Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

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...

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Bibliographic Details
Main Authors: Bateman, T.M (Author), Berman, D.S (Author), Dey, D. (Author), Di Carli, M. (Author), Dorbala, S. (Author), Einstein, A.J (Author), Fish, M.B (Author), Hauser, M.T (Author), Kaufmann, P.A (Author), Kwieciński, J. (Author), Liang, J.X (Author), Miller, E.J (Author), Miller, R.J.H (Author), Motwani, M. (Author), Pieszko, K. (Author), Ruddy, T.D (Author), Shanbhag, A.D (Author), Sharir, T. (Author), Singh, A. (Author), Sinusas, A.J (Author), Slomka, P.J (Author)
Format: Article
Language:English
Published: Nature Research 2023
Subjects:
Cardiovascular event
Confidence interval
Coronary syndromes
Decision making
Deep learning
Event Types
Event-specific
Forecasting
Learning models
Myocardial perfusion
Perfusion imaging
Risk assessment
Risks assessments
Time interval
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