Published on in Vol 8, No 3 (2020): March

A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm Development

A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm Development

A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm Development

Journals

  1. Pilia N, Severi S, Raimann J, Genovesi S, Dössel O, Kotanko P, Corsi C, Loewe A. Quantification and classification of potassium and calcium disorders with the electrocardiogram: What do clinical studies, modeling, and reconstruction tell us?. APL Bioengineering 2020;4(4) View
  2. Somani S, Russak A, Richter F, Zhao S, Vaid A, Chaudhry F, De Freitas J, Naik N, Miotto R, Nadkarni G, Narula J, Argulian E, Glicksberg B. Deep learning and the electrocardiogram: review of the current state-of-the-art. EP Europace 2021;23(8):1179 View
  3. Palmieri F, Gomis P, Ferreira D, Ruiz J, Bergasa B, Martín-Yebra A, Bukhari H, Pueyo E, Martínez J, Ramírez J, Laguna P. Monitoring blood potassium concentration in hemodialysis patients by quantifying T-wave morphology dynamics. Scientific Reports 2021;11(1) View
  4. Chang D, Lin C, Tsao T, Lee C, Chen J, Tsai C, Lin W, Lin C. Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography. International Journal of Environmental Research and Public Health 2021;18(7):3839 View
  5. Lin Z, Wong L, Cheung B. Diuretic-induced hypokalaemia: an updated review. Postgraduate Medical Journal 2022;98(1160):477 View
  6. Kwon J, Jung M, Kim K, Jo Y, Shin J, Cho Y, Lee Y, Ban J, Jeon K, Lee S, Park J, Oh B. Artificial intelligence for detecting electrolyte imbalance using electrocardiography. Annals of Noninvasive Electrocardiology 2021;26(3) View
  7. Lin C, Lin C, Lee D, Lee C, Chen S, Tsai S, Kuo F, Chau T, Lin S. Artificial Intelligence–Assisted Electrocardiography for Early Diagnosis of Thyrotoxic Periodic Paralysis. Journal of the Endocrine Society 2021;5(9) View
  8. Lin C, Lee Y, Fang W, Lou Y, Kuo F, Lee C, Lin C. Deep Learning Algorithm for Management of Diabetes Mellitus via Electrocardiogram-Based Glycated Hemoglobin (ECG-HbA1c): A Retrospective Cohort Study. Journal of Personalized Medicine 2021;11(8):725 View
  9. Nedadur R, Wang B, Yanagawa B. The cardiac surgeon's guide to artificial intelligence. Current Opinion in Cardiology 2021;36(5):637 View
  10. Chang C, Lin C, Luo Y, Lee Y, Lin C. Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders. Frontiers in Cardiovascular Medicine 2022;9 View
  11. Hafer C. Kalium in der Intensivmedizin. Aktuelle Ernährungsmedizin 2022;47(01):35 View
  12. Urtnasan E, Lee J, Moon B, Lee H, Lee K, Youk H. Noninvasive Screening Tool for Hyperkalemia Using a Single-Lead Electrocardiogram and Deep Learning: Development and Usability Study. JMIR Medical Informatics 2022;10(6):e34724 View
  13. Lou Y, Lin C, Fang W, Lee C, Lin C. Extensive deep learning model to enhance electrocardiogram application via latent cardiovascular feature extraction from identity identification. Computer Methods and Programs in Biomedicine 2023;231:107359 View
  14. Bukhari H, Sánchez C, Ruiz J, Potse M, Laguna P, Pueyo E. Monitoring of Serum Potassium and Calcium Levels in End-Stage Renal Disease Patients by ECG Depolarization Morphology Analysis. Sensors 2022;22(8):2951 View
