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Deep Learning Synthetic Strain: Quantitative Assessment of Regional Myocardial Wall Motion at MRI. | LitMetric

Deep Learning Synthetic Strain: Quantitative Assessment of Regional Myocardial Wall Motion at MRI.

Radiol Cardiothorac Imaging

From the Departments of Bioengineering (E.M.M.) and Radiology (R.S.C., L.D.H., M.H., K.J., S.K., A.H.), University of California, San Diego, 9300 Campus Point Dr, MC 0841, La Jolla, CA 92037-0841; Department of Medicine, University of Virginia, Charlottesville, Va (S.W., A.P.); and Meyer Children's Hospital IRCCS, Cardiac Imaging Unit, Pediatric Cardiology, University of Florence, Florence, Italy (C.Z., F.R.).

Published: June 2023

AI Article Synopsis

  • A study evaluated a new deep learning algorithm (DLSS) that infers myocardial velocity and detects wall motion abnormalities from cardiac MRI images in patients with ischemic heart disease.
  • The research involved data from 223 cardiac MRI exams, measuring strain in healthy individuals and assessing the algorithm's performance against many cardiothoracic radiologists' interpretations in patients with coronary artery disease.
  • DLSS demonstrated strong performance with an area under the curve of 0.90, achieving sensitivity and specificity rates of 86% and 85%, indicating it performs comparably to expert radiologists in identifying heart issues.

Article Abstract

Purpose: To assess the feasibility of a newly developed algorithm, called (DLSS), to infer myocardial velocity from cine steady-state free precession (SSFP) images and detect wall motion abnormalities in patients with ischemic heart disease.

Materials And Methods: In this retrospective study, DLSS was developed by using a data set of 223 cardiac MRI examinations including cine SSFP images and four-dimensional flow velocity data (November 2017 to May 2021). To establish normal ranges, segmental strain was measured in 40 individuals (mean age, 41 years ± 17 [SD]; 30 men) without cardiac disease. Then, DLSS performance in the detection of wall motion abnormalities was assessed in a separate group of patients with coronary artery disease, and these findings were compared with consensus results of four independent cardiothoracic radiologists (ground truth). Algorithm performance was evaluated by using receiver operating characteristic curve analysis.

Results: Median peak segmental radial strain in individuals with normal cardiac MRI findings was 38% (IQR: 30%-48%). Among patients with ischemic heart disease (846 segments in 53 patients; mean age, 61 years ± 12; 41 men), the Cohen κ among four cardiothoracic readers for detecting wall motion abnormalities was 0.60-0.78. DLSS achieved an area under the receiver operating characteristic curve of 0.90. Using a fixed 30% threshold for abnormal peak radial strain, the algorithm achieved a sensitivity, specificity, and accuracy of 86%, 85%, and 86%, respectively.

Conclusion: The deep learning algorithm had comparable performance with subspecialty radiologists in inferring myocardial velocity from cine SSFP images and identifying myocardial wall motion abnormalities at rest in patients with ischemic heart disease. Neural Networks, Cardiac, MR Imaging, Ischemia/Infarction © RSNA, 2023.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10316298PMC
http://dx.doi.org/10.1148/ryct.220202DOI Listing

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