Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs

J Thorac Imaging. 2020 Nov 1;35(6):369-376. doi: 10.1097/RTI.0000000000000559.


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.

MeSH terms

  • Adult
  • Aged
  • Algorithms
  • COVID-19 / diagnostic imaging*
  • Deep Learning*
  • Female
  • Humans
  • Lung / diagnostic imaging*
  • Male
  • Middle Aged
  • Radiographic Image Interpretation, Computer-Assisted / methods*
  • Radiography, Thoracic / methods*
  • Retrospective Studies
  • SARS-CoV-2
  • Sensitivity and Specificity
  • Young Adult