DeepStrain evidence of asymptomatic left ventricular diastolic and systolic dysfunction in young adults with cardiac risk factors Articles uri icon

authors

  • MORALES, MANUEL A.
  • SNEL, GERT J. H.
  • VAN DEN BOOMEN, MAAIKE
  • BORRA, RONALD J. H.
  • VAN DEURSEN, VINCENT M.
  • SLART, RIEMER H. J. A.
  • IZQUIERDO GARCÍA, DAVID
  • PRAKKEN, NIEK H. J.
  • CATANA, CIPRIAN

publication date

  • April 2022

issue

  • 831080

volume

  • 9

International Standard Serial Number (ISSN)

  • 2297-055X

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.

subjects

  • Biology and Biomedicine

keywords

  • deep learning; myocardial strain; young adults; risk factors; cardiac mri; left ventricular dysfunction