ムラガキ　ヨシヒロ   Muragaki Yoshihiro   村垣　善浩    所属   医学部 医学科（東京女子医科大学病院）    職種   客員教授
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	Diagnosing Atrial Septal Defect from Electrocardiogram with Deep Learning.
掲載誌名	正式名：Pediatric cardiology 略　 称：Pediatr Cardiol ISSNコード：14321971/01720643
掲載区分	国外
巻・号・頁	42(6),pp.1379-1387
著者・共著者	MORI Hiroki†, INAI Kei, SUGIYAMA Hisashi, MURAGAKI Yoshihiro
担当区分	最終著者,責任著者
発行年月	2021/08
概要	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