The deep learning synthetic strain algorithm detected myocardial wall motion abnormalities at rest in patients with ischemic heart disease with an area under the receiver operating characteristic curve of 0.90.
Observational (n=316)
Blinded to clinical information
No
Does a deep learning synthetic strain algorithm accurately detect regional myocardial wall motion abnormalities from cine SSFP images compared to expert radiologist consensus in patients with ischemic heart disease?
A deep learning algorithm can accurately infer myocardial velocities and detect regional wall motion abnormalities from standard cine SSFP MRI images, performing comparably to subspecialty radiologists.
Effect estimate: AUC 0.90
Purpose To assess the feasibility of a newly developed algorithm, called deep learning synthetic strain (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. Keywords: Neural Networks, Cardiac, MR Imaging, Ischemia/Infarction Supplemental material is available for this article. © RSNA, 2023
Masutani et al. (Thu,) conducted a observational in Ischemic heart disease (n=316). Deep learning synthetic strain (DLSS) algorithm vs. Consensus of four independent cardiothoracic radiologists was evaluated on Detection of wall motion abnormalities (AUC 0.90). The deep learning synthetic strain algorithm detected myocardial wall motion abnormalities at rest in patients with ischemic heart disease with an area under the receiver operating characteristic curve of 0.90.