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Article

Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography

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Division of Cardiology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan
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Division of Cardiology, Heart Centre, Cheng Hsin General Hospital, Taipei 112, Taiwan
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Planning and Management Office, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan
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Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan
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Department of Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan
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Division of Cardiovascular Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan
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School of Public Health, National Defense Medical Center, Taipei 11490, Taiwan
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School of Medicine, National Defense Medical Center, Taipei 11490, Taiwan
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Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 11490, Taiwan
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Author to whom correspondence should be addressed.
Academic Editor: Paul B. Tchounwou
Int. J. Environ. Res. Public Health 2021, 18(7), 3839; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18073839
Received: 25 February 2021 / Revised: 24 March 2021 / Accepted: 1 April 2021 / Published: 6 April 2021
Although digoxin is important in heart rate control, the utilization of digoxin is declining due to its narrow therapeutic window. Misdiagnosis or delayed diagnosis of digoxin toxicity is common due to the lack of awareness and the time-consuming laboratory work that is involved. Electrocardiography (ECG) may be able to detect potential digoxin toxicity based on characteristic presentations. Our study attempted to develop a deep learning model to detect digoxin toxicity based on ECG manifestations. This study included 61 ECGs from patients with digoxin toxicity and 177,066 ECGs from patients in the emergency room from November 2011 to February 2019. The deep learning algorithm was trained using approximately 80% of ECGs. The other 20% of ECGs were used to validate the performance of the Artificial Intelligence (AI) system and to conduct a human-machine competition. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the performance of ECG interpretation between humans and our deep learning system. The AUCs of our deep learning system for identifying digoxin toxicity were 0.912 and 0.929 in the validation cohort and the human-machine competition, respectively, which reached 84.6% of sensitivity and 94.6% of specificity. Interestingly, the deep learning system using only lead I (AUC = 0.960) was not worse than using complete 12 leads (0.912). Stratified analysis showed that our deep learning system was more applicable to patients with heart failure (HF) and without atrial fibrillation (AF) than those without HF and with AF. Our ECG-based deep learning system provides a high-accuracy, economical, rapid, and accessible way to detect digoxin toxicity, which can be applied as a promising decision supportive system for diagnosing digoxin toxicity in clinical practice. View Full-Text
Keywords: artificial intelligence; electrocardiogram; deep learning algorithm; digoxin toxicity artificial intelligence; electrocardiogram; deep learning algorithm; digoxin toxicity
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MDPI and ACS Style

Chang, D.-W.; Lin, C.-S.; Tsao, T.-P.; Lee, C.-C.; Chen, J.-T.; Tsai, C.-S.; Lin, W.-S.; Lin, C. Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography. Int. J. Environ. Res. Public Health 2021, 18, 3839. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18073839

AMA Style

Chang D-W, Lin C-S, Tsao T-P, Lee C-C, Chen J-T, Tsai C-S, Lin W-S, Lin C. Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography. International Journal of Environmental Research and Public Health. 2021; 18(7):3839. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18073839

Chicago/Turabian Style

Chang, Da-Wei, Chin-Sheng Lin, Tien-Ping Tsao, Chia-Cheng Lee, Jiann-Torng Chen, Chien-Sung Tsai, Wei-Shiang Lin, and Chin Lin. 2021. "Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography" International Journal of Environmental Research and Public Health 18, no. 7: 3839. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18073839

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