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The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia

Authors :
Balázs Futácsi
Pál Maurovich-Horvat
Anna Sára Kardos
Norbert Nagy
Bence Fejer
Chiara Nardocci
Renad-Heyam Abdelrahman
Judit Simon
Emese Zsarnóczay
Béla Merkely
István Szabó
Veronika Müller
Source :
Tomography, Volume 7, Issue 4, Pages 58-710, Tomography, Vol 7, Iss 58, Pp 697-710 (2021)
Publication Year :
2021
Publisher :
Multidisciplinary Digital Publishing Institute, 2021.

Abstract

We sought to analyze the prognostic value of laboratory and clinical data, and an artificial intelligence (AI)-based algorithm for Coronavirus disease 2019 (COVID-19) severity scoring, on CT-scans of patients hospitalized with COVID-19. Moreover, we aimed to determine personalized probabilities of clinical deterioration. Data of symptomatic patients with COVID-19 who underwent chest-CT-examination at the time of hospital admission between April and November 2020 were analyzed. COVID-19 severity score was automatically quantified for each pulmonary lobe as the percentage of affected lung parenchyma with the AI-based algorithm. Clinical deterioration was defined as a composite of admission to the intensive care unit, need for invasive mechanical ventilation, use of vasopressors or in-hospital mortality. In total 326 consecutive patients were included in the analysis (mean age 66.7 ± 15.3 years, 52.1% male) of whom 85 (26.1%) experienced clinical deterioration. In the multivariable regression analysis prior myocardial infarction (OR = 2.81, 95% CI = 1.12–7.04, p = 0.027), immunodeficiency (OR = 2.08, 95% CI = 1.02–4.25, p = 0.043), C-reactive protein (OR = 1.73, 95% CI = 1.32–2.33, p &lt<br />0.001) and AI-based COVID-19 severity score (OR = 1.08<br />95% CI = 1.02–1.15, p = 0.013) appeared to be independent predictors of clinical deterioration. Personalized probability values were determined. AI-based COVID-19 severity score assessed at hospital admission can provide additional information about the prognosis of COVID-19, possibly serving as a useful tool for individualized risk-stratification.

Details

Language :
English
ISSN :
2379139X
Database :
OpenAIRE
Journal :
Tomography
Accession number :
edsair.doi.dedup.....7a1f0c2cd38c224babfbcb2833badbd9
Full Text :
https://doi.org/10.3390/tomography7040058