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. 2023 Nov 10;12(22):7039.
doi: 10.3390/jcm12227039.

The Diagnostic Utility of Artificial Intelligence-Guided Computed Tomography-Based Severity Scores for Predicting Short-Term Clinical Outcomes in Adults with COVID-19 Pneumonia

Zeynep Atceken  1 , Yeliz Celik  2 , Cetin Atasoy  1 , Yüksel Peker  2   3   4   5   6
Affiliations

The Diagnostic Utility of Artificial Intelligence-Guided Computed Tomography-Based Severity Scores for Predicting Short-Term Clinical Outcomes in Adults with COVID-19 Pneumonia

Zeynep Atceken et al. J Clin Med. .

Abstract

Chest computed tomography (CT) imaging with the use of an artificial intelligence (AI) analysis program has been helpful for the rapid evaluation of large numbers of patients during the COVID-19 pandemic. We have previously demonstrated that adults with COVID-19 infection with high-risk obstructive sleep apnea (OSA) have poorer clinical outcomes than COVID-19 patients with low-risk OSA. In the current secondary analysis, we evaluated the association of AI-guided CT-based severity scores (SSs) with short-term outcomes in the same cohort. In total, 221 patients (mean age of 52.6 ± 15.6 years, 59% men) with eligible chest CT images from March to May 2020 were included. The AI program scanned the CT images in 3D, and the algorithm measured volumes of lobes and lungs as well as high-opacity areas, including ground glass and consolidation. An SS was defined as the ratio of the volume of high-opacity areas to that of the total lung volume. The primary outcome was the need for supplemental oxygen and hospitalization over 28 days. A receiver operating characteristic (ROC) curve analysis of the association between an SS and the need for supplemental oxygen revealed a cut-off score of 2.65 on the CT images, with a sensitivity of 81% and a specificity of 56%. In a multivariate logistic regression model, an SS > 2.65 predicted the need for supplemental oxygen, with an odds ratio (OR) of 3.98 (95% confidence interval (CI) 1.80-8.79; p < 0.001), and hospitalization, with an OR of 2.40 (95% CI 1.23-4.71; p = 0.011), adjusted for age, sex, body mass index, diabetes, hypertension, and coronary artery disease. We conclude that AI-guided CT-based SSs can be used for predicting the need for supplemental oxygen and hospitalization in patients with COVID-19 pneumonia.

Keywords: COVID-19; artificial intelligence; chest CT; clinical outcomes; supplemental oxygen.

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Conflict of interest statement

Y.P. reports grants from ResMed Inc and ResMed Foundation outside the submitted work. Z.A., C.A. and Y.C. have nothing to declare.

Figures

Figure 1
Figure 1
Flow of patients through the study. COVID-19 = coronavirus disease; CT = computed tomography.
Figure 2
Figure 2
Chest tomography images and artificial intelligence calculations of a patient with a severity score of 1.1.
Figure 3
Figure 3
Chest tomography images and artificial intelligence calculations of a patient with a severity score of 4.8.
Figure 4
Figure 4
ROC analysis curve for patients requiring supplemental oxygen and hospitalization. Abbreviations: AUC = area under the curve; CI = confidence interval.
See this image and copyright information in PMC

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References

    1. Coronavirus Disease (COVID-19 Pandemic) [(accessed on 16 October 2023)]. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
    1. Yang W., Sirajuddin A., Zhang X., Liu G., Teng Z., Zhao S., Lu M. The role of imaging in 2019 novel coronavirus pneumonia (COVID-19) Eur. Radiol. 2020;30:4874–4882. doi: 10.1007/s00330-020-06827-4. - DOI - PMC - PubMed
    1. Jin Y.-H., Cai L., Cheng Z.-S., Cheng H., Deng T., Fan Y.-P., Fang C., Huang D., Huang L.-Q., Huang Q., et al. A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version) Mil. Med. Res. 2020;7:4. doi: 10.1186/s40779-020-0233-6. - DOI - PMC - PubMed
    1. Xie X., Zhong Z., Zhao W., Zheng C., Wang F., Liu J. Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR Testing. Radiology. 2020;296:E41–E45. doi: 10.1148/radiol.2020200343. - DOI - PMC - PubMed
    1. Zhao W., Zhong Z., Xie X., Yu Q., Liu J. Relation Between Chest CT Findings and Clinical Conditions of Coronavirus Disease (COVID-19) Pneumonia: A Multicenter Study. Am. J. Roentgenol. 2020;214:1072–1077. doi: 10.2214/AJR.20.22976. - DOI - PubMed

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