Automated Phenotyping of Mitral Stenosis Using Deep Learning

Background and Aims: Accurate classification of mitral stenosis (MS) remains a significant clinical challenge. This study aimed to develop an artificial intelligence (AI) framework to automatically detect clinically significant MS from echocardiography. Methods: We developed EchoNet-MS, an open-source end-to-end integrated approach combining video based convolutional neural networks to assess MS severity and differentiate rheumatic etiology from echocardiography and validated its performance across four cohorts. Results: EchoNet-MS was trained and validated in total of 431,612 videos from 44,671 studies from three different healthcare system. Combining assessments from multiple echocardiographic videos, the model was trained on a Kaiser Permanente Northern California (KPNC) cohort of 8,677 studies from 7,576 patients with a range of MS severity. The model was validated on a KPNC held-out test cohort (N=1,623) and a temporally distinct cohort (N=19,206), as well as Stanford Healthcare (SHC) cohort (N=3,333) and Cedars-Sinai Medical Center (CSMC) cohort (N=72,909). EchoNet-MS achieved excellent discrimination of severe MS with AUC 0.937 95% CI: 0.913 - 0.958 in the KPNC held-out cohort, 0.994 0.986 - 0.999 in the temporally distinct cohort, 0.991 0.986 - 0.995 in SHC, and 0.973 0.958 - 0.987 in CSMC. The model achieved excellent performance in classifying both rheumatic or non-rheumatic MS with AUC ranging from 0.890 and 0.967. Conclusions: EchoNet-MS accurately assesses MS severity and etiology using information from multiple echocardiographic views. Its strong performance generalizes robustly to external cohorts and shows potential as an automated clinical decision support tool.