Radiomics for Predicting Oxygen Necessity in COVID-19 Patients Using Longitudinal Lung Computed Tomography-Reference-Cited by-同舟云学术

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Radiomics for Predicting Oxygen Necessity in COVID-19 Patients Using Longitudinal Lung Computed Tomography

Published:2023-12-04 Issue: Volume:abs/2010.16061 Page:272-276
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Container-title:2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE)
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Author:

Ravazio Rafaela¹,Mattjie Christian¹,De Moura Luis Vinicius¹,Kupssinskü Lucas¹,Barros Rodrigo C.¹,Machado Denise Cantarelli²,Da Silva Ana Maria Marques³

Affiliation:

1. Pontifical Catholic University of Rio Grande do Sul,Machine Learning Theory and Applications Lab (MALTA),Porto Alegre,Brazil

2. Graduate Program in Biomedical Gerontology, Pontifical Catholic University of Rio Grande do Sul,Porto Alegre,Brazil

3. Graduate Program in Radiology, University of São Paulo,São Paulo,Brazil

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10431814/10431802/10431834.pdf?arnumber=10431834

Reference24 articles.

1. The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic

2. Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China

3. Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics

4. Radiomics: Extracting more information from medical images using advanced feature analysis

5. A Radiomics Signature to Quantitatively Analyze COVID-19-Infected Pulmonary Lesions

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