  15. Lin C, Chau T, Lin C, Shang H, Fang W, Lee D, Lee C, Tsai S, Wang C, Lin S. Point-of-care artificial intelligence-enabled ECG for dyskalemia: a retrospective cohort analysis for accuracy and outcome prediction. npj Digital Medicine 2022;5(1) View
  16. Lou Y, Lin C, Fang W, Lee C, Wang C, Lin C. Development and validation of a dynamic deep learning algorithm using electrocardiogram to predict dyskalaemias in patients with multiple visits. European Heart Journal - Digital Health 2023;4(1):22 View
  17. Lin C, Chen C, Chau T, Lin C, Tsai S, Lee D, Lee C, Shang H, Lin S. Artificial intelligence-enabled electrocardiography identifies severe dyscalcemias and has prognostic value. Clinica Chimica Acta 2022;536:126 View
  18. Liu Y, Lin C, Cheng C, Lin C. A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram. Journal of Personalized Medicine 2022;12(7):1150 View
  19. Lee Y, Lin C, Fang W, Lee C, Ho C, Wang C, Tsai D, Lin C. Artificial Intelligence-Enabled Electrocardiography Detects Hypoalbuminemia and Identifies the Mechanism of Hepatorenal and Cardiovascular Events. Frontiers in Cardiovascular Medicine 2022;9 View
  20. Zheng Z, Soomro Q, Charytan D. Deep Learning Using Electrocardiograms in Patients on Maintenance Dialysis. Advances in Kidney Disease and Health 2023;30(1):61 View
  21. Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers J, Katsaggelos A, Maglaveras N. State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review. JMIR Medical Informatics 2022;10(8):e38454 View
  22. Chiu I, Cheng J, Chen T, Wang Y, Cheng C, Kung C, Cheng F, Yau F, Lin C. Using Deep Transfer Learning to Detect Hyperkalemia From Ambulatory Electrocardiogram Monitors in Intensive Care Units: Personalized Medicine Approach. Journal of Medical Internet Research 2022;24(12):e41163 View
  23. Liu W, Lin C, Tsao T, Lee C, Cheng C, Chen J, Tsai C, Lin W, Lin C. A Deep-Learning Algorithm-Enhanced System Integrating Electrocardiograms and Chest X-rays for Diagnosing Aortic Dissection. Canadian Journal of Cardiology 2022;38(2):160 View
  24. Chen H, Lin C, Fang W, Lee C, Ho C, Wang C, Lin C. Artificial Intelligence-Enabled Electrocardiogram Predicted Left Ventricle Diameter as an Independent Risk Factor of Long-Term Cardiovascular Outcome in Patients With Normal Ejection Fraction. Frontiers in Medicine 2022;9 View
  25. Chen H, Lin C, Fang W, Lou Y, Cheng C, Lee C, Lin C. Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis. Journal of Personalized Medicine 2022;12(3):455 View
  26. Hafer C. Kalium – was Intensivmediziner wissen sollten. Intensivmedizin up2date 2021;17(03):291 View
  27. Bukhari H, Sánchez C, Srinivasan S, Palmieri F, Potse M, Laguna P, Pueyo E. Estimation of potassium levels in hemodialysis patients by T wave nonlinear dynamics and morphology markers. Computers in Biology and Medicine 2022;143:105304 View
  28. Lee C, Lin C, Tsai C, Tsao T, Cheng C, Liou J, Lin W, Lee C, Chen J, Lin C. A deep learning-based system capable of detecting pneumothorax via electrocardiogram. European Journal of Trauma and Emergency Surgery 2022;48(4):3317 View
  29. Terker A, Ellison D. Pathophysiology, Evaluation, and Treatment of Hyperkalemia. Nephrology Self-Assessment Program 2022;20(2):117 View
  30. Koulaouzidis G, Jadczyk T, Iakovidis D, Koulaouzidis A, Bisnaire M, Charisopoulou D. Artificial Intelligence in Cardiology—A Narrative Review of Current Status. Journal of Clinical Medicine 2022;11(13):3910 View
