COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring
This work addresses the problem of monitoring COVID-19 disease progression in patients using automated analysis of chest X-rays, which is incremental as it applies existing deep learning methods to a new medical context.
The researchers developed a deep learning model to detect and segment pneumonia in chest X-ray images, generalizing it to COVID-19, and used this to calculate a Pneumonia Ratio for severity scoring and longitudinal disease monitoring in hospitalized patients.
In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression. To achieve this goal, we developed a deep learning model for simultaneous detection and segmentation of pneumonia in chest Xray (CXR) images and generalized to COVID-19 pneumonia. The segmentations were utilized to calculate a "Pneumonia Ratio" which indicates the disease severity. The measurement of disease severity enables to build a disease extent profile over time for hospitalized patients. To validate the model relevance 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.