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. 2021 Jul 20:4:590189.
doi: 10.3389/frai.2021.590189. eCollection 2021.

AI-Assisted CT as a Clinical and Research Tool for COVID-19

Zion Tsz Ho Tse  1 , Sierra Hovet  1 , Hongliang Ren  2 , Tristan Barrett  3 , Sheng Xu  4 , Baris Turkbey  4 , Bradford J Wood  4
Affiliations

AI-Assisted CT as a Clinical and Research Tool for COVID-19

Zion Tsz Ho Tse et al. Front Artif Intell. .

Abstract

There is compelling support for widening the role of computed tomography (CT) for COVID-19 in clinical and research scenarios. Reverse transcription polymerase chain reaction (RT-PCR) testing, the gold standard for COVID-19 diagnosis, has two potential weaknesses: the delay in obtaining results and the possibility of RT-PCR test kits running out when demand spikes or being unavailable altogether. This perspective article discusses the potential use of CT in conjunction with RT-PCR in hospitals lacking sufficient access to RT-PCR test kits. The precedent for this approach is discussed based on the use of CT for COVID-19 diagnosis and screening in the United Kingdom and China. The hurdles and challenges are presented, which need addressing prior to realization of the potential roles for CT artificial intelligence (AI). The potential roles include a more accurate clinical classification, characterization for research roles and mechanisms, and informing clinical trial response criteria as a surrogate for clinical outcomes.

Keywords: COVID-19; RT-PCR; artificial intelligence; computed tomography; diagnosis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
CT images of the lungs in a subject with COVID-19. (A–C) Original CT images. (D–F) CT images with AI segmentation of COVID-19 lung opacities.
See this image and copyright information in PMC

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References

    1. A. C. O. Radiology (2020). ACR Recommendations for the Use of Chest Radiography and Computed Tomography (CT) for Suspected COVID-19 Infection. Available: https://www.acr.org/Advocacy-and-Economics/ACR-Position-Statements/Recom... (Accessed March 31, 2021).
    1. Ai T., Yang Z., Hou H., Zhan C., Chen C., Lv W., et al. (2020). Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology 296 (2), E32–E40. 10.1148/radiol.2020200642 - DOI - PMC - PubMed
    1. Amalou A., Türkbey B., Sanford T., Harmon S., Türkbey E. B., Xu S., et al. (2020). Targeted Early Chest CT in COVID-19 Outbreaks as Diagnostic Tool for Containment of the Pandemic- A Multinational Opinion. Diagn. Interv. Radiol. 26 (4), 04–24. 10.5152/dir.2020.20231 - DOI - PMC - PubMed
    1. Apostolopoulos I. D., Bessiana T. (2003). Covid-19: Automatic Detection from X-Ray Images Utilizing Transfer Learning with Convolutional Neural Networks. arXiv, 11617. - PMC - PubMed
    1. Bernheim A., Mei X., Huang M., Yang Y., Fayad Z. A., Zhang N. (2020). Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection. Radiology 295, 200463. - PMC - PubMed

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