Work

Prediction of Aortic Stenosis Progression Using Artificial Intelligence

Year: 2025

Type: article

Abstract: 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.

Source: JACC Advances

DOI: 10.1016/j.jacadv.2025.102121