Automated Detection and Quantification of COVID-19 Airspace Disease on Chest Radiographs: A Novel Approach Achieving Expert Radiologist-Level Performance Using a Deep Convolutional Neural Network Trained on Digital Reconstructed Radiographs From Computed Tomography-Derived Ground Truth. Issue 8 (August 2021)

• Record Type: Journal Article
• Authors: Mortani Barbosa, Eduardo J.; Gefter, Warren B.; Ghesu, Florin C.; Liu, Siqi; Mailhe, Boris; Mansoor, Awais; Grbic, Sasa; Vogt, Sebastian
• Is Part Of: Investigative radiology. Volume 56:Issue 8(2021)
• Publication Date: 2021-08
• Subjects: artificial intelligence -- deep learning -- radiography, thoracic -- tomography -- COVID-19; Diagnosis, Radioscopic -- Periodicals; Radiology, Medical -- Periodicals; 616.0757
• DOI: 10.1097/RLI.0000000000000763
• Languages: English
• ISSNs: 0020-9996

Abstract

Abstract : Objectives: The aim of this study was to leverage volumetric quantification of airspace disease (AD) derived from a superior modality (computed tomography [CT]) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to (1) train a convolutional neural network (CNN) to quantify AD on paired chest radiographs (CXRs) and CTs, and (2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19. Materials and Methods: We retrospectively selected a cohort of 86 COVID-19 patients (with positive reverse transcriptase-polymerase chain reaction test results) from March to May 2020 at a tertiary hospital in the northeastern United States, who underwent chest CT and CXR within 48 hours. The ground-truth volumetric percentage of COVID-19-related AD (POv) was established by manual AD segmentation on CT. The resulting 3-dimensional masks were projected into 2-dimensional anterior-posterior DRR to compute area-based AD percentage (POa). A CNN was trained with DRR images generated from a larger-scale CT dataset of COVID-19 and non-COVID-19 patients, automatically segmenting lungs, AD, and quantifying POa on CXR. The CNN POa results were compared with POa quantified on CXR by 2 expert readers and to the POv ground truth, by computing correlations and mean absolute errors. Results: Bootstrap mean absolute error and correlations between POa and POv were 11.98% (11.05%–12.47%) and 0.77 (0.70–0.82) for average of expert readers and 9.56% to 9.78% (8.83%–10.22%) and 0.78 to 0.81 (0.73–0.85) for the CNN, respectively. Conclusions: Our CNN trained with DRR using CT-derived airspace quantification achieved expert radiologist level of accuracy in the quantification of AD on CXR in patients with positive reverse transcriptase-polymerase chain reaction test results for COVID-19. Abstract : Supplemental digital content is available in the text.