  31. Lee C, Liu W, Lou Y, Lin C, Fang W, Lee C, Ho C, Wang C, Lin C. Artificial intelligence-enabled electrocardiogram screens low left ventricular ejection fraction with a degree of confidence. DIGITAL HEALTH 2022;8:205520762211432 View
  32. Liu W, Lin C, Tsai C, Tsao T, Cheng C, Liou J, Lin W, Cheng S, Lou Y, Lee C, Lin C. A deep learning algorithm for detecting acute myocardial infarction. EuroIntervention 2021;17(9):765 View
  33. Tsai D, Tsai S, Chiang H, Lee C, Chen S. Development and Validation of an Artificial Intelligence Electrocardiogram Recommendation System in the Emergency Department. Journal of Personalized Medicine 2022;12(5):700 View
  34. Lou Y, Lin C, Fang W, Lee C, Ho C, Wang C, Lin C. Artificial Intelligence-Enabled Electrocardiogram Estimates Left Atrium Enlargement as a Predictor of Future Cardiovascular Disease. Journal of Personalized Medicine 2022;12(2):315 View
  35. Lin Z, Cheng Y, Cheung B. Machine learning algorithms identify hypokalaemia risk in people with hypertension in the United States National Health and Nutrition Examination Survey 1999–2018. Annals of Medicine 2023;55(1) View
  36. Jeon E, Kim A, Lee J, Heo H, Lee H, Woo K. Developing a Classification Algorithm for Prediabetes Risk Detection From Home Care Nursing Notes. CIN: Computers, Informatics, Nursing 2023;41(7):539 View
  37. Chang H, Chiang J, Tsai C, Chiu P. Predicting hyperkalemia in patients with advanced chronic kidney disease using the XGBoost model. BMC Nephrology 2023;24(1) View
  38. Raileanu G, de Jong J. Electrocardiogram Interpretation Using Artificial Intelligence: Diagnosis of Cardiac and Extracardiac Pathologic Conditions. How Far Has Machine Learning Reached?. Current Problems in Cardiology 2024;49(1):102097 View
  39. Joy S, Kumar K, Palanivelan M, Lakshmi D. Review on Advent of Artificial Intelligence in Electrocardiogram for the Detection of Extra-Cardiac and Cardiovascular Disease. IEEE Canadian Journal of Electrical and Computer Engineering 2023;46(2):99 View
  40. Regolisti G, Rossi G, Genovesi S. Can we trust ECG for diagnosing hyperkalemia? A challenging question for clinicians and bioengineers. International Journal of Cardiology 2023;393:131380 View
  41. Liu P, Lin C, Lin C, Fang W, Lee C, Wang C, Tsai D. Artificial Intelligence-Enabled Electrocardiography Detects B-Type Natriuretic Peptide and N-Terminal Pro-Brain Natriuretic Peptide. Diagnostics 2023;13(17):2723 View
  42. Chen Y, Lin C, Lin C, Tsai D, Fang W, Lee C, Wang C, Chen S. An AI-Enabled Dynamic Risk Stratification for Emergency Department Patients with ECG and CXR Integration. Journal of Medical Systems 2023;47(1) View
  43. Kim D, Jeong J, Kim J, Cho Y, Park I, Lee S, Oh Y, Baek S, Kang D, Lee E, Jeong B. Hyperkalemia Detection in Emergency Departments Using Initial ECGs: A Smartphone AI ECG Analyzer vs. Board-Certified Physicians. Journal of Korean Medical Science 2023;38(45) View
  44. Lu A, Chen C, Lin C, Wu T, Lin S. Artificial Intelligence Electrocardiography Detecting Thyrotoxic Periodic Paralysis Following a SARS-CoV-2 Infection. The American Journal of Medicine 2024;137(5):e91 View
  45. Haverkamp W, Strodthoff N. Durch künstliche Intelligenz verstärkte Elektrokardiographie. Herzschrittmachertherapie + Elektrophysiologie 2024;35(2):104 View
