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    Popescu et al. developed a deep learning approach that blends neural networks and survival analysis to predict patient-specific survival curves from raw contrast-enhanced cardiac magnetic resonance images and clinical covariates for patients with ischemic heart disease to offer accurate arrhythmic sudden death predictions.

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    • An integral part of our personalized risk prediction in https://t.co/3FHPmpE9sk @NatureCVR is deep learning on raw cardiac MRI images. Images are currently underutilized in #SuddenCardiacDeath risk assessment, as we discuss #EPpeeps #WhyCMR @JHUBME @JohnsHopkins @HopkinsEngineer https://t.co/SlwLYKlnPe

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    Sudden cardiac death from arrhythmia is a major cause of mortality worldwide. In this study, we developed a novel deep learning (DL) approach that blends neural networks and survival analysis to predict patient-specific survival curves from contrast-enhanced cardiac magnetic resonance images and clinical covariates for patients with ischemic heart disease. The DL-predicted survival curves offer…

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    • NBC DC discusses our latest #deeplearning paper for predicting risk of #SuddenCardiacDeath: https://t.co/aUzw0xs6jn in @NatureCVR. Love the production! @trayanovalab @julieshade2 @ChangxinLai @KathyWuMD @aroniskn @HopkinsEngineer @JHUBME @JohnsHopkins https://t.co/dMvdt4sy5M

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    WASHINGTON — A higher coronary artery calcium burden may predict incident sudden cardiac death independent of other atherosclerotic CVD risk factors, particularly for primary prevention patients, researchers reported.Despite significant advancements in the management of subclinical CHD in primary prevention patients, the prediction of sudden cardiac death remains “elusive,” Omar

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    • ⬆️ coronary artery calcium burden can predict #suddencardiacdeath independent of additional #atherosclerotic #CVD risks. #JACCINT https://t.co/q7PzLGpeWo @CardiologyToday