A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019

Diagn Interv Radiol. 2020 Sep;26(5):443-448. doi: 10.5152/dir.2019.20294.

Abstract

The results of research on the use of artificial intelligence (AI) for medical imaging of the lungs of patients with coronavirus disease 2019 (COVID-19) has been published in various forms. In this study, we reviewed the AI for diagnostic imaging of COVID-19 pneumonia. PubMed, arXiv, medRxiv, and Google scholar were used to search for AI studies. There were 15 studies of COVID-19 that used AI for medical imaging. Of these, 11 studies used AI for computed tomography (CT) and 4 used AI for chest radiography. Eight studies presented independent test data, 5 used disclosed data, and 4 disclosed the AI source codes. The number of datasets ranged from 106 to 5941, with sensitivities ranging from 0.67-1.00 and specificities ranging from 0.81-1.00 for prediction of COVID-19 pneumonia. Four studies with independent test datasets showed a breakdown of the data ratio and reported prediction of COVID-19 pneumonia with sensitivity, specificity, and area under the curve (AUC). These 4 studies showed very high sensitivity, specificity, and AUC, in the range of 0.9-0.98, 0.91-0.96, and 0.96-0.99, respectively.

Publication types

  • Review

MeSH terms

  • Artificial Intelligence*
  • Betacoronavirus*
  • COVID-19
  • Coronavirus Infections / diagnostic imaging*
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Lung / diagnostic imaging*
  • Pandemics
  • Pneumonia, Viral / diagnostic imaging*
  • Radiography / methods*
  • Reproducibility of Results
  • SARS-CoV-2
  • Sensitivity and Specificity
  • Tomography, X-Ray Computed / methods