Clin Res Cardiol (2022). https://doi.org/10.1007/s00392-022-02002-5 |
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Machine learning to optimize ECG leadwise evaluation for localization of myocardial infarction | ||
E. Stützner1, N. Gumpfer1, S. Wegener2, J. Prim1, D. Grün2, J. Hanning1, M. Guckert1, T. Keller2 | ||
1Kompetenzzentrum für Informationstechnologie, Technische Hochschule Mittelhessen, Friedberg; 2Abteilung für innere Medizin I, Kardiologie, Justus-Liebig-Universität Giessen, Gießen; | ||
Background and Aim: Aim of this project is to combine different machine learning models to develop a diagnostic algorithm for identification and localization of myocardial infarction (MI) in 12-lead ECG data which allows the visualization of the decision process of the algorithm therefore improving trust. Methods and Results:
The two step machine learning approach is able to identify an anterior MI with a median accuracy of 90.8%, a median sensitivity of 83.1%, a median specificity of 94.58 % and a median area under the receiver operator characterist curve (AUROC) of 88.8 %. Here, the model mainly focuses on the anterior leads V2 and V3 (see Figure 1A presenting the model with a tree depth of 3). Regarding differntiation of an inferior MI the model a median accuracy of 87.0 %. a median sensitivity of 77.6 %, a median specificity of 91,9% and a median AUROC of 84.2%. IN the context of inferior MI identification the meta-learner identified the lead AVF as the most relevant one followed by the leads V3, V2,III, V5 and II (see Figure 2B). Conclusion:The step-wise combination of convolutional neural networks with a decision tree using 12-lead ECG data yields competitive results for identifying localized myocardial infarction while also providing an intuitive source of visualization. This could facilitate development of trustworthy models for automated ECG evaluation as part of clinical decision support systems. |
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https://dgk.org/kongress_programme/jt2022/aV982.html |