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Related Concept Videos

Heart Failure Drugs: Inotropic Agents01:26

Heart Failure Drugs: Inotropic Agents

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Positive inotropic agents are commonly used as the first line of treatment for heart failure. One such agent is digoxin, derived from the genus Digitalis, which has been known for centuries but effectively utilized since 1785. However, these cardiac glycosides can have potentially toxic effects due to their mechanism of action, which involves inhibiting Na+/K+-ATPase and increasing contractility. Digoxin is absorbed orally and distributed in various tissues, including the CNS. It has a long...
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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
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Dysrhythmias V: Evaluating Dysrhythmias01:30

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Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
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Pulse rhythm01:30

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Cardiopulmonary Resuscitation III: AED Use01:23

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Introduction to AEDAn Automated External Defibrillator (AED) is a portable medical device that analyzes the heart's rhythm and, if necessary, delivers an electrical shock to help the heart re-establish an effective rhythm during sudden cardiac arrest (SCA). SCA occurs when the heart suddenly and unexpectedly stops beating, leading to a loss of blood flow to the brain and other vital organs. In such emergencies, time is of the essence, and using an AED, combined with Cardiopulmonary...
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Electrocardiogram Fundamentals01:28

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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography.

Da-Wei Chang1, Chin-Sheng Lin1, Tien-Ping Tsao2

  • 1Division of Cardiology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan.

International Journal of Environmental Research and Public Health
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

A new AI system can detect digoxin toxicity using electrocardiograms (ECGs) with high accuracy. This tool offers a rapid and accessible method for diagnosing digoxin toxicity, improving patient care.

Keywords:
artificial intelligencedeep learning algorithmdigoxin toxicityelectrocardiogram

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Digoxin is crucial for heart rate control but has a narrow therapeutic window.
  • Digoxin toxicity is often misdiagnosed or delayed due to low awareness and lengthy lab tests.
  • Electrocardiography (ECG) shows characteristic patterns indicative of digoxin toxicity.

Purpose of the Study:

  • To develop a deep learning model for detecting digoxin toxicity using ECG data.
  • To evaluate the AI system's performance against human interpretation and in a competition setting.

Main Methods:

  • Trained a deep learning algorithm on a large dataset of 177,067 ECGs (61 with digoxin toxicity).
  • Validated the AI system using 20% of the ECG data and conducted a human-machine competition.
  • Evaluated performance using Area Under the Curve (AUC), sensitivity, and specificity.

Main Results:

  • The AI system achieved high diagnostic accuracy, with AUCs of 0.912 (validation) and 0.929 (competition).
  • Achieved 84.6% sensitivity and 94.6% specificity in detecting digoxin toxicity.
  • A single lead ECG analysis (Lead I) showed superior performance (AUC = 0.960) compared to 12 leads.

Conclusions:

  • The developed ECG-based deep learning system offers a highly accurate, economical, and rapid method for digoxin toxicity detection.
  • This AI system shows promise as a decision support tool for clinical diagnosis of digoxin toxicity.
  • The system demonstrated particular efficacy in patients with heart failure and without atrial fibrillation.