Evaluation and development of Artificial Intelligence tools to assess COVID-19 Severe Acute4 Respiratory Syndrome from chest imaging

• Author(s) / Creator(s)

  • Lekhasree Narayanagari

• Chest CT is being more widely used as a diagnostic test for COVID-19 Severe Acute
  Respiratory Syndrome-related lung disease. Artificial intelligence (AI) has the ability
  to assist in the rapid assessment of CT scans for COVID-19 Severe Acute Respiratory
  Syndrome findings from other clinical entities. Here that a group of deep learning al-
  gorithms trained on a diverse multinational cohort of 1280 patients to localize parietal
  lung parenchyma followed by classification of COVID-19 Severe Acute Respiratory
  Syndrome can achieve up to 90.8 percent accuracy, with 84 percent sensitivity and 93
  percent specificity, as measured in an independent test set (not included in training
  and validation) can achieve up to 90.8 percent accuracy, with 84 percent sensitivity

  and 93 percent specificity. Chest CTs from oncology, emergency, and pneumonia-
  related indications were used as normal controls. In 140 patients with laboratory
  reported other (non COVID-19) pneumonia, the false positive rate was 10 percent.
  In a variety of patient populations, AI-based algorithms can quickly differentiate CT
  scans with COVID-19 induced pneumonia, as well as distinguish non-COVID related
  Severe Acute Respiratory Syndrome from chest imaging with high specificity.

• Date created

  2021-09-01

• Subjects / Keywords

  • Artificial intelligence
  • COVID-19
  • Severe Acute Respiratory Syndrome
  • SARS
  • Chest Imaging

• Type of Item

  Research Material

• DOI

  https://doi.org/10.7939/r3-fwfs-e726

• License

  Attribution-NonCommercial 4.0 International