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Abstract: TH-OR069

ECG12Net: A Deep Learning Algorithm Capable of Suprahuman Detection of Hypokalemia and Hyperkalemia by Electrocardiography

Session Information

Category: Fluid and Electrolytes

  • 902 Fluid and Electrolytes: Clinical


  • Lin, Shih-Hua P., Tri-Service General Hospital, Neihu, Taiwan
  • Sung, Chih-Chien, Tri-Service General Hospital, Neihu, Taiwan
  • Lin, Chien-Ming, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
  • Hsu, Yu-Juei, Division of Nephrology, Tri-Service General Hospital, Taipei, Taiwan

Detection of dyskalemias (hypo- and hyperkalemia), important causes of sudden cardiac death, currently depends on laboratory tests. Since cardiac tissue is very sensitive to dyskalemias, electrocardiography (ECG) may be able to uncover clinically important dyskalemias before laboratory results. Our study aimed to develop a deep learning model, ECG12Net, to detect dyskalemias based on ECG presentation and to evaluate the logic and performance of this model.


Between May 2011 and December 2016, 66,321 ECG records with corresponding serum potassium (K+) concentrations were obtained from 40,180 patients admitted to the emergency department. ECG12Net is an 82-layer convolutional neural network, which estimates serum K+ concentration. Six clinicians (three emergency physicians and three cardiologists) participated a human-machine competition. We used sensitivity and specificity as evaluation measures to compare the performance of ECG12Net with these physicians.


In a human-machine competition including 300 ECGs of different serum K+ concentrations, the area under curve in detecting hypo- and hyperkalemia by ECG12Net was 0.926 and 0.958, respectively, which was significantly better than that of our best clinicians. Moreover, the sensitivities and specificities of detecting hypokalemia and hyperkalemia were 96.7% and 83.3%, and 93.3% and 97.8%, respectively. In the test set including 13,222 ECGs, ECG12Net had the same performance with sensitivities for severe hypokalemia/hyperkalemia achieving 95.6% and 84.5%, respectively with the mean absolute error of 0.531. The specificities of detecting hypokalemia and hyperkalemia were 81.6% and 96.0%, respectively.


A deep learning model based on 12-lead ECG may help physicians to promptly recognize severe dyskalemias and thereby reduce cardiac events.