A Web-Deployed Computer Vision Pipeline for Automated Determination of Cardiac Structure and Function and Detection of Disease by Two-Dimensional Echocardiography

by   Jeffrey Zhang, et al.

Automated cardiac image interpretation has the potential to transform clinical practice in multiple ways including enabling low-cost serial assessment of cardiac function in the primary care and rural setting. We hypothesized that advances in computer vision could enable building a fully automated, scalable analysis pipeline for echocardiogram (echo) interpretation. Our approach entailed: 1) preprocessing; 2) convolutional neural networks (CNN) for view identification, image segmentation, and phasing of the cardiac cycle; 3) quantification of chamber volumes and left ventricular mass; 4) particle tracking to compute longitudinal strain; and 5) targeted disease detection. CNNs accurately identified views (e.g. 99 individual cardiac chambers. Cardiac structure measurements agreed with study report values (e.g. mean absolute deviations (MAD) of 7.7 mL/kg/m2 for left ventricular diastolic volume index, 2918 studies). We computed automated ejection fraction and longitudinal strain measurements (within 2 cohorts), which agreed with commercial software-derived values [for ejection fraction, MAD=5.3 demonstrated applicability to serial monitoring of breast cancer patients for trastuzumab cardiotoxicity. Overall, we found that, compared to manual measurements, automated measurements had superior performance across seven internal consistency metrics with an average increase in the Spearman correlation coefficient of 0.05 (p=0.02). Finally, we developed disease detection algorithms for hypertrophic cardiomyopathy and cardiac amyloidosis, with C-statistics of 0.90 and 0.84, respectively. Our pipeline lays the groundwork for using automated interpretation to support point-of-care handheld cardiac ultrasound and large-scale analysis of the millions of echos archived within healthcare systems.


page 3

page 4

page 5


Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery

The electrocardiogram or ECG has been in use for over 100 years and rema...

AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography

Left ventricular (LV) function is an important factor in terms of patien...

High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning

Left ventricular hypertrophy (LVH) results from chronic remodeling cause...

CathAI: Fully Automated Interpretation of Coronary Angiograms Using Neural Networks

Coronary heart disease (CHD) is the leading cause of adult death in the ...

Automated Left Ventricle Dimension Measurement in 2D Cardiac Ultrasound via an Anatomically Meaningful CNN Approach

Two-dimensional echocardiography (2DE) measurements of left ventricle (L...