DeepStrain Evidence of Asymptomatic Left Ventricular Diastolic and Systolic Dysfunction in Young Adults With Cardiac Risk Factors

Manuel A Morales; Gert J H Snel; Maaike van den Boomen; Ronald J H Borra; Vincent M van Deursen; Riemer H J A Slart; David Izquierdo-Garcia; Niek H J Prakken; Ciprian Catana

doi:10.3389/fcvm.2022.831080

DeepStrain Evidence of Asymptomatic Left Ventricular Diastolic and Systolic Dysfunction in Young Adults With Cardiac Risk Factors

Front Cardiovasc Med. 2022 Apr 11:9:831080. doi: 10.3389/fcvm.2022.831080. eCollection 2022.

Authors

Manuel A Morales^{1

2}, Gert J H Snel³, Maaike van den Boomen^{1

3

4}, Ronald J H Borra^{3

5}, Vincent M van Deursen⁶, Riemer H J A Slart^{5

7}, David Izquierdo-Garcia^{1

2}, Niek H J Prakken³, Ciprian Catana¹

Affiliations

¹ Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
² Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, United States.
³ Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
⁴ Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
⁵ Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
⁶ Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
⁷ Department of Biomedical Photonic Imaging, Faculty of Science and Technology, University of Twente, Enschede, Netherlands.

Abstract

Purpose: To evaluate if a fully-automatic deep learning method for myocardial strain analysis based on magnetic resonance imaging (MRI) cine images can detect asymptomatic dysfunction in young adults with cardiac risk factors.

Methods: An automated workflow termed DeepStrain was implemented using two U-Net models for segmentation and motion tracking. DeepStrain was trained and tested using short-axis cine-MRI images from healthy subjects and patients with cardiac disease. Subsequently, subjects aged 18-45 years were prospectively recruited and classified among age- and gender-matched groups: risk factor group (RFG) 1 including overweight without hypertension or type 2 diabetes; RFG2 including hypertension without type 2 diabetes, regardless of overweight; RFG3 including type 2 diabetes, regardless of overweight or hypertension. Subjects underwent cardiac short-axis cine-MRI image acquisition. Differences in DeepStrain-based left ventricular global circumferential and radial strain and strain rate among groups were evaluated.

Results: The cohort consisted of 119 participants: 30 controls, 39 in RFG1, 30 in RFG2, and 20 in RFG3. Despite comparable (>0.05) left-ventricular mass, volumes, and ejection fraction, all groups (RFG1, RFG2, RFG3) showed signs of asymptomatic left ventricular diastolic and systolic dysfunction, evidenced by lower circumferential early-diastolic strain rate (<0.05, <0.001, <0.01), and lower septal circumferential end-systolic strain (<0.001, <0.05, <0.001) compared with controls. Multivariate linear regression showed that body surface area correlated negatively with all strain measures (<0.01), and mean arterial pressure correlated negatively with early-diastolic strain rate (<0.01).

Conclusion: DeepStrain fully-automatically provided evidence of asymptomatic left ventricular diastolic and systolic dysfunction in asymptomatic young adults with overweight, hypertension, and type 2 diabetes risk factors.

Keywords: cardiac MRI; deep learning; left ventricular dysfunction; myocardial strain; risk factors; young adults.