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. 2023 May 10;23(1):314.
doi: 10.1186/s12879-023-08303-y.

Artificial intelligence guided HRCT assessment predicts the severity of COVID-19 pneumonia based on clinical parameters

Robert Chrzan  1 , Barbara Wizner  2 , Wojciech Sydor  3 , Wiktoria Wojciechowska  4 , Tadeusz Popiela  5 , Monika Bociąga-Jasik  6 , Agnieszka Olszanecka  4 , Magdalena Strach  3
Affiliations

Artificial intelligence guided HRCT assessment predicts the severity of COVID-19 pneumonia based on clinical parameters

Robert Chrzan et al. BMC Infect Dis. .

Abstract

Background: The purpose of the study was to compare the results of AI (artificial intelligence) analysis of the extent of pulmonary lesions on HRCT (high resolution computed tomography) images in COVID-19 pneumonia, with clinical data including laboratory markers of inflammation, to verify whether AI HRCT assessment can predict the clinical severity of COVID-19 pneumonia.

Methods: The analyzed group consisted of 388 patients with COVID-19 pneumonia, with automatically analyzed HRCT parameters of volume: AIV (absolute inflammation), AGV (absolute ground glass), ACV (absolute consolidation), PIV (percentage inflammation), PGV (percentage ground glass), PCV (percentage consolidation). Clinical data included: age, sex, admission parameters: respiratory rate, oxygen saturation, CRP (C-reactive protein), IL6 (interleukin 6), IG - immature granulocytes, WBC (white blood count), neutrophil count, lymphocyte count, serum ferritin, LDH (lactate dehydrogenase), NIH (National Institute of Health) severity score; parameters of clinical course: in-hospital death, transfer to the ICU (intensive care unit), length of hospital stay.

Results: The highest correlation coefficients were found for PGV, PIV, with LDH (respectively 0.65, 0.64); PIV, PGV, with oxygen saturation (respectively - 0.53, -0.52); AIV, AGV, with CRP (respectively 0.48, 0.46); AGV, AIV, with ferritin (respectively 0.46, 0.45). Patients with critical pneumonia had significantly lower oxygen saturation, and higher levels of immune-inflammatory biomarkers on admission. The radiological parameters of lung involvement proved to be strong predictors of transfer to the ICU (in particular, PGV ≥ cut-off point 29% with Odds Ratio (OR): 7.53) and in-hospital death (in particular: AIV ≥ cut-off point 831 cm3 with OR: 4.31).

Conclusions: Automatic analysis of HRCT images by AI may be a valuable method for predicting the severity of COVID-19 pneumonia. The radiological parameters of lung involvement correlate with laboratory markers of inflammation, and are strong predictors of transfer to the ICU and in-hospital death from COVID-19.

Trial registration: National Center for Research and Development CRACoV-HHS project, contract number SZPITALE-JEDNOIMIENNE/18/2020.

Keywords: Artificial intelligence; COVID-19; HRCT; In-hospital death; Inflammatory biomarkers; Oxygen saturation; Transfer to ICU.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The final report from the automatic analysis of HRCT by AI. Inflammation regions are marked in color depending on attenuation values in Hounsfield units; for example, the areas of ground glass are shown in blue
See this image and copyright information in PMC

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