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Association of AI quantified COVID-19 chest CT and patient outcome
PURPOSE: Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822756/ https://www.ncbi.nlm.nih.gov/pubmed/33484428 http://dx.doi.org/10.1007/s11548-020-02299-5 |
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author | Fang, Xi Kruger, Uwe Homayounieh, Fatemeh Chao, Hanqing Zhang, Jiajin Digumarthy, Subba R. Arru, Chiara D. Kalra, Mannudeep K. Yan, Pingkun |
author_facet | Fang, Xi Kruger, Uwe Homayounieh, Fatemeh Chao, Hanqing Zhang, Jiajin Digumarthy, Subba R. Arru, Chiara D. Kalra, Mannudeep K. Yan, Pingkun |
author_sort | Fang, Xi |
collection | PubMed |
description | PURPOSE: Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome. METHODS: We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients). RESULTS: AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman’s rank correlation 0.837, [Formula: see text] ). Using AI-based scores produced significantly higher ([Formula: see text] ) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets. CONCLUSIONS: Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic. |
format | Online Article Text |
id | pubmed-7822756 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-78227562021-01-25 Association of AI quantified COVID-19 chest CT and patient outcome Fang, Xi Kruger, Uwe Homayounieh, Fatemeh Chao, Hanqing Zhang, Jiajin Digumarthy, Subba R. Arru, Chiara D. Kalra, Mannudeep K. Yan, Pingkun Int J Comput Assist Radiol Surg Original Article PURPOSE: Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome. METHODS: We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients). RESULTS: AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman’s rank correlation 0.837, [Formula: see text] ). Using AI-based scores produced significantly higher ([Formula: see text] ) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets. CONCLUSIONS: Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic. Springer International Publishing 2021-01-23 2021 /pmc/articles/PMC7822756/ /pubmed/33484428 http://dx.doi.org/10.1007/s11548-020-02299-5 Text en © CARS 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article Fang, Xi Kruger, Uwe Homayounieh, Fatemeh Chao, Hanqing Zhang, Jiajin Digumarthy, Subba R. Arru, Chiara D. Kalra, Mannudeep K. Yan, Pingkun Association of AI quantified COVID-19 chest CT and patient outcome |
title | Association of AI quantified COVID-19 chest CT and patient outcome |
title_full | Association of AI quantified COVID-19 chest CT and patient outcome |
title_fullStr | Association of AI quantified COVID-19 chest CT and patient outcome |
title_full_unstemmed | Association of AI quantified COVID-19 chest CT and patient outcome |
title_short | Association of AI quantified COVID-19 chest CT and patient outcome |
title_sort | association of ai quantified covid-19 chest ct and patient outcome |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822756/ https://www.ncbi.nlm.nih.gov/pubmed/33484428 http://dx.doi.org/10.1007/s11548-020-02299-5 |
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