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Time And Event-Specific Deep Learning For Personalized Risk Assessment After Cardiac Perfusion Imaging
Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features.
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A novel deep learning model can analyze standard cardiac perfusion imaging plus clinical data to predict a patient’s time-specific risk for death, acute coronary syndrome (heart attack), and revascularization, outperforming traditional assessments and potentially helping U.S. clinicians tailor follow-up care more precisely.
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