  46. Di Costanzo A, Spaccarotella C, Esposito G, Indolfi C. An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review. Journal of Clinical Medicine 2024;13(4):1033 View
  47. Avetisyan A, Tigranyan S, Asatryan A, Mashkova O, Skorik S, Ananev V, Markin Y. Deep neural networks generalization and fine-tuning for 12-lead ECG classification. Biomedical Signal Processing and Control 2024;93:106160 View
  48. Baek Y. The emergence and clinical significance of artificial intelligence–enhanced electrocardiography. Cardiovascular Prevention and Pharmacotherapy 2024;6(2):41 View
  49. Ose B, Sattar Z, Gupta A, Toquica C, Harvey C, Noheria A. Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review. Current Cardiology Reports 2024;26(6):561 View
  50. Wong C, Lau Y, Lui H, Chan W, San W, Zhou M, Cheng Y, Huang D, Lai W, Lau Y, Siu C. Automatic detection of cardiac conditions from photos of electrocardiogram captured by smartphones. Heart 2024;110(17):1074 View
  51. von Bachmann P, Gedon D, Gustafsson F, Ribeiro A, Lampa E, Gustafsson S, Sundström J, Schön T. Evaluating regression and probabilistic methods for ECG-based electrolyte prediction. Scientific Reports 2024;14(1) View
  52. Samandari Z, Molaeezadeh S. Noninvasive estimation of blood potassium concentration using ECG and FCM-ANFIS model. Research on Biomedical Engineering 2024;40(3-4):647 View
  53. Xi Y, Liu Z, Ni J, Miao Y, Chen Z. Non-invasive serum potassium concentration measurement in children with CKD by quantifying T-wave morphology dynamics. EngMedicine 2024;1(2):100019 View
  54. Liu P, Hsing S, Tsai D, Lin C, Lin C, Wang C, Fang W. A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension. Journal of Imaging Informatics in Medicine 2024 View
  55. Alahdab F, Saad M, Ahmed A, Al Tashi Q, Aminu M, Han Y, Moody J, Murthy V, Wu J, Al-Mallah M. Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients’ electrocardiograms. Cell Reports Medicine 2024;5(10):101746 View
  56. An J, Park M, Joo S, Chang M, Kim D, Shin D, Na Y, Kim J, Lee H, Song Y, Lee Y, Kim S. Development of deep learning algorithm for detecting dyskalemia based on electrocardiogram. Scientific Reports 2024;14(1) View
  57. Chiu I, Wu P, Zhang H, Hughes J, Rogers A, Jalilian L, Perez M, Lin C, Lee C, Zou J, Ouyang D. Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms. JACC: Clinical Electrophysiology 2024 View
  58. Babur S, Moghaddamnia S, Bozkurt M. Estimation of Blood Calcium and Potassium Values from ECG Records. Measurement Science Review 2024;24(5):158 View
  59. Kim J, Jung J, Kim J, Cho Y, Lee E, Son D. Non-Inferiority Analysis of Electrocardiography Analysis Application vs. Point-of-Care Ultrasound for Screening Left Ventricular Dysfunction. Yonsei Medical Journal 2025;66 View
  60. Liu K, Peng S, Tsao Y, Liu C, Chen Z, Han Han Z, Chen W, Hsieh P, Li Y, Hsu Y, Hsu S. A Cross-Modal Autoencoder for Contactless Electrocardiography Monitoring Using Frequency-Modulated Continuous Wave Radar. IEEE Sensors Journal 2024;24(24):41462 View

Books/Policy Documents

  1. Kashou A, Adedinsewo D, Siontis K, Noseworthy P. Comprehensive Physiology. View
  2. Chugh A, Jain C. Artificial Intelligence-based Healthcare Systems. View
  3. Xue Z, Chen K, Li X, Liu T, Xie J, Guo S, Song W, Chu H, Fu G, Zhang N, Zhou B, Tang M, Wang B, Xie B, Mu G, Wang P. AI Augmented ECG Technology. View