TY - JOUR
T1 - Fully automated cardiac assessment for diagnostic and prognostic stratification following myocardial infarction
AU - Schuster, Andreas
AU - Lange, Torben
AU - Backhaus, Sören J.
AU - Strohmeyer, Carolin
AU - Boom, Patricia C.
AU - Matz, Jonas
AU - Kowallick, Johannes T.
AU - Lotz, Joachim
AU - Steinmetz, Michael
AU - Kutty, Shelby
AU - Bigalke, Boris
AU - Gutberlet, Matthias
AU - de Waha-Thiele, Suzanne
AU - Desch, Steffen
AU - Hasenfuß, Gerd
AU - Thiele, Holger
AU - Stiermaier, Thomas
AU - Eitel, Ingo
N1 - Funding Information:
This work was supported by the German Centre for Cardiovascular Research (DZHK).
Publisher Copyright:
© 2020 The Authors.
PY - 2020/9/15
Y1 - 2020/9/15
N2 - 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: https://www.clinicaltrials.gov; Unique identifiers: NCT00712101 and NCT01612312.
AB - 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: https://www.clinicaltrials.gov; Unique identifiers: NCT00712101 and NCT01612312.
KW - Artificial intelligence
KW - Automated postprocessing
KW - Cardiac magnetic resonance imaging
KW - Deep learning software
KW - Risk stratification
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U2 - 10.1161/JAHA.120.016612
DO - 10.1161/JAHA.120.016612
M3 - Article
C2 - 32873121
AN - SCOPUS:85091125388
SN - 2047-9980
VL - 9
SP - 1
EP - 13
JO - Journal of the American Heart Association
JF - Journal of the American Heart Association
IS - 18
M1 - e016612
ER -