Utilizing AI CAD for early pandemic screening in chest radiographs

To investigate the potential application of existing artificial intelligence (AI) software in diagnosing COVID-19 (coronavirus disease 2019) and other pneumonia-related radiographic findings with the unprecedented challenge by COVID-19 pandemic, leveraging pre-trained commercial AI software originally designed for detecting pulmonary nodules in chest radiographs, we assessed its performance using public dataset comprising chest radiographs collected during the early stages of pandemic. The software demonstrated promising results, achieving a sensitivity of 86.83% and a specificity of 59.59% in detecting pneumonia features, including those caused by COVID-19. Considering only posterior-anterior (PA) radiographs, AI software exhibited improved performance, with sensitivity 89.16% and specificity 67.41%. The findings suggest the AI CAD software could serve as a triage for initial screening of infectious pulmonary diseases, especially in regions with limited access to advanced medical resources. This study underscores the importance of promptly adapting existing AI CAD to address urgent healthcare needs during infectious disease outbreaks.

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Publication Details

Journal
Scientific Reports
Published
2026-01-06
DOI
https://doi.org/10.1038/s41598-025-14195-6
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
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article

Utilizing AI CAD for early pandemic screening in chest radiographs

YoungBeom Kim
Scientific Reports
COVID-19 diagnosis using AI
article

Utilizing AI CAD for early pandemic screening in chest radiographs

YoungBeom Kim
article en

Abstract

To investigate the potential application of existing artificial intelligence (AI) software in diagnosing COVID-19 (coronavirus disease 2019) and other pneumonia-related radiographic findings with the unprecedented challenge by COVID-19 pandemic, leveraging pre-trained commercial AI software originally designed for detecting pulmonary nodules in chest radiographs, we assessed its performance using public dataset comprising chest radiographs collected during the early stages of pandemic. The software demonstrated promising results, achieving a sensitivity of 86.83% and a specificity of 59.59% in detecting pneumonia features, including those caused by COVID-19. Considering only posterior-anterior (PA) radiographs, AI software exhibited improved performance, with sensitivity 89.16% and specificity 67.41%. The findings suggest the AI CAD software could serve as a triage for initial screening of infectious pulmonary diseases, especially in regions with limited access to advanced medical resources. This study underscores the importance of promptly adapting existing AI CAD to address urgent healthcare needs during infectious disease outbreaks.

Scientific ReportsVol. 16(1)
Pyeongtaek University (KR)
Good health and well-being
Openalex Percentile: Top 96%
COVID-19 diagnosis using AI
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