COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring

Maayan Frid-Adar*, Rula Amer, Ophir Gozes, Jannette Nassar, Hayit Greenspan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This work estimates the severity of pneumonia in COVID-19 patients and reports the findings of a longitudinal study of disease progression. It presents a deep learning model for simultaneous detection and localization of pneumonia in chest Xray (CXR) images, which is shown to generalize to COVID-19 pneumonia. The localization maps are utilized to calculate a 'Pneumonia Ratio' which indicates disease severity. The assessment of disease severity serves to build a temporal disease extent profile for hospitalized patients. To validate the model's applicability to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression profiles that were generated from the DRRs to those that were generated from CT volumes.

Original languageEnglish
Article number9387536
Pages (from-to)1892-1903
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Issue number6
StatePublished - Jun 2021


  • COVID-19
  • DRR
  • detection
  • localization
  • patient monitoring
  • pneumonia
  • severity scoring


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