MURAGAKI Yoshihiro
   Department   School of Medicine(Tokyo Women's Medical University Hospital), School of Medicine
   Position   Visiting Professor
Article types Original article
Language English
Peer review Peer reviewed
Title Diagnosing Atrial Septal Defect from Electrocardiogram with Deep Learning.
Journal Formal name:Pediatric cardiology
Abbreviation:Pediatr Cardiol
ISSN code:14321971/01720643
Domestic / ForeginForegin
Volume, Issue, Page 42(6),pp.1379-1387
Author and coauthor MORI Hiroki†, INAI Kei, SUGIYAMA Hisashi, MURAGAKI Yoshihiro
Authorship Last author,Corresponding author
Publication date 2021/08
Summary 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.
DOI 10.1007/s00246-021-02622-0
PMID 33907875