Fully Automated Cardiac Assessment for Diagnostic and Prognostic Stratification Following Myocardial Infarction

Abstract

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.

Keywords: artificial intelligence; automated postprocessing; cardiac magnetic resonance imaging; deep learning software; risk stratification.

Figure 1. Automated, automated corrected, and manual biventricular volumetric analyses.

Overview of a tracked short‐axis stack from base to apex of 1 patient with and without MACE each using different analysis software types. MACE indicates major adverse cardiac events.

Figure 2. Automated, automated corrected, and manual quantification of enhancement detection.

Overview of IS detection in short‐axis orientation from base to apex of 1 patient with and without MACE. IS indicates infarct size; MACE indicates major adverse cardiac events.

Figure 3. Study flow chart.

AIDA STEMI indicates Abciximab i.v. versus i.c. in ST‐elevation Myocardial Infarction; CMR, cardiac magnetic resonance; FU, follow‐up; MACE indicates major adverse cardiac events; NSTEMI, non–ST‐segment–elevation myocardial infarction; STEMI, ST‐segment–elevation myocardial infarction; and TATORT NSTEMI, Thrombus Aspiration in Thrombus Containing Culprit Lesions in Non‐ST‐Elevation Myocardial Infarction.

Figure 4. Bland‐Altmann plots for agreement of manual and automated biventricular volumes.

Agreement of ventricular parameters derived by automatic and manual analyses. Bland‐Altman plots (manual–automatic) are shown. EDV, end‐diastolic volume; EF, ejection fraction; ESV, end‐systolic volume; LV, left ventricular; and RV, right ventricular.

Figure 5. Kaplan–Meier plots according to LVEF.

Kaplan–Meier curves for manual and automated LVEF analyses presenting the time to MACE in patients dichotomized by clinically relevant LVEF 35% in manual and automated groups, respectively. LVEF indicates left ventricular ejection fraction; and MACE, major adverse cardiac events.