charney Division of Cardiology, Department of Medicine, New York University Grossman School of Medicine, New York, New York, USA.
³ Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
⁴ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA.
⁵ Heart and Vascular Center, Semmelweis University, Budapest, Hungary.
⁶ Division of Cardiology, Department of Medicine, San Francisco VA, University of California-San Francisco, San Francisco, California, USA.
⁷ Heart and Vascular Center, Semmelweis University, Budapest, Hungary; Department of Surgical Research and Techniques, Semmelweis University, Budapest, Hungary.
⁸ Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA. Electronic address: David.Ouyang@cshs.org.

PMID: 38551533
DOI: 10.1016/j.jcmg.2024.01.011

Abstract

Background: Echocardiographic strain measurements require extensive operator experience and have significant intervendor variability. Creating an automated, open-source, vendor-agnostic method to retrospectively measure global longitudinal strain (GLS) from standard echocardiography B-mode images would greatly improve post hoc research applications and may streamline patient analyses.

Objectives: This study was seeking to develop an automated deep learning strain (DLS) analysis pipeline and validate its performance across multiple applications and populations.

Methods: Interobserver/-vendor variation of traditional GLS, and simulated effects of variation in contour on speckle-tracking measurements were assessed. The DLS pipeline was designed to take semantic segmentation results from EchoNet-Dynamic and derive longitudinal strain by calculating change in the length of the left ventricular endocardial contour. DLS was evaluated for agreement with GLS on a large external dataset and applied across a range of conditions that result in cardiac hypertrophy.

Results: In patients scanned by 2 sonographers using 2 vendors, GLS had an intraclass correlation of 0.29 (95% CI: -0.01 to 0.53, P = 0.03) between vendor measurements and 0.63 (95% CI: 0.48-0.74, P < 0.001) between sonographers. With minor changes in initial input contour, step-wise pixel shifts resulted in a mean absolute error of 3.48% and proportional strain difference of 13.52% by a 6-pixel shift. In external validation, DLS maintained moderate agreement with 2-dimensional GLS (intraclass correlation coefficient [ICC]: 0.56, P = 0.002) with a bias of -3.31% (limits of agreement: -11.65% to 5.02%). The DLS method showed differences (P < 0.0001) between populations with cardiac hypertrophy and had moderate agreement in a patient population of advanced cardiac amyloidosis: ICC was 0.64 (95% CI: 0.53-0.72), P < 0.001, with a bias of 0.57%, limits of agreement of -4.87% to 6.01% vs 2-dimensional GLS.

Conclusions: The open-source DLS provides lower variation than human measurements and similar quantitative results. The method is rapid, consistent, vendor-agnostic, publicly released, and applicable across a wide range of imaging qualities.

Keywords: deep learning; echocardiography; longitudinal strain.