Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance

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