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The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia
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 c...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8628928/ https://www.ncbi.nlm.nih.gov/pubmed/34842822 http://dx.doi.org/10.3390/tomography7040058 |
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author | Szabó, István Viktor Simon, Judit Nardocci, Chiara Kardos, Anna Sára Nagy, Norbert Abdelrahman, Renad-Heyam Zsarnóczay, Emese Fejér, Bence Futácsi, Balázs Müller, Veronika Merkely, Béla Maurovich-Horvat, Pál |
author_facet | Szabó, István Viktor Simon, Judit Nardocci, Chiara Kardos, Anna Sára Nagy, Norbert Abdelrahman, Renad-Heyam Zsarnóczay, Emese Fejér, Bence Futácsi, Balázs Müller, Veronika Merkely, Béla Maurovich-Horvat, Pál |
author_sort | Szabó, István Viktor |
collection | PubMed |
description | 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 < 0.001) and AI-based COVID-19 severity score (OR = 1.08; 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. |
format | Online Article Text |
id | pubmed-8628928 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86289282021-11-30 The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia Szabó, István Viktor Simon, Judit Nardocci, Chiara Kardos, Anna Sára Nagy, Norbert Abdelrahman, Renad-Heyam Zsarnóczay, Emese Fejér, Bence Futácsi, Balázs Müller, Veronika Merkely, Béla Maurovich-Horvat, Pál Tomography Article 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 < 0.001) and AI-based COVID-19 severity score (OR = 1.08; 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. MDPI 2021-11-01 /pmc/articles/PMC8628928/ /pubmed/34842822 http://dx.doi.org/10.3390/tomography7040058 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Szabó, István Viktor Simon, Judit Nardocci, Chiara Kardos, Anna Sára Nagy, Norbert Abdelrahman, Renad-Heyam Zsarnóczay, Emese Fejér, Bence Futácsi, Balázs Müller, Veronika Merkely, Béla Maurovich-Horvat, Pál The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia |
title | The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia |
title_full | The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia |
title_fullStr | The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia |
title_full_unstemmed | The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia |
title_short | The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia |
title_sort | predictive role of artificial intelligence-based chest ct quantification in patients with covid-19 pneumonia |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8628928/ https://www.ncbi.nlm.nih.gov/pubmed/34842822 http://dx.doi.org/10.3390/tomography7040058 |
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