Key result
The CardioLearn deep learning model achieved ROC-AUC scores greater than 0.93 across multiple cardiac diseases, including 0.9789 for atrial fibrillation detection using 12-lead ECGs.
Why the study?
Traditional ECG detection models have high misdiagnosis rates, and deep learning alternatives do not provide publicly available services for users lacking training data or computational resources.
A cloud-based deep learning service (CardioLearn) demonstrated high diagnostic accuracy for detecting multiple cardiac arrhythmias from both single-lead and 12-lead ECG recordings.
Electrocardiogram (ECG) is one of the most convenient and non-invasive tools for monitoring peoples' heart condition, which can use for diagnosing a wide range of heart diseases, including Cardiac Arrhythmia, Acute Coronary Syndrome, et al. However, traditional ECG disease detection models show substantial rates of misdiagnosis due to the limitations of the abilities of extracted features. Recent deep learning methods have shown significant advantages, but they do not provide publicly available services for those who have no training data or computational resources. In this paper, we demonstrate our work on building, training, and serving such out-of-the-box cloud deep learning service for cardiac disease detection from ECG named CardioLearn. The analytic ability of any other ECG recording devices can be enhanced by connecting to the Internet and invoke our open API. As a practical example, we also design a portable smart hardware device along with an interactive mobile program, which can collect ECG and detect potential cardiac diseases anytime and anywhere.
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Hong et al. (2020) studied Cardiac diseases (Arrhythmia) (n=6,877). CardioLearn deep learning model was evaluated on ROC-AUC score for Atrial Fibrillation detection (12-lead). The CardioLearn deep learning model achieved ROC-AUC scores greater than 0.93 across multiple cardiac diseases, including 0.9789 for atrial fibrillation detection using 12-lead ECGs.
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