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Overview of Flail Chest
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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
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How far have we come? Artificial intelligence for chest radiograph interpretation.

K Kallianos1, J Mongan1, S Antani2

  • 1Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.

Clinical Radiology
|February 2, 2019
PubMed
Summary

Artificial intelligence (AI) shows promise for automating chest radiograph interpretation. This review covers AI progress, datasets, algorithms, and limitations in medical imaging analysis.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiography

Background:

  • Renewed interest in artificial intelligence (AI) for medical imaging interpretation.
  • Chest radiographs are crucial for public health and widely used globally.
  • Interpreting chest X-rays is complex and requires extensive training.

Purpose of the Study:

  • To introduce artificial intelligence concepts relevant to medical imaging.
  • To review the current state of AI in chest radiograph interpretation.
  • To discuss available datasets, algorithms, and limitations.

Main Methods:

  • Literature review of AI applications in chest radiography.
  • Analysis of existing datasets and machine learning algorithms.
  • Discussion of challenges and future directions in AI for imaging.

Main Results:

  • AI has made significant progress in chest radiograph interpretation.
  • Various datasets and algorithms are available for research.
  • Limitations in current AI approaches need to be addressed.

Conclusions:

  • AI holds potential to enhance chest radiograph interpretation.
  • Further research is needed to overcome current limitations.
  • AI tools could support radiologists in clinical practice.