Key result
AI-driven automated longitudinal strain analysis standardizes LV function assessment in AMI and accurately localizes infarct regions.
Why the study?
Does an AI-driven automated strain framework accurately quantify myocardial deformation and correlate with clinical biomarkers in patients with first-onset ST-elevation AMI?
Cohort (n=192)
Does an AI-driven automated strain framework accurately quantify myocardial deformation and correlate with clinical biomarkers in patients with first-onset ST-elevation AMI?
Effect estimate: r = -0.609
p-value: p=<0.001
AI-driven automated strain analysis provides standardized, reproducible quantification of LV function and infarct localization in AMI, correlating strongly with enzymatic and electrophysiological markers.
Enables standardized LV assessment in AMI; extends automated strain validation to infarct localization.
PURPOSE: Accurate evaluation of left ventricular (LV) dysfunction and infarct localization in acute myocardial infarction (AMI) remains challenging due to subjective variability in conventional echocardiographic analysis. This study validates an artificial intelligence (AI)-driven automated strain framework for standardized quantification of myocardial deformation and its correlation with clinical biomarkers. METHODS: A retrospective cohort of 102 first-onset ST-elevation AMI patients and 90 age-/sex-matched controls underwent 2D speckle-tracking echocardiography. A modified ResNet-18 architecture processed standardized apical views (112 × 112 pixels, 25-frame cycles) through dual-task learning: global/regional longitudinal strain (LPSS) quantification and infarct localization. Training employed a two-phase optimization-myocardial tracking followed by strain regression and infarct classification. Real-time augmentation included speckle noise and cardiac-phase variations. Statistical analyses assessed correlations between strain parameters, LV ejection fraction (LVEF), cardiac troponin T (cTnT), and ST-segment elevation. RESULTS: AI-derived global LPSS strongly correlated with LVEF (r = -0.609; p < 0.001), outperforming the wall motion score index (r = 0.291). Infarct-zone LPSS demonstrated the strongest associations with cTnT (r = 0.671; p < 0.001) and ST elevation (r = 0.321; p = 0.001). Remote myocardium exhibited compensatory hyperkinesis (LPSS = -17.93%). Bland-Altman analysis confirmed reproducibility (intra-observer bias: 0.7% ± 1.2%; interobserver: 1.1% ± 3.1%). CONCLUSIONS: AI-driven strain analysis standardized LV functional assessment in AMI, providing quantitative correlations with enzymatic and electrophysiological injury markers. Its ability to localize infarcts and detect compensatory mechanisms supports clinical decision-making, bridging gaps between echocardiography and advanced imaging.
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Guo et al. (2026) conducted a cohort in acute myocardial infarction (AMI) (n=192). AI-driven automated strain analysis vs. Wall motion score index was evaluated on Correlation between AI-derived global longitudinal strain (LPSS) and LVEF (r = -0.609, p=<0.001). AI-driven automated strain analysis for left ventricular functional assessment strongly correlated with LVEF (r = -0.609; p < 0.001), outperforming the wall motion score index.
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