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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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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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Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays.

Nikos Tsiknakis1, Eleftherios Trivizakis1,2, Evangelia E Vassalou3,4

  • 1Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece.

Experimental and Therapeutic Medicine
|August 4, 2020
PubMed
Summary

Artificial intelligence (AI) and chest X-rays offer a promising solution for diagnosing COVID-19 when testing kits are scarce. This interpretable AI framework accurately identifies infected patients using transfer learning, achieving perfect classification accuracy.

Keywords:
COVID-19chest X-raysinterpretable artificial intelligencetransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Disease Diagnostics

Background:

  • The COVID-19 pandemic created a global healthcare crisis, highlighting the need for rapid and accurate diagnostic tools.
  • Scarcity of traditional testing kits necessitates alternative methods for diagnosing COVID-19.
  • Chest radiological imaging presents a viable option for disease detection, especially when combined with advanced analytical techniques.

Purpose of the Study:

  • To develop and evaluate an interpretable artificial intelligence (AI) framework for diagnosing COVID-19 using chest X-ray images.
  • To address the challenge of limited datasets in medical AI by employing transfer learning techniques.
  • To assess the diagnostic relevance of AI-identified image regions through expert radiologist evaluation.

Main Methods:

  • Utilized transfer learning techniques to train AI models on limited datasets of COVID-19 patient X-rays.
  • Developed an interpretable AI framework that generates attention maps to highlight diagnostically relevant image areas.
  • Validated the AI framework's performance using a 5-fold cross-validation approach for binary classification.

Main Results:

  • The proposed transfer learning methodology achieved an area under the curve (AUC) of 1 for binary classification.
  • The interpretable AI framework demonstrated the ability to focus attention on diagnostically relevant regions of chest X-rays.
  • Expert radiologists confirmed the clinical relevance of the AI's attention maps.

Conclusions:

  • The developed interpretable AI framework shows high accuracy in diagnosing COVID-19 from chest X-rays.
  • Transfer learning is an effective strategy for building diagnostic AI models with limited medical data.
  • This AI approach offers a valuable tool to aid clinicians in COVID-19 diagnosis, especially during resource limitations.