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...
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2021
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oai:doaj.org-article:780497f3d4a74b4eaaf0e6941506731a2021-11-25T18:11:49ZCOVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis10.3390/life111112812075-1729https://doaj.org/article/780497f3d4a74b4eaaf0e6941506731a2021-11-01T00:00:00Zhttps://www.mdpi.com/2075-1729/11/11/1281https://doaj.org/toc/2075-1729(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.Anca Loredana UdriștoiuAlice Elena GheneaȘtefan UdriștoiuManuela NeagaOvidiu Mircea ZlatianCorina Maria VasileMihaela PopescuEugen Nicolae ȚieranuAlex-Ioan SalanAdina Andreea TurcuDragos NicolosuDaniela CalinaRamona CioboataMDPI AGarticleCOVID-19artificial intelligencedeep learningScienceQENLife, Vol 11, Iss 1281, p 1281 (2021) |
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DOAJ |
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DOAJ |
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EN |
| topic |
COVID-19 artificial intelligence deep learning Science Q |
| spellingShingle |
COVID-19 artificial intelligence deep learning Science Q Anca Loredana Udriștoiu Alice Elena Ghenea Ștefan Udriștoiu Manuela Neaga Ovidiu Mircea Zlatian Corina Maria Vasile Mihaela Popescu Eugen Nicolae Țieranu Alex-Ioan Salan Adina Andreea Turcu Dragos Nicolosu Daniela Calina Ramona Cioboata COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis |
| 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 |
article |
| author |
Anca Loredana Udriștoiu Alice Elena Ghenea Ștefan Udriștoiu Manuela Neaga Ovidiu Mircea Zlatian Corina Maria Vasile Mihaela Popescu Eugen Nicolae Țieranu Alex-Ioan Salan Adina Andreea Turcu Dragos Nicolosu Daniela Calina Ramona Cioboata |
| author_facet |
Anca Loredana Udriștoiu Alice Elena Ghenea Ștefan Udriștoiu Manuela Neaga Ovidiu Mircea Zlatian Corina Maria Vasile Mihaela Popescu Eugen Nicolae Țieranu Alex-Ioan Salan Adina Andreea Turcu Dragos Nicolosu Daniela Calina Ramona Cioboata |
| author_sort |
Anca Loredana Udriștoiu |
| title |
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_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_sort |
covid-19 and artificial intelligence: an approach to forecast the severity of diagnosis |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/780497f3d4a74b4eaaf0e6941506731a |
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