Joon-Myoung Kwon1,2, Ki-Hyun Jeon2,3, Hyue Mee Kim3, Min Jeong Kim3, Sung Min Lim3, Kyung-Hee Kim3, Pil Sang Song3, Jinsik Park3, Rak Kyeong Choi3, Byung-Hee Oh3. 1. Department of Emergency Medicine, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, Republic of Korea. 2. Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, 28, Hohyeon-ro 489beon-gil, Bucheon-si, Gyeonggi-do, Republic of Korea. 3. Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, 21080 Incheon, Republic of Korea.
Abstract
AIMS: Although left ventricular hypertrophy (LVH) has a high incidence and clinical importance, the conventional diagnosis criteria for detecting LVH using electrocardiography (ECG) has not been satisfied. We aimed to develop an artificial intelligence (AI) algorithm for detecting LVH. METHODS AND RESULTS: This retrospective cohort study involved the review of 21 286 patients who were admitted to two hospitals between October 2016 and July 2018 and underwent 12-lead ECG and echocardiography within 4 weeks. The patients in one hospital were divided into a derivation and internal validation dataset, while the patients in the other hospital were included in only an external validation dataset. An AI algorithm based on an ensemble neural network (ENN) combining convolutional and deep neural network was developed using the derivation dataset. And we visualized the ECG area that the AI algorithm used to make the decision. The area under the receiver operating characteristic curve of the AI algorithm based on ENN was 0.880 (95% confidence interval 0.877-0.883) and 0.868 (0.865-0.871) during the internal and external validations. These results significantly outperformed the cardiologist's clinical assessment with Romhilt-Estes point system and Cornell voltage criteria, Sokolov-Lyon criteria, and interpretation of ECG machine. At the same specificity, the AI algorithm based on ENN achieved 159.9%, 177.7%, and 143.8% higher sensitivities than those of the cardiologist's assessment, Sokolov-Lyon criteria, and interpretation of ECG machine. CONCLUSION: An AI algorithm based on ENN was highly able to detect LVH and outperformed cardiologists, conventional methods, and other machine learning techniques. Published on behalf of the European Society of Cardiology. All rights reserved.
AIMS: Although left ventricular hypertrophy (LVH) has a high incidence and clinical importance, the conventional diagnosis criteria for detecting LVH using electrocardiography (ECG) has not been satisfied. We aimed to develop an artificial intelligence (AI) algorithm for detecting LVH. METHODS AND RESULTS: This retrospective cohort study involved the review of 21 286 patients who were admitted to two hospitals between October 2016 and July 2018 and underwent 12-lead ECG and echocardiography within 4 weeks. The patients in one hospital were divided into a derivation and internal validation dataset, while the patients in the other hospital were included in only an external validation dataset. An AI algorithm based on an ensemble neural network (ENN) combining convolutional and deep neural network was developed using the derivation dataset. And we visualized the ECG area that the AI algorithm used to make the decision. The area under the receiver operating characteristic curve of the AI algorithm based on ENN was 0.880 (95% confidence interval 0.877-0.883) and 0.868 (0.865-0.871) during the internal and external validations. These results significantly outperformed the cardiologist's clinical assessment with Romhilt-Estes point system and Cornell voltage criteria, Sokolov-Lyon criteria, and interpretation of ECG machine. At the same specificity, the AI algorithm based on ENN achieved 159.9%, 177.7%, and 143.8% higher sensitivities than those of the cardiologist's assessment, Sokolov-Lyon criteria, and interpretation of ECG machine. CONCLUSION: An AI algorithm based on ENN was highly able to detect LVH and outperformed cardiologists, conventional methods, and other machine learning techniques. Published on behalf of the European Society of Cardiology. All rights reserved.
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