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Prediction of Aortic Stenosis Progression Using Artificial Intelligence A Machine Learning Model
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DOI：10.1016/j.jacadv.2025.102121.png)
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Abstract
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BACKGROUND Current guidelines for monitoring aortic stenosis (AS) progression focus on serial echocardiographic assessment, which is resource-intensive and subject to variability. Artificial intelligence may offer an opportunity to enhance the early identification of patients at risk of developing severe AS. OBJECTIVES The objective of this study was to create an echo-based model that can predict whether a patient will deteriorate from mild/moderate AS to severe AS. METHODS We retrospectively analyzed a single-center database of 529,751 echo exams and identified 9,330 echocardiograms of patients initially diagnosed with mild or moderate AS, 56% of which progressed to severe AS within 5 years. We developed a model agnostic to any patient data outside the scope of the echocardiography report, and the reports were obtained from a large database of a tertiary medical center. Performance was assessed for accuracy, area under the curve-receiver operating characteristic, and calibration SHapley Additive exPlanations values provided interpretability for the model's predictions. RESULTS During the follow-up, 1,625 (47%) patients developed severe AS. The model demonstrated strong predictive performance-an area under the curve-receiver operating characteristic of 0.91, an accuracy of 83%, and an Integrated Calibration Index = 0.0576. The model successfully identified patients at high risk of progression, with robust calibration and generalizability confirmed through cross-validation. CONCLUSIONS Our novel, echocardiography-focused artificial intelligence model is a reliable tool for the early identification of patients at risk of progression to severe AS. Pending future, multicenter, prospective validation, such models may facilitate personalized follow-up strategies and timely interventions, ultimately leading to improved patient outcomes and resource utilization. (JACC Adv. 2025;4:102121) (c) 2025 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords:
aortic stenosis
artificial intelligence
echocardiography
machine learning
risk prediction
valve disease progression
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