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Diagnosing Atrial Septal Defect from Electrocardiogram with Deep Learning

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Abstract

The heart murmur associated with atrial septal defects is often faint and can thus only be detected by chance. Although electrocardiogram examination can prompt diagnoses, identification of specific findings remains a major challenge. We demonstrate improved diagnostic accuracy realized by incorporating a proposed deep learning model, comprising a convolutional neural network (CNN) and long short-term memory (LSTM), with electrocardiograms. This retrospective observational study included 1192 electrocardiograms of 728 participants from January 1, 2000, to December 31, 2017, at Tokyo Women's Medical University Hospital. Using echocardiography, we confirmed the status of healthy subjects—no structural heart disease—and the diagnosis of atrial septal defects in patients. We used a deep learning model comprising a CNN and LTSMs. All pediatric cardiologists (n = 12) were blinded to patient groupings when analyzing them by electrocardiogram. Using electrocardiograms, the model’s diagnostic ability was compared with that of pediatric cardiologists. We assessed 1192 electrocardiograms (828 normally structured hearts and 364 atrial septal defects) pertaining to 792 participants. The deep learning model results revealed that the accuracy, sensitivity, specificity, positive predictive value, and F1 score were 0.89, 0.76, 0.96, 0.88, and 0.81, respectively. The pediatric cardiologists (n = 12) achieved means of accuracy, sensitivity, specificity, positive predictive value, and F1 score of 0.58 ± 0.06, 0.53 ± 0.04, 0.67 ± 0.10, 0.69 ± 0.18, and 0.58 ± 0.06, respectively. The proposed method is a superior alternative to accurately diagnose atrial septal defects.

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Acknowledgments

We would like to express much appreciation to Dr. Nagata, Dr. Hattori, and Dr. Hagiwara for their valuable and constructive suggestions during the planning and development of this work. We would also like to thank Editage for English language editing.

Funding

This work was supported in part by the 4th Miyata Foundation Award, for which we thank Mr. Miyata.

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Authors and Affiliations

  1. Department of Pediatric Cardiology, Tokyo Women’s Medical University Hospital, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-0054, Japan

    Hiroki Mori, Kei Inai & Hisashi Sugiyama

  2. Institute of Advanced BioMedical Engineering and Science, Tokyo Women’s Medical University, Tokyo, 162-0054, Japan

    Hiroki Mori & Yoshihiro Muragaki

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  1. Hiroki Mori
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  2. Kei Inai
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  4. Yoshihiro Muragaki
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Contributions

HM conceived of the presented idea. HM developed the theory and performed the computations. HS and KI verified the analytical methods. YM encouraged HM to investigate and supervised the findings of this work.

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Correspondence to Yoshihiro Muragaki.

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Mori, H., Inai, K., Sugiyama, H. et al. Diagnosing Atrial Septal Defect from Electrocardiogram with Deep Learning. Pediatr Cardiol 42, 1379–1387 (2021). https://doi.org/10.1007/s00246-021-02622-0

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