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Related Concept Videos

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

103
Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
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Electrocardiogram01:29

Electrocardiogram

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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.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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Acute Coronary Syndrome III: Diagnostic studies01:30

Acute Coronary Syndrome III: Diagnostic studies

3
Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
3
Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

3
Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
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Related Experiment Video

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Using Extraordinary Optical Transmission to Quantify Cardiac Biomarkers in Human Serum
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Predicting troponin biomarker elevation from electrocardiograms using a deep neural network.

Lukas Hilgendorf1,2, Petur Petursson3,4, Vibha Gupta3,2

  • 1Department of Molecular and Clinical Medicine, University of Gothenburg, Goteborg, Sweden lukas.hilgendorf@gu.se.

Open Heart
|October 30, 2024
PubMed
Summary

A deep learning model using electrocardiograms (ECGs) can predict elevated troponin levels in chest pain patients. This tool offers high negative predictive accuracy, aiding rapid triage in emergency settings.

Keywords:
Acute Coronary SyndromeChest PainCoronary Artery Disease

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Elevated troponin levels indicate cardiac injury.
  • Electrocardiograms (ECGs) are readily available in emergency settings.
  • Predicting troponin elevation from ECGs can expedite patient care.

Purpose of the Study:

  • To develop and evaluate a deep learning model for predicting troponin elevation.
  • To assess the utility of ECGs in conjunction with artificial intelligence for cardiac diagnostics.
  • To provide a time-saving tool for emergency room decision-making.

Main Methods:

  • A residual convolutional neural network (ResNet) was trained on 15,856 ECGs from patients with chest pain or dyspnea.
  • Data included high-sensitivity troponin test results (troponin I and troponin T) within 6 hours of ECG acquisition.
  • The model was trained and validated using multiple data splits to ensure robustness.

Main Results:

  • The ResNet model achieved an average Area Under the Curve (AUC) of 0.7717.
  • The model demonstrated an accuracy of 71.43% and an F1 score of 0.5642.
  • A high negative predictive value (NPV) of 0.8660 was observed, indicating reliable exclusion of elevated troponin.

Conclusions:

  • The developed neural network shows clinically meaningful performance in predicting troponin elevation.
  • The model's high negative predictive accuracy makes it a valuable tool for ruling out cardiac injury.
  • This AI-driven approach can serve as a valuable option for first-line triage of patients with chest pain or dyspnea.