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. 2022 Jun 14;12(6):1465.
doi: 10.3390/diagnostics12061465.

Artificial Intelligence-Based Detection of Pneumonia in Chest Radiographs

Judith Becker  1 , Josua A Decker  1 , Christoph Römmele  2 , Maria Kahn  2 , Helmut Messmann  2 , Markus Wehler  3   4 , Florian Schwarz  1 , Thomas Kroencke  1 , Christian Scheurig-Muenkler  1
Affiliations

Artificial Intelligence-Based Detection of Pneumonia in Chest Radiographs

Judith Becker et al. Diagnostics (Basel). .

Abstract

Artificial intelligence is gaining increasing relevance in the field of radiology. This study retrospectively evaluates how a commercially available deep learning algorithm can detect pneumonia in chest radiographs (CR) in emergency departments. The chest radiographs of 948 patients with dyspnea between 3 February and 8 May 2020, as well as 15 October and 15 December 2020, were used. A deep learning algorithm was used to identify opacifications associated with pneumonia, and the performance was evaluated by using ROC analysis, sensitivity, specificity, PPV and NPV. Two radiologists assessed all enrolled images for pulmonal infection patterns as the reference standard. If consolidations or opacifications were present, the radiologists classified the pulmonal findings regarding a possible COVID-19 infection because of the ongoing pandemic. The AUROC value of the deep learning algorithm reached 0.923 when detecting pneumonia in chest radiographs with a sensitivity of 95.4%, specificity of 66.0%, PPV of 80.2% and NPV of 90.8%. The detection of COVID-19 pneumonia in CR by radiologists was achieved with a sensitivity of 50.6% and a specificity of 73%. The deep learning algorithm proved to be an excellent tool for detecting pneumonia in chest radiographs. Thus, the assessment of suspicious chest radiographs can be purposefully supported, shortening the turnaround time for reporting relevant findings and aiding early triage.

Keywords: COVID-19; artificial intelligence; chest radiograph; deep learning; early detection; pneumonia.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Chest radiographs with the distinguished distribution patterns regarding the probability of COVID-19 infection. (a) Typical (bilateral, peripheral opacifications), (b) almost typical (unilateral, peripheral opacifications), (c) non-typical (limited to one pulmonary lobe consistent with a lobar pneumonia) and (d) indeterminate (opacifications that could not be clearly classified as typical, almost typical, or non-typical).
Figure 2
Figure 2
Different distribution patterns in patients with and without COVID-19. Significantly different distribution patterns of opacifications and/or consolidations in chest radiographs between patients with confirmed and ruled-out COVID-19 infections.
Figure 3
Figure 3
Diagnostic accuracy of the deep learning algorithm for detecting pneumonia. Opacifications and/or consolidations associated with pneumonia can be detected with a corresponding AUROC value of 0.923.
Figure 4
Figure 4
Performance of the artificial intelligence in detecting pneumonia in chest radiographs. Performance of the artificial intelligence in detecting pneumonia in chest radiographs with the radiologists’ assessment as reference standard.
Figure 5
Figure 5
False positive assignments by the AI. Examples of chest radiographs with false positive assignments by the artificial intelligence possibly caused by large pleural effusions (a,b) or a small dystelectasis in the right lower field (c).
See this image and copyright information in PMC

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