The effect of confounding data features on a deep learning algorithm to predict complete coronary occlusion in a retrospective observational setting. Issue 1 (20th February 2021)

• Record Type: Journal Article
• Authors: Brisk, Rob; Bond, Raymond; Finlay, Dewar; McLaughlin, James; Piadlo, Alicja; Leslie, Stephen J; Gossman, David E; Menown, Ian B; McEneaney, D J; Warren, S
• Is Part Of: European heart journal. Volume 2:Issue 1(2021)
• Page Start: 127
• Page End: 134
• Publication Date: 2021-02-20
• Subjects: Deep learning -- Artificial intelligence -- STEMI -- ECG; Medical informatics -- Periodicals; Medical technology -- Periodicals; Cardiovascular system -- Periodicals; 616.100284
• DOI: 10.1093/ehjdh/ztab002
• Languages: English
• ISSNs: 2634-3916

Abstract

Abstract: Aims: Deep learning (DL) has emerged in recent years as an effective technique in automated ECG analysis. Methods and results: A retrospective, observational study was designed to assess the feasibility of detecting induced coronary artery occlusion in human subjects earlier than experienced cardiologists using a DL algorithm. A deep convolutional neural network was trained using data from the STAFF III database. The task was to classify ECG samples as showing acute coronary artery occlusion, or no occlusion. Occluded samples were recorded after 60 s of balloon occlusion of a single coronary artery. For the first iteration of the experiment, non-occluded samples were taken from ECGs recorded in a restroom prior to entering theatres. For the second iteration of the experiment, non-occluded samples were taken in the theatre prior to balloon inflation. Results were obtained using a cross-validation approach. In the first iteration of the experiment, the DL model achieved an F1 score of 0.814, which was higher than any of three reviewing cardiologists or STEMI criteria. In the second iteration of the experiment, the DL model achieved an F1 score of 0.533, which is akin to the performance of a random chance classifier. Conclusion: The dataset was too small for the second model to achieve meaningful performance, despite the use of transfer learning. However, 'data leakage' during the first iteration of the experiment led to falsely high results. This study highlights the risk of DL models leveraging data leaks to produce spurious results. Graphical Abstract: