Open AccessA Deep-Learning Diagnostic Support System for the Detection of COVID-19 Using Chest Radiographs
Author(s) -
Matthias Fontanellaz,
Lukas Ebner,
Adrian Thomas Huber,
Alan A. Peters,
Laura Löbelenz,
Cynthia Hourscht,
Jeremias Klaus,
Jaro Munz,
Thomas D. Ruder,
Dionysios Drakopoulos,
Dominik Sieroń,
Elias Primetis,
Johannes T. Heverhagen,
Stavroula Mougiakakou,
Andreas Christe
Publication year - 2020
Publication title -
investigative radiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.33
H-Index - 114
eISSN - 1536-0210
pISSN - 0020-9996
DOI - 10.1097/rli.0000000000000748
Subject(s) - pneumonia , medicine , covid-19 , radiography , radiology , pathology , disease , outbreak , infectious disease (medical specialty)
Five publicly available databases comprising normal CXR, confirmed COVID-19 pneumonia cases, and other pneumonias were used. After the harmonization of the data, the training set included 7966 normal cases, 5451 with other pneumonia, and 258 CXRs with COVID-19 pneumonia, whereas in the testing data set, each category was represented by 100 cases. Eleven blinded radiologists with various levels of expertise independently read the testing data set. The data were analyzed separately with the newly proposed artificial intelligence-based system and by consultant radiologists and residents, with respect to positive predictive value (PPV), sensitivity, and F-score (harmonic mean for PPV and sensitivity). The χ2 test was used to compare the sensitivity, specificity, accuracy, PPV, and F-scores of the readers and the system.