P1926Artificial intelligence in echocardiography for standard clinical metrics

Abstract

Abstract Introduction A full transthoracic echocardiogram (TTE) study usually takes 40 - 60 mins to perform and report. Our aim was to validate an artificial intelligence (AI) which automatically calculates measurements with manual standard clinical metrics. Methods 41 patients with heart failure (HF) and 19 controls were enrolled retrospectively. A shortened 5-minute TTE exam was performed. Studies were exported from the hospital database in a DICOM format and fed to an AI pipeline to classify, segment and analyse each image. A convolutional neural network (CNN) was used to label each view into one of 23 classes. Views of interest (Apical 2-, 4-Chamber and Parasternal Short/Long Axis) were individually segmented using a segmentation CNN. View classification was trained on 4,000 labelled studies, segmentation models were trained for each view with 72 manually segmented images for PSAX, 128 for PLAX, 168 for A4C and 198 for A2C. The area-length formula was used to calculate left-ventricular volumes (LVEDV/LVESV), ejection fraction (AI-LVEF). Indexed LA volume (LAVOLI) LV mass (LVMI) were also compared. 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. Compute time using was between 4 to 7 mins for classification, segmentation and analysis using a single Graphics Processing Unit (GPU). 11 (18%) non-physiological AI-ESV and associated AI-LVEF were excluded vs 2 (3%) M-LVEF (×2 7 95% CI 3 to 27%, p=0.008). AI generated 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. Mean absolute error of M-LVEF vs AI-LVEF was 7.4±6.6%. 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 to 0.15, p=0.19). AI vs Manual, Correlation Matrix and ROC Conclusion AI with minimal human input is approaching the accuracy required for clinical utility. AI has the ability to distinguish LV systolic dysfunction, and chamber volumes which could be applied to handheld ultrasound in real-time. Acknowledgement/Funding Health Research Council of New Zealand, Auckland Bioengineering Institute