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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Related Experiment Video

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Ultrasonic Assessment of Myocardial Microstructure
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Detection of Left Ventricular Systolic Dysfunction Using an Artificial Intelligence-Enabled Chest X-Ray.

Chih-Weim Hsiang1, Chin Lin2, Wen-Cheng Liu3

  • 1Department of Radiology, Tri-Service General Hospital, National Defense Medical Centre, Taipei, Taiwan.

The Canadian Journal of Cardiology
|January 10, 2022
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Summary

A deep-learning model using chest X-rays (CXRs) can identify low left ventricular ejection fraction (LVEF ≤ 35%). This CXR-EF≤35% tool aids in predicting cardiovascular risks and mortality, offering a novel screening approach.

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Left ventricular systolic dysfunction is crucial for cardiovascular disease prognosis.
  • Accurate assessment of left ventricular ejection fraction (LVEF) is essential for patient management.

Purpose of the Study:

  • To develop a deep-learning model (CXR-EF≤35%) to identify LVEF ≤ 35% using chest X-rays (CXRs).
  • To evaluate the model's performance and clinical implications in predicting cardiovascular outcomes.

Main Methods:

  • Utilized 90,547 CXRs with corresponding LVEF data from transthoracic echocardiography.
  • Developed and validated the CXR-EF≤35% model using internal and external cohorts (77,227 and 13,320 CXRs).
  • Assessed long-term risks using Kaplan-Meier analysis and Cox proportional hazards models.

Main Results:

  • Achieved high detection performance with AUCs of 0.888 (internal) and 0.867 (external).
  • Identified patients with normal baseline LVEF but flagged by CXR-EF≤35% had significantly higher risks of developing LVEF ≤ 35% and adverse cardiovascular outcomes.
  • CXR-EF≤35% predicted increased risks of all-cause mortality, cardiovascular mortality, and atrial fibrillation.

Conclusions:

  • CXR-EF≤35% shows promise as a screening tool for early detection of LVEF ≤ 35%.
  • The model independently predicts long-term cardiovascular risks and outcomes.
  • Further prospective studies are warranted to confirm the model's clinical utility.