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Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images

We have conducted a study of the COVID-19 severity with the chest x-ray images, a private dataset collected from our collaborator St Bernards Medical Center. The dataset is comprised of chest x-ray images from 1,550 patients who were admitted to emergency room (ER) and were all tested positive for C...

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Autores principales: Stubblefield, Jonathan, Saldivar, Christopher, Feria, Anna De, Riddle, James, Shivkumar, Abhijit, Causey, Jason, Qualls, Jake, Fowler, Jennifer, Huang, Xiuzhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9810237/
https://www.ncbi.nlm.nih.gov/pubmed/36597524
http://dx.doi.org/10.1101/2022.12.25.22283942
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author Stubblefield, Jonathan
Saldivar, Christopher
Feria, Anna De
Riddle, James
Shivkumar, Abhijit
Causey, Jason
Qualls, Jake
Fowler, Jennifer
Huang, Xiuzhen
author_facet Stubblefield, Jonathan
Saldivar, Christopher
Feria, Anna De
Riddle, James
Shivkumar, Abhijit
Causey, Jason
Qualls, Jake
Fowler, Jennifer
Huang, Xiuzhen
author_sort Stubblefield, Jonathan
collection PubMed
description We have conducted a study of the COVID-19 severity with the chest x-ray images, a private dataset collected from our collaborator St Bernards Medical Center. The dataset is comprised of chest x-ray images from 1,550 patients who were admitted to emergency room (ER) and were all tested positive for COVID-19. Our study is focused on the following two questions: (1) To predict patients hospital staying duration, based on the chest x-ray image which was taken when the patient was admitted to the ER. The length of stay ranged from zero hours to 95 days in the hospital and followed a power law distribution. Based on our testing results, it is hard for the prediction models to detect strong signal from the chest x-ray images. No model was able to perform better than a trivial most-frequent classifier. However, each model was able to outperform the most-frequent classifier when the data was split evenly into four categories. This would suggest that there is signal in the images, and the performance may be further improved by the addition of clinical features as well as increasing the training set. (2) To predict if a patient is COVID-19 positive or not with the chest x-ray image. We also tested the generalizability of training a prediction model on chest x-ray images from one hospital and then testing the model on images captures from other sites. With our private dataset and the COVIDx dataset, the prediction model can achieve a high accuracy of 95.9%. However, for our hold-one-out study of the generalizability of the models trained on chest x-rays, we found that the model performance suffers due to a significant reduction in training samples of any class.
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spelling pubmed-98102372023-01-04 Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images Stubblefield, Jonathan Saldivar, Christopher Feria, Anna De Riddle, James Shivkumar, Abhijit Causey, Jason Qualls, Jake Fowler, Jennifer Huang, Xiuzhen medRxiv Article We have conducted a study of the COVID-19 severity with the chest x-ray images, a private dataset collected from our collaborator St Bernards Medical Center. The dataset is comprised of chest x-ray images from 1,550 patients who were admitted to emergency room (ER) and were all tested positive for COVID-19. Our study is focused on the following two questions: (1) To predict patients hospital staying duration, based on the chest x-ray image which was taken when the patient was admitted to the ER. The length of stay ranged from zero hours to 95 days in the hospital and followed a power law distribution. Based on our testing results, it is hard for the prediction models to detect strong signal from the chest x-ray images. No model was able to perform better than a trivial most-frequent classifier. However, each model was able to outperform the most-frequent classifier when the data was split evenly into four categories. This would suggest that there is signal in the images, and the performance may be further improved by the addition of clinical features as well as increasing the training set. (2) To predict if a patient is COVID-19 positive or not with the chest x-ray image. We also tested the generalizability of training a prediction model on chest x-ray images from one hospital and then testing the model on images captures from other sites. With our private dataset and the COVIDx dataset, the prediction model can achieve a high accuracy of 95.9%. However, for our hold-one-out study of the generalizability of the models trained on chest x-rays, we found that the model performance suffers due to a significant reduction in training samples of any class. Cold Spring Harbor Laboratory 2022-12-27 /pmc/articles/PMC9810237/ /pubmed/36597524 http://dx.doi.org/10.1101/2022.12.25.22283942 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Stubblefield, Jonathan
Saldivar, Christopher
Feria, Anna De
Riddle, James
Shivkumar, Abhijit
Causey, Jason
Qualls, Jake
Fowler, Jennifer
Huang, Xiuzhen
Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
title Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
title_full Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
title_fullStr Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
title_full_unstemmed Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
title_short Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
title_sort study covid-19 severity of patients admitted to emergency room (er) with chest x-ray images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9810237/
https://www.ncbi.nlm.nih.gov/pubmed/36597524
http://dx.doi.org/10.1101/2022.12.25.22283942
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