Artificial intelligence in echocardiography diagnostics – detection of takotsubo syndrome. (25th November 2020)

• Record Type: Journal Article
• Authors: Di Vece, D; Laumer, F; Schwyzer, M; Burkholz, R; Corinzia, L; Cammann, V.L; Citro, R; Bax, J; Ghadri, J.R; Buhmann, J.M; Templin, C
• Is Part Of: European heart journal. Volume 41:(2020)Supplement 2
• Publication Date: 2020-11-25
• Subjects: Acute Heart Failure: Imaging; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/ehjci/ehaa946.1221
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background: Machine learning allows classifying diseases based only on raw echocardiographic imaging data and is therefore a landmark in the development of computer-assisted decision support systems in echocardiography. Purpose: The present study sought to determine the value of deep (machine) learning systems for automatic discrimination of takotsubo syndrome and acute myocardial infarction. Methods: Apical 2- and 4-chamber echocardiographic views of 110 patients with takotsubo syndrome and 110 patients with acute myocardial infarction were used in the development, training and validation of a deep learning approach, i.e. a convolutional autoencoder (CAE) for feature extraction followed by classical machine learning models for classification of the diseases. Results: The deep learning model achieved an area under the receiver operating curve (AUC) of 0.801 with an overall accuracy of 74.5% for 5-fold cross validation evaluated on a clinically relevant dataset. In comparison, experienced cardiologists achieved AUCs in the range 0.678–0.740 and an average accuracy of 64.5% on the same dataset. Conclusions: A real-time system for fully automated interpretation of echocardiographic videos was established and trained to differentiate takotsubo syndrome from acute myocardial infarction. The framework provides insight into the algorithms' decision process for physicians and yields new and valuable information on the manifestation of disease patterns in echocardiographic data. While our system was superior to cardiologists in echocardiography-based disease classification, further studies should be conducted in a larger patient population to prove its clinical application. Funding Acknowledgement: Type of funding source: None