Identification of patients with dilated phase of hypertrophic cardiomyopathy using a convolutional neural network applied to multiple, dual, and single lead electrocardiograms

• Published in: International journal of cardiology. Heart & vasculature Vol. 46; p. 101211
• Main Authors: Hirota, Naomi, Suzuki, Shinya, Motogi, Jun, Umemoto, Takuya, Nakai, Hiroshi, Matsuzawa, Wataru, Takayanagi, Tsuneo, Hyodo, Akira, Satoh, Keiichi, Arita, Takuto, Yagi, Naoharu, Kishi, Mikio, Semba, Hiroaki, Kano, Hiroto, Matsuno, Shunsuke, Kato, Yuko, Otsuka, Takayuki, Uejima, Tokuhisa, Oikawa, Yuji, Hori, Takayuki, Matsuhama, Minoru, Iida, Mitsuru, Yajima, Junji, Yamashita, Takeshi
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.06.2023; Elsevier
• Subjects: Artificial intelligence; Dilated phase of hypertrophic cardiomyopathy; Electrocardiography; Original Paper; Electrocardiography; Artificial intelligence; Dilated phase of hypertrophic cardiomyopathy
• ISSN: 2352-9067; 2352-9067
• DOI: 10.1016/j.ijcha.2023.101211

•Dilated phase hypertrophic cardiomyopathy (dHCM) was well identified by our convolutional neural network (CNN) algorithm applied to eight-, single-, and double-lead ECGs.•A single V5 lead showed similar performance to eight-lead ECG.•To the best of our knowledge, this is the first report of a CNN applied to ECG to detect dHCM. This study sought to develop an artificial intelligence-derived model to detect the dilated phase of hypertrophic cardiomyopathy (dHCM) on digital electrocardiography (ECG) and to evaluate the performance of the model applied to multiple-lead or single-lead ECG. This is a retrospective analysis using a single-center prospective cohort study (Shinken Database 2010–2017, n = 19,170). After excluding those without a normal P wave on index ECG (n = 1,831) and adding dHCM patients registered before 2009 (n = 39), 17,378 digital ECGs were used. Totally 54 dHCM patients were identified of which 11 diagnosed at baseline, 4 developed during the time course, and 39 registered before 2009. The performance of the convolutional neural network (CNN) model for detecting dHCM was evaluated using eight-lead (I, II, and V1-6), single-lead, and double-lead (I, II) ECGs with the five-fold cross validation method. The area under the curve (AUC) of the CNN model to detect dHCM (n = 54) with eight-lead ECG was 0.929 (standard deviation [SD]: 0.025) and the odds ratio was 38.64 (SD 9.10). Among the single-lead and double-lead ECGs, the AUC was highest with the single lead of V5 (0.953 [SD: 0.038]), with an odds ratio of 58.89 (SD:68.56). Compared with the performance of eight-lead ECG, the most similar performance was achieved with the model with a single V5 lead, suggesting that this single-lead ECG can be an alternative to eight-lead ECG for the screening of dHCM.