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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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A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) and deep learning show promise in analyzing chest CT scans for COVID-19 detection. Further research is needed to overcome challenges in cross-population model training and dataset variability for clinical application.

Keywords:
COVID-19chest CTdeep structured learningmedical imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • The COVID-19 pandemic necessitated rapid advancements in diagnostic tools.
  • Artificial intelligence (AI) offers potential benefits in medical imaging analysis for disease detection.
  • Chest CT scans are crucial for diagnosing COVID-19, but interpretation can be challenging.

Purpose of the Study:

  • To review AI-driven methodologies for COVID-19 analysis using chest CT data from 2020-2022.
  • To assess the efficacy of deep learning algorithms in COVID-19 screening and decision-making.
  • To identify challenges and emerging techniques in AI-based COVID-19 detection.

Main Methods:

  • Systematic review of 231 peer-reviewed research articles published between 2020 and 2022.
  • Meta-analysis using specific keywords on PubMed Central and Web of Science.
  • Focus on AI and deep learning applications in chest CT for COVID-19.

Main Results:

  • AI and deep learning algorithms show potential in analyzing chest CT scans for COVID-19.
  • Many AI tools are developed for educational and training purposes.
  • Challenges include dataset size variations and the need for cross-population train/test models.

Conclusions:

  • AI holds promise for improving COVID-19 detection and analysis via chest CT.
  • Further development is required to address limitations for widespread clinical adoption.
  • Standardization of datasets and robust validation are crucial for reliable AI tools.