Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography

Da-Wei Chang, Chin-Sheng Lin, Tien-Ping Tsao, Chia-Cheng Lee, Jiann-Torng Chen, Chien-Sung Tsai, Wei-Shiang Lin and Chin Lin
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Da-Wei Chang: Division of Cardiology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan
Chin-Sheng Lin: Division of Cardiology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan
Tien-Ping Tsao: Division of Cardiology, Heart Centre, Cheng Hsin General Hospital, Taipei 112, Taiwan
Chia-Cheng Lee: Planning and Management Office, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan
Jiann-Torng Chen: Department of Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan
Chien-Sung Tsai: Division of Cardiovascular Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan
Wei-Shiang Lin: Division of Cardiology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan
Chin Lin: School of Public Health, National Defense Medical Center, Taipei 11490, Taiwan

IJERPH, 2021, vol. 18, issue 7, 1-13

Abstract: 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.

Keywords: artificial intelligence; electrocardiogram; deep learning algorithm; digoxin toxicity (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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