Before revision:  Here we show that a large dataset of 723,754 clinically-acquired echocardiographic videos ( approx. 45 million images) linked to longitudinal follow-up data in 27,028 patients can be used to train a deep neural network to predict 1-year survival with good accuracy . We also demonstrate that prediction accuracy can be further improved by adding highly predictive clinical variables from the electronic health record .
After revision:  Here we show that a large dataset of 723,754 clinically-acquired echocardiographic videos ( approx. 45 million images) linked to longitudinal follow-up data in 27,028 patients can be used to train a deep neural network to predict 1-year survival with good accuracy (area under the curve (AUC) in an independent test set = 0.839). Prediction accuracy was further improved by adding highly predictive clinical variables from the electronic health record .