An explainable variational auto-encoder pipeline achieved similar performance to black box deep neural networks for conventional ECG interpretation (AUROC 0.94 vs. 0.96) and reduced EF detection.
Does a VAE-based pipeline provide improved explainability while maintaining performance compared to 'black box' DNNs for ECG interpretation?
A novel VAE-based pipeline provides explainable ECG interpretation with performance comparable to 'black box' deep neural networks, facilitating clinical implementation of AI.
Absolute Event Rate: 0.94% vs 0.96%
Aims: Deep neural networks (DNNs) perform excellently in interpreting electrocardiograms (ECGs), both for conventional ECG interpretation and for novel applications such as detection of reduced ejection fraction (EF). Despite these promising developments, implementation is hampered by the lack of trustworthy techniques to explain the algorithms to clinicians. Especially, currently employed heatmap-based methods have shown to be inaccurate. Methods and results: We present a novel pipeline consisting of a variational auto-encoder (VAE) to learn the underlying factors of variation of the median beat ECG morphology (the FactorECG), which are subsequently used in common and interpretable prediction models. As the ECG factors can be made explainable by generating and visualizing ECGs on both the model and individual level, the pipeline provides improved explainability over heatmap-based methods. By training on a database with 1.1 million ECGs, the VAE can compress the ECG into 21 generative ECG factors, most of which are associated with physiologically valid underlying processes. Performance of the explainable pipeline was similar to 'black box' DNNs in conventional ECG interpretation area under the receiver operating curve (AUROC) 0.94 vs. 0.96, detection of reduced EF (AUROC 0.90 vs. 0.91), and prediction of 1-year mortality (AUROC 0.76 vs. 0.75). Contrary to the 'black box' DNNs, our pipeline provided explainability on which morphological ECG changes were important for prediction. Results were confirmed in a population-based external validation dataset. Conclusions: Future studies on DNNs for ECGs should employ pipelines that are explainable to facilitate clinical implementation by gaining confidence in artificial intelligence and making it possible to identify biased models.
Leur et al. (Mon,) conducted a other in Electrocardiogram interpretation (n=1,100,000). Variational auto-encoder (VAE) pipeline vs. 'Black box' deep neural networks (DNNs) was evaluated on Conventional ECG interpretation (AUROC). An explainable variational auto-encoder pipeline achieved similar performance to black box deep neural networks for conventional ECG interpretation (AUROC 0.94 vs. 0.96) and reduced EF detection.