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Automated cardiac image interpretation has the potential to transformclinical practice in multiple ways including enabling low-cost serialassessment of cardiac function in the primary care and rural setting. Wehypothesized that advances in computer vision could enable building a fullyautomated, 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) particletracking to compute longitudinal strain; and 5) targeted disease detection.CNNs accurately identified views (e.g. 99% for apical 4-chamber) and segmentedindividual cardiac chambers. Cardiac structure measurements agreed with studyreport values (e.g. mean absolute deviations (MAD) of 7.7 mL/kg/m2 for leftventricular diastolic volume index, 2918 studies). We computed automatedejection fraction and longitudinal strain measurements (within 2 cohorts),which agreed with commercial software-derived values [for ejection fraction,MAD=5.3%, N=3101 studies; for strain, MAD=1.5% (n=197) and 1.6% (n=110)], anddemonstrated applicability to serial monitoring of breast cancer patients fortrastuzumab cardiotoxicity. Overall, we found that, compared to manualmeasurements, automated measurements had superior performance across seveninternal consistency metrics with an average increase in the Spearmancorrelation coefficient of 0.05 (p=0.02). Finally, we developed diseasedetection algorithms for hypertrophic cardiomyopathy and cardiac amyloidosis,with C-statistics of 0.93 and 0.84, respectively. Our pipeline lays thegroundwork for using automated interpretation to support point-of-care handheldcardiac ultrasound and large-scale analysis of the millions of echos archivedwithin healthcare systems.

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