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In Journal of the American Heart Association ; h5-index 70.0

Background Cardiovascular magnetic resonance imaging is considered the reference methodology for cardiac morphology and function but requires manual postprocessing. Whether novel artificial intelligence-based automated analyses deliver similar information for risk stratification is unknown. Therefore, this study aimed to investigate feasibility and prognostic implications of artificial intelligence-based, commercially available software analyses. Methods and Results Cardiovascular magnetic resonance data (n=1017 patients) from 2 myocardial infarction multicenter trials were included. Analyses of biventricular parameters including ejection fraction (EF) were manually and automatically assessed using conventional and artificial intelligence-based software. Obtained parameters entered regression analyses for prediction of major adverse cardiac events, defined as death, reinfarction, or congestive heart failure, within 1 year after the acute event. Both manual and uncorrected automated volumetric assessments showed similar impact on outcome in univariate analyses (left ventricular EF, manual: hazard ratio [HR], 0.93 [95% CI 0.91-0.95]; P<0.001; automated: HR, 0.94 [95% CI, 0.92-0.96]; P<0.001) and multivariable analyses (left ventricular EF, manual: HR, 0.95 [95% CI, 0.92-0.98]; P=0.001; automated: HR, 0.95 [95% CI, 0.92-0.98]; P=0.001). Manual correction of the automated contours did not lead to improved risk prediction (left ventricular EF, area under the curve: 0.67 automated versus 0.68 automated corrected; P=0.49). There was acceptable agreement (left ventricular EF: bias, 2.6%; 95% limits of agreement, -9.1% to 14.2%; intraclass correlation coefficient, 0.88 [95% CI, 0.77-0.93]) of manual and automated volumetric assessments. Conclusions User-independent volumetric analyses performed by fully automated software are feasible, and results are equally predictive of major adverse cardiac events compared with conventional analyses in patients following myocardial infarction. Registration URL:; Unique identifiers: NCT00712101 and NCT01612312.

Schuster Andreas, Lange Torben, Backhaus Sören J, Strohmeyer Carolin, Boom Patricia C, Matz Jonas, Kowallick Johannes T, Lotz Joachim, Steinmetz Michael, Kutty Shelby, Bigalke Boris, Gutberlet Matthias, de Waha-Thiele Suzanne, Desch Steffen, Hasenfuß Gerd, Thiele Holger, Stiermaier Thomas, Eitel Ingo


artificial intelligence, automated postprocessing, cardiac magnetic resonance imaging, deep learning software, risk stratification