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

Autor: Chrzan R; Department of Radiology, Jagiellonian University Medical College, Kopernika 19, Krakow, 31-501, Poland. robert.chrzan@uj.edu.pl., Wizner B; Department of Internal Medicine and Gerontology, Jagiellonian University Medical College, Krakow, Poland., Sydor W; Department of Rheumatology and Immunology, Jagiellonian University Medical College, Krakow, Poland., Wojciechowska W; 1st Department of Cardiology, Interventional Electrocardiology and Arterial Hypertension, Jagiellonian University Medical College, Krakow, Poland., Popiela T; Department of Radiology, Jagiellonian University Medical College, Kopernika 19, Krakow, 31-501, Poland., Bociąga-Jasik M; Department of Infectious Diseases, Jagiellonian University Medical College, Krakow, Poland., Olszanecka A; 1st Department of Cardiology, Interventional Electrocardiology and Arterial Hypertension, Jagiellonian University Medical College, Krakow, Poland., Strach M; Department of Rheumatology and Immunology, Jagiellonian University Medical College, Krakow, Poland.
Jazyk: angličtina
Zdroj: BMC infectious diseases [BMC Infect Dis] 2023 May 10; Vol. 23 (1), pp. 314. Date of Electronic Publication: 2023 May 10.
DOI: 10.1186/s12879-023-08303-y
Abstrakt: 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 cm 3 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.
(© 2023. The Author(s).)
Databáze: MEDLINE
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