On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis
Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-02-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/11/2/293 |
| _version_ | 1797396740931846144 |
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| author | Antonella Santone Maria Paola Belfiore Francesco Mercaldo Giulia Varriano Luca Brunese |
| author_facet | Antonella Santone Maria Paola Belfiore Francesco Mercaldo Giulia Varriano Luca Brunese |
| author_sort | Antonella Santone |
| collection | DOAJ |
| description | Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection. |
| first_indexed | 2024-03-09T00:55:58Z |
| format | Article |
| id | doaj.art-b5d0d3279d974a2194f2106fdc3b6367 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T00:55:58Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-b5d0d3279d974a2194f2106fdc3b63672023-12-11T16:55:16ZengMDPI AGDiagnostics2075-44182021-02-0111229310.3390/diagnostics11020293On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus DiagnosisAntonella Santone0Maria Paola Belfiore1Francesco Mercaldo2Giulia Varriano3Luca Brunese4Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Napoli, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyConsidering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection.https://www.mdpi.com/2075-4418/11/2/293radiologyradiomicsformal methodsartificial intelligenceCoronavirusCOVID-19 |
| spellingShingle | Antonella Santone Maria Paola Belfiore Francesco Mercaldo Giulia Varriano Luca Brunese On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis Diagnostics radiology radiomics formal methods artificial intelligence Coronavirus COVID-19 |
| title | On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis |
| title_full | On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis |
| title_fullStr | On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis |
| title_full_unstemmed | On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis |
| title_short | On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis |
| title_sort | on the adoption of radiomics and formal methods for covid 19 coronavirus diagnosis |
| topic | radiology radiomics formal methods artificial intelligence Coronavirus COVID-19 |
| url | https://www.mdpi.com/2075-4418/11/2/293 |
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