Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification

Abstract To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 infection and initially of non-severe type w...

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Autores principales:	Yuehua Li, Kai Shang, Wei Bian, Li He, Ying Fan, Tao Ren, Jiayin Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/76cf112cbaf740a580a00ea66995c052
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spelling	oai:doaj.org-article:76cf112cbaf740a580a00ea66995c0522021-12-02T13:34:00ZPrediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification10.1038/s41598-020-79097-12045-2322https://doaj.org/article/76cf112cbaf740a580a00ea66995c0522020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79097-1https://doaj.org/toc/2045-2322Abstract To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 infection and initially of non-severe type were retrospectively included. The initial CT scan on admission was used for imaging analysis. The presence of ground glass opacity (GGO), consolidation and other findings were visually evaluated. CT severity score was calculated according to the extent of lesion involvement. In addition, AI based quantification of GGO and consolidation volume were also performed. 123 patients (mean age: 64.43 ± 14.02; 62 males) were included. GGO + consolidation was more frequently revealed in progress-to-severe group whereas pure GGO was more likely to be found in non-severe group. Compared to non-severe group, patients in progress-to-severe group had larger GGO volume (167.33 ± 167.88 cm3 versus 101.12 ± 127 cm3, p = 0.013) as well as consolidation volume (40.85 ± 60.4 cm3 versus 6.63 ± 14.91 cm3, p < 0.001). Among imaging parameters, consolidation volume had the largest area under curve (AUC) in discriminating non-severe from progress-to-severe group (AUC = 0.796, p < 0.001) and patients with or without critical events (AUC = 0.754, p < 0.001). According to multivariate regression, consolidation volume and age were two strongest predictors for disease progression (hazard ratio: 1.053 and 1.071, p: 0.006 and 0.008) whereas age and diabetes were predictors for unfavorable outcome. Consolidation volume quantified on initial chest CT was the strongest predictor for disease severity progression and larger consolidation volume was associated with unfavorable clinical outcome.Yuehua LiKai ShangWei BianLi HeYing FanTao RenJiayin ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yuehua Li Kai Shang Wei Bian Li He Ying Fan Tao Ren Jiayin Zhang Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification
description	Abstract To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 infection and initially of non-severe type were retrospectively included. The initial CT scan on admission was used for imaging analysis. The presence of ground glass opacity (GGO), consolidation and other findings were visually evaluated. CT severity score was calculated according to the extent of lesion involvement. In addition, AI based quantification of GGO and consolidation volume were also performed. 123 patients (mean age: 64.43 ± 14.02; 62 males) were included. GGO + consolidation was more frequently revealed in progress-to-severe group whereas pure GGO was more likely to be found in non-severe group. Compared to non-severe group, patients in progress-to-severe group had larger GGO volume (167.33 ± 167.88 cm3 versus 101.12 ± 127 cm3, p = 0.013) as well as consolidation volume (40.85 ± 60.4 cm3 versus 6.63 ± 14.91 cm3, p < 0.001). Among imaging parameters, consolidation volume had the largest area under curve (AUC) in discriminating non-severe from progress-to-severe group (AUC = 0.796, p < 0.001) and patients with or without critical events (AUC = 0.754, p < 0.001). According to multivariate regression, consolidation volume and age were two strongest predictors for disease progression (hazard ratio: 1.053 and 1.071, p: 0.006 and 0.008) whereas age and diabetes were predictors for unfavorable outcome. Consolidation volume quantified on initial chest CT was the strongest predictor for disease severity progression and larger consolidation volume was associated with unfavorable clinical outcome.
format	article
author	Yuehua Li Kai Shang Wei Bian Li He Ying Fan Tao Ren Jiayin Zhang
author_facet	Yuehua Li Kai Shang Wei Bian Li He Ying Fan Tao Ren Jiayin Zhang
author_sort	Yuehua Li
title	Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification
title_short	Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification
title_full	Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification
title_fullStr	Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification
title_full_unstemmed	Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification
title_sort	prediction of disease progression in patients with covid-19 by artificial intelligence assisted lesion quantification
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/76cf112cbaf740a580a00ea66995c052
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Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification

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