Predicting future clinical events , such as death, is an important task in medicine that helps physicians guide appropriate action. Neural networks have particular promise to assist with medical prediction tasks related to clinical imaging by learning patterns from large datasets. Significant advances have been made in predicting complex diagnoses from medical imaging 1-5%DIFDELCMD < ]%%% . Predicting future events, then, is a natural but relatively unexplored extension of those efforts. Moreover, neural networks have not yet been applied to medical videos on a large scale, such as ultrasound of the heart (echocardiography). 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 . Finally, in a blinded, independent test set, the trained neural network was more accurate in discriminating 1-year survival outcomes than two expert cardiologists. These results therefore highlight the potential of neural networks to add new predictive power to clinical image interpretations .
Predicting future clinical events helps physicians guide appropriate intervention. Machine learning has tremendous promise to assist %DIFDELCMD < ]%%% physicians with predictions based on the discovery of complex patterns from historical data, such as large, longitudinal electronic health records (EHR). This study is a first attempt to demonstrate such capabilities using raw echocardiographic videos of the heart . We show that a large dataset of 723,754 clinically-acquired echocardiographic videos ( ~ 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 mortality with good accuracy (area under the curve (AUC) in an independent test set = 0.839). Prediction accuracy was further improved by adding EHR data (AUC = 0.858) . Finally, we demonstrate that the trained neural network was more accurate in mortality prediction than two expert cardiologists. These results highlight the potential of neural networks to add new power to clinical predictions .
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