Automatic diagnosis of the 12-lead ECG using a deep neural network
By Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão, D. Oliveira, P. R. Gomes, Jéssica A. Canazart, Milton P. S. Ferreira, Carl R. Andersson, P. Macfarlane, W. Meira Jr. and 2 more. Nature Communications, 2019
1,027 citations recorded by the source corpus.
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice. The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. In that context, the authors present a Deep Neural Network (DNN) that recognizes different abnormalities in ECG recordings which matches or outperform cardiology and emergency resident medical doctors.
Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/e400bc71c978ea5bfabd0b1f158b77d5a8564d09
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Citations in the corpus
- Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL (2020, methodology): “[25] (resnet1d_wang), and were successfully applied in different large-scale studies [10], [27].”
- Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020 (2020, background): “However, there has been limited success in achieving this goal [20, 16] Over the last decade, the rapid development of machine learning techniques have also included a growing number of 12-lead ECG classifiers [21, 15, 3].”
- Artificial intelligence-enhanced electrocardiography in cardiovascular disease management (2021, methodology): “For instance, using 2 million labelled single-lead ECG traces collected in the Clinical Outcomes in Digital Electrocardiology study, one group used a CNN to identify six types of abnormalities on the 12-lead ECG 9 .”