Statistical analysis of COVID-19 infection severity in lung lobes from chest CT
Detection of the COVID 19 virus is possible through the reverse transcription-polymerase chain reaction (RT-PCR) kits and computed tomography (CT) images of the lungs. Diagnosis via CT images provides a faster diagnosis than the RT-PCR method does. In addition to low false-negative rate, CT is also...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914822000831 |
| _version_ | 1818009206784000000 |
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| author | Mehdi Yousefzadeh Mozhdeh Zolghadri Masoud Hasanpour Fatemeh Salimi Ramezan Jafari Mehran Vaziri Bozorg Sara Haseli Abolfazl Mahmoudi Aqeel Abadi Shahrokh Naseri Mohammadreza Ay Mohammad-Reza Nazem-Zadeh |
| author_facet | Mehdi Yousefzadeh Mozhdeh Zolghadri Masoud Hasanpour Fatemeh Salimi Ramezan Jafari Mehran Vaziri Bozorg Sara Haseli Abolfazl Mahmoudi Aqeel Abadi Shahrokh Naseri Mohammadreza Ay Mohammad-Reza Nazem-Zadeh |
| author_sort | Mehdi Yousefzadeh |
| collection | DOAJ |
| description | Detection of the COVID 19 virus is possible through the reverse transcription-polymerase chain reaction (RT-PCR) kits and computed tomography (CT) images of the lungs. Diagnosis via CT images provides a faster diagnosis than the RT-PCR method does. In addition to low false-negative rate, CT is also used for prognosis in determining the severity of the disease and the proposed treatment method. In this study, we estimated a probability density function (PDF) to examine the infections caused by the virus. We collected 232 chest CT of suspected patients and had them labeled by two radiologists in 6 classes, including a healthy class and 5 classes of different infection severity. To segment the lung lobes, we used a pre-trained U-Net model with an average Dice similarity coefficient (DSC) greater than 0.96. First, we extracted the PDF to grade the infection of each lobe and selected five specific thresholds as feature vectors. We then assigned this feature vector to a support vector machine (SVM) model and made the final prediction of the infection severity. Using the T-Test statistics, we calculated the p-value at different pixel thresholds and reported the significant differences in the pixel values. In most cases, the p-value was less than 0.05. Our developed model was developed on roughly labeled data without any manual segmentation, which estimated lung infection involvements with the area under the curve (AUC) in the range of [0.64, 0.87]. The introduced model can be used to generate a systematic automated report for individual patients infected by COVID-19. |
| first_indexed | 2024-04-14T05:39:06Z |
| format | Article |
| id | doaj.art-32f363cbab41489eade9540c5083ebd1 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-04-14T05:39:06Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-32f363cbab41489eade9540c5083ebd12022-12-22T02:09:30ZengElsevierInformatics in Medicine Unlocked2352-91482022-01-0130100935Statistical analysis of COVID-19 infection severity in lung lobes from chest CTMehdi Yousefzadeh0Mozhdeh Zolghadri1Masoud Hasanpour2Fatemeh Salimi3Ramezan Jafari4Mehran Vaziri Bozorg5Sara Haseli6Abolfazl Mahmoudi Aqeel Abadi7Shahrokh Naseri8Mohammadreza Ay9Mohammad-Reza Nazem-Zadeh10Department of Physics, Shahid Beheshti University, Tehran, Iran; School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, IranDepartment of Medical Physics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, IranResearch Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, IranMedical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences (TUMS), Tehran, IranDepartment of Radiology and Health Research Center Baqiyatallah University of Medical Sciences, Tehran, IranDepartment of Radiology, Kasra Hospital, Tehran, IranChronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences and Health Services, Tehran, IranMedical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences (TUMS), Tehran, IranDepartment of Medical Physics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, IranResearch Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran; Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences (TUMS), Tehran, IranResearch Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran; Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences (TUMS), Tehran, Iran; Corresponding author. Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran.Detection of the COVID 19 virus is possible through the reverse transcription-polymerase chain reaction (RT-PCR) kits and computed tomography (CT) images of the lungs. Diagnosis via CT images provides a faster diagnosis than the RT-PCR method does. In addition to low false-negative rate, CT is also used for prognosis in determining the severity of the disease and the proposed treatment method. In this study, we estimated a probability density function (PDF) to examine the infections caused by the virus. We collected 232 chest CT of suspected patients and had them labeled by two radiologists in 6 classes, including a healthy class and 5 classes of different infection severity. To segment the lung lobes, we used a pre-trained U-Net model with an average Dice similarity coefficient (DSC) greater than 0.96. First, we extracted the PDF to grade the infection of each lobe and selected five specific thresholds as feature vectors. We then assigned this feature vector to a support vector machine (SVM) model and made the final prediction of the infection severity. Using the T-Test statistics, we calculated the p-value at different pixel thresholds and reported the significant differences in the pixel values. In most cases, the p-value was less than 0.05. Our developed model was developed on roughly labeled data without any manual segmentation, which estimated lung infection involvements with the area under the curve (AUC) in the range of [0.64, 0.87]. The introduced model can be used to generate a systematic automated report for individual patients infected by COVID-19.http://www.sciencedirect.com/science/article/pii/S2352914822000831Statistical analysisCOVID-19CT scanLung lobes segmentationProbability density functionSupport vector machine |
| spellingShingle | Mehdi Yousefzadeh Mozhdeh Zolghadri Masoud Hasanpour Fatemeh Salimi Ramezan Jafari Mehran Vaziri Bozorg Sara Haseli Abolfazl Mahmoudi Aqeel Abadi Shahrokh Naseri Mohammadreza Ay Mohammad-Reza Nazem-Zadeh Statistical analysis of COVID-19 infection severity in lung lobes from chest CT Informatics in Medicine Unlocked Statistical analysis COVID-19 CT scan Lung lobes segmentation Probability density function Support vector machine |
| title | Statistical analysis of COVID-19 infection severity in lung lobes from chest CT |
| title_full | Statistical analysis of COVID-19 infection severity in lung lobes from chest CT |
| title_fullStr | Statistical analysis of COVID-19 infection severity in lung lobes from chest CT |
| title_full_unstemmed | Statistical analysis of COVID-19 infection severity in lung lobes from chest CT |
| title_short | Statistical analysis of COVID-19 infection severity in lung lobes from chest CT |
| title_sort | statistical analysis of covid 19 infection severity in lung lobes from chest ct |
| topic | Statistical analysis COVID-19 CT scan Lung lobes segmentation Probability density function Support vector machine |
| url | http://www.sciencedirect.com/science/article/pii/S2352914822000831 |
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