Abstract 4366948: Multi-View Deep Learning for Automated Quantification of Mitral Stenosis

Background: Accurate assessment of mitral stenosis (MS) severity is critical to guide timely clinical management. Current evaluation relies on expert interpretation of B-mode and Doppler echocardiography, requiring integration of multiple views and skilled Doppler imaging. This study aimed to develop and validate a deep learning model for automated MS severity assessment using multi-view B-mode and color Doppler echocardiographic videos. Methods: We developed a two-stage framework for automated MS assessment. First, four video-based convolutional neural networks were trained to classify MS severity from distinct echocardiographic views: B-mode [parasternal long-axis (PLAX), apical three-chamber and five-chamber (AP)] and color Doppler [PLAX-color, and AP-color]. Next, outputs from the four models were integrated using a machine learning ensemble (HistGradientBoostingClassifier) to produce a study-level MS severity classification. Performance was assessed using area under the receiver operating characteristic curve (AUC) on held-out test set from Kaiser Permanente (KP) and Stanford Health Care (SHC). Results: The models were trained on 66,714 videos from 3,921 studies at KP, and evaluated on internal held-out test data (KP; 7,344 videos, 438 studies) and external test data (SHC; 29,181 videos, 1,988 studies). The multi-view ensemble model demonstrated strong performance, achieving a macro-AUC of 0.853 (95% CI: 0.828–0.877; Figure 1) on the KP test dataset. Generalizability was confirmed on the external SHC cohort with an AUC of 0.974 (95% CI: 0.968–0.981; Figure 2). Conclusion: This study confirmed the ability for multi-view deep learning models to assess MS severity. The model demonstrated accurate, generalizable performance and highlights the potential of AI-powered decision support tools in echocardiographic evaluation of MS.