Di Marco, A. et al. (2017) Late gadolinium enhancement and the risk for ventricular arrhythmias or sudden death in dilated cardiomyopathy: systematic review and meta-analysis. JACC: Heart Failure, 5(1), pp. 28-38. (doi:10.1016/j.jchf.2016.09.017) This is the author's final accepted version.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/133553/ Background: Risk stratification for SCD in DCM needs to be improved.
Both CMR and serial BNP testing provide a better prediction of LVRR in recent-onset DCM than EMB results, other biomarkers, and the conventional methods of follow-up.
To develop a deep learning-based method for fully automated quantification of left ventricular (LV) function from shortaxis cine MR images and to evaluate its performance in a multivendor and multicenter setting. Materials and Methods: This retrospective study included cine MRI data sets obtained from three major MRI vendors in four medical centers from 2008 to 2016. Three convolutional neural networks (CNNs) with the U-NET architecture were trained on data sets of increasing variability: (a) a single-vendor, single-center, homogeneous cohort of 100 patients (CNN1); (b) a single-vendor, multicenter, heterogeneous cohort of 200 patients (CNN2); and (c) a multivendor, multicenter, heterogeneous cohort of 400 patients (CNN3). All CNNs were tested on an independent multivendor, multicenter data set of 196 patients. CNN performance was evaluated with respect to the manual annotations from three experienced observers in terms of (a) LV detection accuracy, (b) LV segmentation accuracy, and (c) LV functional parameter accuracy. Automatic and manual results were compared with the paired Wilcoxon test, Pearson correlation, and Bland-Altman analysis. Results: CNN3 achieved the highest performance on the independent testing data set. The average perpendicular distance compared with manual analysis was 1.1 mm 6 0.3 for CNN3, compared with 1.5 mm 6 1.0 for CNN1 (P , .05) and 1.3 mm 6 0.6 for CNN2 (P , .05). The LV function parameters derived from CNN3 showed a high correlation (r 2 0.98) and agreement with those obtained by experts for data sets from different vendors and centers. Conclusion: A deep learning-based method trained on a data set with high variability can achieve fully automated and accurate cine MRI analysis on multivendor, multicenter cine MRI data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.