Detection of COVID-19 using deep learning algorithms on chest radiographs

Wan Hang Keith CHIU, Varut VARDHANABHUTI, Dmytro POPLAVSKIY, Leung Ho Philip YU, Richard DU, Alistair Yun Hee YAP, Sailong ZHANG, Ambrose Ho-Tung FONG, Thomas Wing-Yan CHIN, Jonan Chun Yin LEE, Siu Ting LEUNG, Christine Shing Yen LO, Macy Mei-Sze LUI, Benjamin Xin Hao FANG, Ming-Yen NG, Michael D KUO

Research output: Contribution to journalArticlespeer-review

13 Citations (Scopus)


Purpose: To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR).
Materials and methods: In this retrospective study, a DL model was trained on 112,120 CXR images with 14 labeled classifiers (ChestX-ray14) and fine-tuned using initial CXR on hospital admission of 509 patients, who had undergone COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR). The test set consisted of a CXR on presentation of 248 individuals suspected of COVID-19 pneumonia between February 16 and March 3, 2020 from 4 centers (72 RT-PCR positives and 176 RT-PCR negatives). The CXR were independently reviewed by 3 radiologists and using the DL algorithm. Diagnostic performance was compared with radiologists' performance and was assessed by area under the receiver operating characteristics (AUC).
Results: The median age of the subjects in the test set was 61 (interquartile range: 39 to 79) years (51% male). The DL algorithm achieved an AUC of 0.81, sensitivity of 0.85, and specificity of 0.72 in detecting COVID-19 using RT-PCR as the reference standard. On subgroup analyses, the model achieved an AUC of 0.79, sensitivity of 0.80, and specificity of 0.74 in detecting COVID-19 in patients presented with fever or respiratory systems and an AUC of 0.87, sensitivity of 0.85, and specificity of 0.81 in distinguishing COVID-19 from other forms of pneumonia. The algorithm significantly outperforms human readers (P<0.001 using DeLong test) with higher sensitivity (P=0.01 using McNemar test).
Conclusions: A DL algorithm (COV19NET) for the detection of COVID-19 on chest radiographs can potentially be an effective tool in triaging patients, particularly in resource-stretched health-care systems. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.
Original languageEnglish
Pages (from-to)369-376
JournalJournal of Thoracic Imaging
Issue number6
Publication statusPublished - Nov 2020


Chiu, W. H. K., Vardhanabhuti, V., Poplavskiy, D., Yu, P. L. H., Du, R., Yap, A. Y. H., . . . Kuo, M. D. (2021). Detection of COVID-19 using deep learning algorithms on chest radiographs. Journal of Thoracic Imaging, 35(6), 369-376. doi: 10.1097/RTI.0000000000000559


  • COVID-19
  • Chest radiographs
  • Artificial intelligence
  • Deep learning
  • Pneumonia


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