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.

Impact of fully automated assessment on interstudy reproducibility of biventricular volumes and function in cardiac magnetic resonance imaging

“…Recently, automated analyses demonstrated feasibility and equally predictive prognostic value in 1017 patients following acute myocardial infarction compared to conventional analyses by trained and experienced medical personal 14 . Several previous studies applying the proposed automated algorithm showed consistently high interobserver reproducibility with experienced CMR observers 13 , 14 . The feasibility and reliability of automated LV analyses in clinical routine imaging is further underlined by the present data demonstrating high interstudy reproducibility.…”

“…Future applications may expand to automated tissue characterisation e.g. scar quantification 14 as well as deformation imaging 24 . Deformation imaging has gained recognition for enhanced risk prediction beyond conventional volumetric derived functional analyses, e.g.…”

“…Despite efforts directed towards automation of volume and mass assessments, most approaches require manual preparation and preselection of CMR images 11 , 12 . More recently, novel artificial intelligence (AI)-based deep learning algorithms were introduced which allow for fully automated post-processing of LV mass and biventricular volumes showing promising initial results including risk stratification following acute myocardial infarction 13 , 14 . Data on interstudy reproducibility is of high clinical importance when it comes to follow-up surveys.…”

Functional and prognostic implications of cardiac magnetic resonance feature tracking-derived remote myocardial strain analyses in patients following acute myocardial infarction

“…Especially patients having reduced cardiac performance and being classified to high-risk groups according to commonly used parameters can be assessed precisely and efficiently. Furthermore, since fully automatic strain and function quantification with dedicated post-processing software is feasible, these time-saving and highly reproducible methods might additionally facilitate the implementation of RM strain assessment in clinical routine [25][26][27][28].…”

Quantification of Myocardial Deformation Applying CMR-Feature-Tracking—All About the Left Ventricle?

“…First software for automated post-processing of CMR images and subsequent deformation analyses have been introduced and utilized successfully [132,133]. As a result, post-processing times can be reduced, higher work efficiency might lead to significant cost reduction and results are less prone to observer variability [134]. Importantly, AI techniques are able to simultaneously evaluate deformation, cardiac volumes and ejection dynamics and may provide post-processing parallel to finishing the CMR protocol [135,136].…”