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Publication Detail
Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance
  • Publication Type:
    Journal article
  • Authors:
    Xue H, Artico J, Davies RH, Adam R, Shetye A, Augusto JB, Bhuva A, Fröjdh F, Wong TC, Fukui M, Cavalcante JL, Treibel TA, Manisty C, Fontana M, Ugander M, Moon JC, Schelbert EB, Kellman P
  • Publisher:
    Ovid Technologies (Wolters Kluwer Health)
  • Publication date:
    15/02/2022
  • Journal:
    Journal of the American Heart Association
  • Volume:
    11
  • Issue:
    4
  • Article number:
    e023849
  • Medium:
    Print-Electronic
  • Status:
    Published
  • Country:
    England
  • Print ISSN:
    2047-9980
  • Language:
    English
  • Keywords:
    artificial intelligence, cardiac magnetic resonance imaging, global longitudinal shortening, reproducibility, image processing, prognosis
  • Notes:
    © 2022 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Abstract
Background Global longitudinal shortening (GL-Shortening) and the mitral annular plane systolic excursion (MAPSE) are known markers in heart failure patients, but measurement may be subjective and less frequently reported because of the lack of automated analysis. Therefore, a validated, automated artificial intelligence (AI) solution can be of strong clinical interest. Methods and Results The model was implemented on cardiac magnetic resonance scanners with automated in-line processing. Reproducibility was evaluated in a scan-rescan data set (n=160 patients). The prognostic association with adverse events (death or hospitalization for heart failure) was evaluated in a large patient cohort (n=1572) and compared with feature tracking global longitudinal strain measured manually by experts. Automated processing took ≈1.1 seconds for a typical case. On the scan-rescan data set, the model exceeded the precision of human expert (coefficient of variation 7.2% versus 11.1% for GL-Shortening, P=0.0024; 6.5% versus 9.1% for MAPSE, P=0.0124). The minimal detectable change at 90% power was 2.53 percentage points for GL-Shortening and 1.84 mm for MAPSE. AI GL-Shortening correlated well with manual global longitudinal strain (R2=0.85). AI MAPSE had the strongest association with outcomes (χ2, 255; hazard ratio [HR], 2.5 [95% CI, 2.2-2.8]), compared with AI GL-Shortening (χ2, 197; HR, 2.1 [95% CI,1.9-2.4]), manual global longitudinal strain (χ2, 192; HR, 2.1 [95% CI, 1.9-2.3]), and left ventricular ejection fraction (χ2, 147; HR, 1.8 [95% CI, 1.6-1.9]), with P<0.001 for all. Conclusions Automated in-line AI-measured MAPSE and GL-Shortening can deliver immediate and highly reproducible results during cardiac magnetic resonance scanning. These results have strong associations with adverse outcomes that exceed those of global longitudinal strain and left ventricular ejection fraction.
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