Abstract 13659: Automated Detection of Aortic Stenosis From Single-View 2-Dimensional Echocardiography Using a Semi-Supervised, Contrastive Learning Approach

Abstract

Introduction: Early diagnosis of aortic stenosis (AS) is critical for its timely management. However, despite emergence of hand-held echocardiography, the detection and grading of AS requires Doppler imaging, which is limited by both access and expertise. We developed a semi-supervised, contrastive learning approach to identify severe AS using limited labelled data of parasternal long axis (PLAX) videos from transthoracic echocardiography (TTE). Methods: We sampled TTE studies performed between 2015-2021 in a large health system. TTEs from 2015-2020 were used for training, with oversampling of AS for diagnostic enrichment (5311 studies, age 70±16 years, n=2601 [49%] women, 5029 unique patients). The testing set represented studies in 2021 without oversampling for AS (2040 studies, mean age 66±16 years, n=997 [49%] women, n=2034 unique patients). We performed self-supervised pretraining by selecting different PLAX videos from the same patient as positive samples for contrastive learning (multi-instance self-supervised learning) ( A ). The learned weights were used to initialize a 3D convolutional neural network to predict severe AS ( B ). Results: An ensemble model of three different weight initialization methods achieved an AUC of 0.97 (95% CI: 0.96-0.99) for severe AS detection, with 0.96 (95% CI: 0.83-0.97) specificity at 90% sensitivity. Among patients without severe AS, positive predictions were characterized by significantly higher peak aortic velocities compared to negative predictions, with no differences in LV function - a negative control ( C ). Saliency maps highlighted the aortic valve as most relevant to the final predictions ( D, i-v: positive; vi: negative predictions). Conclusions: We have developed a novel method to detect severe AS using single-view TTE videos without requiring Doppler data. Our findings have significant implications for point-of-care ultrasound screening as part of routine clinic visits and in low-resource settings.