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September 29, 2025Open Access

Comprehensive aortic stenosis characterization using multi-view deep learning

Hirotaka Ieki Yuki Sahashi Miloš Vukadinovic Meenal Rawlani Christina Binder Neal Yuan Andrew P. Ambrosy Alan S. Go Wansu Chen Ming‐Sum Lee Bryan He Paul Cheng David Ouyang

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Key Points

EchoNet-AS achieved an AUC of 0.964 in a held-out cohort, demonstrating excellent severe aortic stenosis discrimination.
The model integrates B-mode videos and Doppler measurements, outpacing traditional single-view methods.
Validated across multiple cohorts, EchoNet-AS also showed robust performance in external validation with AUCs up to 0.989.
This comprehensive approach supports automated clinical decision-making by synthesizing structural and functional echocardiographic data.

Implication

Deep learning model improves aortic stenosis classification in clinical settings, indicating promising AI integration.

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Cite This Study

Ieki et al. (2025) studied this question.

synapsesocial.com/papers/68dc1e3b8a7d58c25ebb1d27 https://doi.org/10.1101/2025.09.26.25336778

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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