Artificial Intelligence in Echocardiography for Standard Clinical Metrics

Abstract

Background: A transthoracic echocardiogram (TTE) usually takes 40–60 minutes to perform and report. This study validated an artificial intelligence (AI) that automatically calculates measurements with manual standard clinical metrics. Method: Forty-one patients with heart failure (HF) and 19 controls were retrospectively enrolled. A shortened 5-minute TTE exam was performed. Studies were fed to an AI pipeline to classify, segment, and analyse. A convolutional neural network (CNN) was used to label each view. Views of interest (Apical 2-, 4-Chamber and PLAX or PSAX) were segmented using a segmentation CNN. The area-length formula was used to calculate left ventricular volumes (LVEDV and LVESV) and ejection fraction (AI-LVEF). Indexed left atrial volume (LAVOLI) and LV mass (LVMI) were also compared. The LVEDV, LVESV, LVEF, and LVMI were averaged over multiple videos. Results: Mean manual LVEF (M-LVEF) in HF patients was 39 ± 10% vs 57 ± 5% in controls. Eleven (18%) non-physiological AI-ESV and associated AI-LVEF were excluded vs two (3%) M-LVEF (χ2, 7; 95% CI, 3%-27%; p = 0.008). The AI measurements correlated well with manual measures (LVEDV r = 0.77; LVESV r = 0.8; LVEF r = 0.71; LAVOLI r = 0.71; LVMI r = 0.6; p < 0.005). The AI-LVEF, M-LVEF, and other HF biomarkers had a similar discrimination for HF (AUC M-LVEF 0.93 vs AI-LVEF 0.88; 95% CI, 0.03–0.15; p = 0.19). Conclusion: Artificial intelligence with minimal human input is approaching the accuracy required for clinical utility. It has the ability to distinguish LV systolic dysfunction, and chamber volumes, which could be applied to handheld ultrasound in real-time.