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COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis
(1) Background: The new SARS-COV-2 pandemic overwhelmed intensive care units, clinicians, and radiologists, so the development of methods to forecast the diagnosis’ severity became a necessity and a helpful tool. (2) Methods: In this paper, we proposed an artificial intelligence-based multimodal app...
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/PMC8617902/ https://www.ncbi.nlm.nih.gov/pubmed/34833156 http://dx.doi.org/10.3390/life11111281 |
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author | Udriștoiu, Anca Loredana Ghenea, Alice Elena Udriștoiu, Ștefan Neaga, Manuela Zlatian, Ovidiu Mircea Vasile, Corina Maria Popescu, Mihaela Țieranu, Eugen Nicolae Salan, Alex-Ioan Turcu, Adina Andreea Nicolosu, Dragos Calina, Daniela Cioboata, Ramona |
author_facet | Udriștoiu, Anca Loredana Ghenea, Alice Elena Udriștoiu, Ștefan Neaga, Manuela Zlatian, Ovidiu Mircea Vasile, Corina Maria Popescu, Mihaela Țieranu, Eugen Nicolae Salan, Alex-Ioan Turcu, Adina Andreea Nicolosu, Dragos Calina, Daniela Cioboata, Ramona |
author_sort | Udriștoiu, Anca Loredana |
collection | PubMed |
description | (1) Background: The new SARS-COV-2 pandemic overwhelmed intensive care units, clinicians, and radiologists, so the development of methods to forecast the diagnosis’ severity became a necessity and a helpful tool. (2) Methods: In this paper, we proposed an artificial intelligence-based multimodal approach to forecast the future diagnosis’ severity of patients with laboratory-confirmed cases of SARS-CoV-2 infection. At hospital admission, we collected 46 clinical and biological variables with chest X-ray scans from 475 COVID-19 positively tested patients. An ensemble of machine learning algorithms (AI-Score) was developed to predict the future severity score as mild, moderate, and severe for COVID-19-infected patients. Additionally, a deep learning module (CXR-Score) was developed to automatically classify the chest X-ray images and integrate them into AI-Score. (3) Results: The AI-Score predicted the COVID-19 diagnosis’ severity on the testing/control dataset (95 patients) with an average accuracy of 98.59%, average specificity of 98.97%, and average sensitivity of 97.93%. The CXR-Score module graded the severity of chest X-ray images with an average accuracy of 99.08% on the testing/control dataset (95 chest X-ray images). (4) Conclusions: Our study demonstrated that the deep learning methods based on the integration of clinical and biological data with chest X-ray images accurately predicted the COVID-19 severity score of positive-tested patients. |
format | Online Article Text |
id | pubmed-8617902 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86179022021-11-27 COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis Udriștoiu, Anca Loredana Ghenea, Alice Elena Udriștoiu, Ștefan Neaga, Manuela Zlatian, Ovidiu Mircea Vasile, Corina Maria Popescu, Mihaela Țieranu, Eugen Nicolae Salan, Alex-Ioan Turcu, Adina Andreea Nicolosu, Dragos Calina, Daniela Cioboata, Ramona Life (Basel) Article (1) Background: The new SARS-COV-2 pandemic overwhelmed intensive care units, clinicians, and radiologists, so the development of methods to forecast the diagnosis’ severity became a necessity and a helpful tool. (2) Methods: In this paper, we proposed an artificial intelligence-based multimodal approach to forecast the future diagnosis’ severity of patients with laboratory-confirmed cases of SARS-CoV-2 infection. At hospital admission, we collected 46 clinical and biological variables with chest X-ray scans from 475 COVID-19 positively tested patients. An ensemble of machine learning algorithms (AI-Score) was developed to predict the future severity score as mild, moderate, and severe for COVID-19-infected patients. Additionally, a deep learning module (CXR-Score) was developed to automatically classify the chest X-ray images and integrate them into AI-Score. (3) Results: The AI-Score predicted the COVID-19 diagnosis’ severity on the testing/control dataset (95 patients) with an average accuracy of 98.59%, average specificity of 98.97%, and average sensitivity of 97.93%. The CXR-Score module graded the severity of chest X-ray images with an average accuracy of 99.08% on the testing/control dataset (95 chest X-ray images). (4) Conclusions: Our study demonstrated that the deep learning methods based on the integration of clinical and biological data with chest X-ray images accurately predicted the COVID-19 severity score of positive-tested patients. MDPI 2021-11-22 /pmc/articles/PMC8617902/ /pubmed/34833156 http://dx.doi.org/10.3390/life11111281 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 Udriștoiu, Anca Loredana Ghenea, Alice Elena Udriștoiu, Ștefan Neaga, Manuela Zlatian, Ovidiu Mircea Vasile, Corina Maria Popescu, Mihaela Țieranu, Eugen Nicolae Salan, Alex-Ioan Turcu, Adina Andreea Nicolosu, Dragos Calina, Daniela Cioboata, Ramona COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis |
title | COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis |
title_full | COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis |
title_fullStr | COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis |
title_full_unstemmed | COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis |
title_short | COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis |
title_sort | covid-19 and artificial intelligence: an approach to forecast the severity of diagnosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8617902/ https://www.ncbi.nlm.nih.gov/pubmed/34833156 http://dx.doi.org/10.3390/life11111281 |
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