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Related Concept Videos

X-ray Imaging01:24

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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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X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
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COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning.

Isah Salim Ahmad1, Na Li2, Tangsheng Wang1

  • 1Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Bioengineering (Basel, Switzerland)
|November 25, 2023
PubMed
Summary

A new deep neural network, ULTRA-X-COVID, accurately detects Coronavirus disease 2019 (COVID-19) using ultra-low-dose X-ray images. This AI model offers a safe and rapid alternative for COVID-19 diagnosis with minimal radiation exposure.

Keywords:
COVID-19chest X-ray imagesdeep learningultra-low-dose

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Disease Diagnostics

Background:

  • Conventional X-ray imaging for COVID-19 diagnosis involves significant radiation exposure, limiting repeat examinations.
  • Ultra-low-dose X-ray technology offers a safer alternative for rapid and accurate disease detection.
  • Artificial intelligence (AI) is increasingly utilized for medical image analysis and disease diagnosis.

Purpose of the Study:

  • To introduce and evaluate ULTRA-X-COVID, a deep neural network for automated COVID-19 detection using ultra-low-dose X-ray images.
  • To assess the performance of the ULTRA-X-COVID model on a large, multinational, and multicenter dataset.
  • To compare the diagnostic capabilities of ultra-low-dose X-ray imaging with AI against conventional X-ray methods.

Main Methods:

  • A retrospective cohort study (ULTRA-X-COVID) was conducted using 30,882 ultra-low-dose X-ray images from ~16,600 patients across 51 countries.
  • A deep neural network (ULTRA-X-COVID) was developed for automatic COVID-19 detection, with distinct training and testing datasets.
  • Model performance was evaluated using metrics including AUC, accuracy, specificity, and F1 score.

Main Results:

  • The ULTRA-X-COVID model achieved an AUC of 0.968, accuracy of 94.3%, specificity of 88.9%, and an F1 score of 99.0% on the test set.
  • The model demonstrated performance comparable to conventional X-ray doses.
  • The prediction time per image was rapid, averaging only 0.1 seconds.

Conclusions:

  • The ULTRA-X-COVID model effectively identifies COVID-19 infections from ultra-low-dose X-ray scans.
  • This AI-powered approach provides a novel, safe, and efficient alternative for COVID-19 detection.
  • The ULTRA-X-COVID model shows potential for adaptation to diagnose other diseases using medical imaging.