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Related Concept Videos

Electrocardiogram01:29

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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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Instrumentation Amplifier01:25

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Aortic valve regurgitation (AR) occurs when the aortic valve fails to close properly, allowing blood to flow backward from the aorta into the left ventricle. This backflow can result in two distinct clinical presentations: acute and chronic AR, each characterized by its own set of symptoms and physical findings.Acute Aortic RegurgitationAcute AR presents with a sudden onset of severe symptoms. Patients typically experience profound dyspnea (shortness of breath), chest pain, and signs of left...
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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
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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...
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Related Experiment Video

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A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
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Spectrum bias in algorithms derived by artificial intelligence: a case study in detecting aortic stenosis using

Andrew S Tseng1, Michal Shelly-Cohen1, Itzhak Z Attia1

  • 1Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA.

European Heart Journal. Digital Health
|January 30, 2023
PubMed
Summary

Spectrum bias in artificial intelligence (AI) algorithms can overestimate diagnostic test performance. This study demonstrates that AI models trained on limited datasets for severe aortic stenosis (AS) show reduced accuracy when applied to broader patient populations.

Keywords:
Aortic stenosisArtificial intelligenceElectrocardiogramSpectrum bias

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Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Diagnostic Accuracy

Background:

  • Spectrum bias occurs when diagnostic tests are developed in populations that differ from the target population, impacting generalizability.
  • Artificial intelligence (AI)-derived algorithms are increasingly used for medical diagnoses, necessitating evaluation of their performance across diverse patient groups.

Purpose of the Study:

  • To experimentally assess the impact of spectrum bias on the performance of an AI-derived algorithm for detecting severe aortic stenosis (AS).
  • To compare the performance of AI models trained on different patient cohort spectra.

Main Methods:

  • Developed two AI models using distinct patient cohorts from Mayo Clinic data (1989-2019): a whole-spectrum cohort (severe AS vs. any non-severe AS) and an extreme-spectrum cohort (severe AS vs. no AS).
  • Assessed model performance using area under the receiver operator curve (AUC), sensitivity, and specificity.
  • Evaluated the performance of the extreme-spectrum model when applied to the whole-spectrum cohort.

Main Results:

  • The extreme-spectrum model (AUC: 0.91) outperformed the whole-spectrum model (AUC: 0.87).
  • Extreme-spectrum model: sensitivity 84%, specificity 84%. Whole-spectrum model: sensitivity 80%, specificity 81%.
  • Applying the extreme-spectrum model to the whole-spectrum cohort resulted in decreased performance (sensitivity 83%, specificity 73%, AUC 0.86).

Conclusions:

  • Training AI algorithms on narrowly defined datasets can lead to an overestimation of their performance in real-world, broader clinical settings.
  • While the AI algorithm showed robustness, spectrum bias can subtly affect diagnostic accuracy for severe AS detection using electrocardiogram (ECG) data.
  • Clinicians must be aware of potential spectrum bias in AI-derived diagnostic tools to ensure appropriate interpretation and application.