Abstract 14356: Machine Learning for Detection of Presence and Severity of Aortic Stenosis From B-mode Ultrasound Images: Results of a Blinded Clinical Trial

Abstract

Introduction: Aortic stenosis (AS) is common, with >= moderate severity ~10% above age 75. Despite new therapies, many with AS go undiagnosed due to lack of echo availability and untreated due to improper interpretation. To expand AS diagnosis, we developed an artificial intelligence (AI) algorithm to characterize AS from routine B-mode echo exams without Doppler (AutoAS). Methods: 80,000 de-identified clinical echoes were assembled spanning none, mild, moderate, and severe AS. About 30,000 of these were used to train a convolutional neural network (CNN) using labels derived from a cluster analysis of maximal jet velocity, mean AS gradient, and aortic valve area (AVA). Once labeled, we extracted all available B-mode clips from parasternal long-axis, short axis at aortic level, and apical 5-chamber views, irrespective of image quality. A spatiotemporal CNN was trained to return a probability distribution for none/mild/moderate/severe AS. To assess performance of the model, a hold-out test set of echoes uniformly distributed over patient sex and AS severity was used. Three Level 3 echo trained cardiologists blindly read the complete Doppler echoes and agreed to call AS severity by AVA (< 1 cm2, severe; 1.0-1.5 cm2, moderate; > 1.5 cm2 but restricted, mild; no restriction, none). Results were also dichotomized into none/mild and moderate/severe AS. Results: 40 cases comprised this initial test set. Consistency among the readers was outstanding, with κ = 0.95. The table shows the confusion matrix between AutoAS and panel reads, with excellent accuracy (κ = 0.90). Dichotomous results were also outstanding with sensitivity to detect moderate/severe AS of 91% and specificity of 94%. Conclusions: AI can identify AS from 3 B-mode views and characterize AS severity with accuracy highly congruent to an expert panel using full Doppler data. This may allow AS to be detected in low-resource and screening settings. Further assessment with a larger hold-out test set is planned.