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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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Related Experiment Video

Updated: Jul 25, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Artificial Intelligence-Based Software with CE Mark for Chest X-ray Interpretation: Opportunities and Challenges.

Salvatore Claudio Fanni1, Alessandro Marcucci1, Federica Volpi1

  • 1Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy.

Diagnostics (Basel, Switzerland)
|June 28, 2023
PubMed
Summary

Artificial intelligence (AI) software enhances chest X-ray (CXR) interpretation, addressing limitations in sensitivity and scope. CE-marked AI tools are available for detecting diseases like pulmonary tuberculosis and lung nodules.

Keywords:
CE-markartificial intelligencechest c-raydeep learninglung nodulespulmonary tuberculosis

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Computer-Aided Detection

Background:

  • Chest X-ray (CXR) is a primary diagnostic tool but has inherent limitations in sensitivity and scope.
  • These limitations necessitate advanced interpretation methods, leading to the development of AI-based software.
  • Artificial intelligence (AI) offers potential solutions to improve CXR analysis accuracy and efficiency.

Purpose of the Study:

  • To identify and categorize CE-marked AI-based software available for chest X-ray interpretation.
  • To assess the applications of AI software in diagnosing various chest conditions from CXR.
  • To understand the current landscape of AI tools for enhancing CXR analysis.

Main Methods:

  • A systematic search of the 'AI for radiology' online database was conducted.
  • The search focused on identifying CE-marked AI software specifically designed for CXR interpretation.
  • Identified studies and software were categorized based on the targeted disease or application.

Main Results:

  • AI-powered computer-aided detection software is increasingly used for pulmonary tuberculosis screening and triage, particularly in resource-limited settings.
  • AI software demonstrates value in lung nodule detection, automated flagging of positive cases, and digital bone suppression for improved image post-processing.
  • The majority of CE-marked AI software packages for CXR are designed to detect multiple findings, with varying sensitivity and specificity for each.

Conclusions:

  • CE-marked AI software is emerging as a valuable adjunct to traditional chest X-ray interpretation.
  • AI applications span from disease screening (e.g., tuberculosis) to nodule detection and image enhancement.
  • Further evaluation of sensitivity and specificity across different AI tools and findings is warranted.