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Imaging Studies for Cardiovascular System III: X-Ray01:20

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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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An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Related Experiment Video

Updated: Dec 6, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis.

Andrew A Borkowski1, Narayan A Viswanadhan1, L Brannon Thomas1

  • 1is Chief of the Molecular Diagnostics Laboratory, is Chief of the Microbiology Laboratory, is a Research Coordinator, and is Chief of Pathology; is Assistant Chief of Radiology; all at the James A. Haley Veterans' Hospital in Tampa, Florida. is a Cofounder of InterKnowlogy, LLC in Carlsbad, California. Andrew Borkowski and Stephen Mastorides are Professors and L. Brannon Thomas is an Assistant Professor, all in the Department of Pathology and Cell Biology, University of South Florida, Morsani College of Medicine in Tampa, Florida.

Federal Practitioner : for the Health Care Professionals of the VA, Dod, and PHS
|October 8, 2020
PubMed
Summary

Artificial intelligence (AI) combined with chest X-rays (CXRs) shows high accuracy in diagnosing COVID-19 pneumonia. This AI-powered diagnostic tool offers a promising solution for rapid and reliable COVID-19 detection.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Coronavirus disease-19 (COVID-19) rapidly became a pandemic, causing significant morbidity and mortality.
  • Developing reliable diagnostic methods for COVID-19 was initially challenging.
  • Artificial intelligence (AI) and machine learning offer advanced computational capabilities for healthcare applications.

Purpose of the Study:

  • To explore the use of AI with chest X-rays (CXRs) for reliable COVID-19 diagnosis.
  • To train and validate an AI model using CXR images for detecting COVID-19 pneumonia.

Main Methods:

  • Utilized Microsoft CustomVision, an automated image classification and object detection system.
  • Trained the AI model on a dataset of publicly available CXR images, including those with COVID-19 pneumonia, other pneumonias, and normal lungs.
  • Validated the model using institutional CXRs of patients with confirmed COVID-19, non-COVID-19 pneumonia, and normal CXRs.

Main Results:

  • The trained model achieved 92.9% overall sensitivity and positive predictive value.
  • Specific performance metrics included 94.8% sensitivity and 98.9% positive predictive value for COVID-19 pneumonia.
  • Validation using institutional data demonstrated 100% sensitivity, 95% specificity, 97% accuracy, 91% positive predictive value, and 100% negative predictive value.

Conclusions:

  • AI shows significant potential in assisting the diagnosis of COVID-19 pneumonia using CXR images.
  • Findings support AI's role in screening, triage, initial diagnosis, and monitoring disease progression.
  • A website was developed to share this AI technology for combating future health threats like COVID-19.