Khairy, A., ElSerafi, A., Gad, A., Khattab, Y. (2023). Correlation Between Radiologic Severity Assessment by Lung CT and Clinical Scoring Using Pneumonia Severity Index In COVID-19 Patients. Suez Canal University Medical Journal, 26(5), 0-0. doi: 10.21608/scumj.2023.306936
Aliaa A. Khairy; Ahmed F. ElSerafi; Azza A. Gad; Yara H. Khattab. "Correlation Between Radiologic Severity Assessment by Lung CT and Clinical Scoring Using Pneumonia Severity Index In COVID-19 Patients". Suez Canal University Medical Journal, 26, 5, 2023, 0-0. doi: 10.21608/scumj.2023.306936
Khairy, A., ElSerafi, A., Gad, A., Khattab, Y. (2023). 'Correlation Between Radiologic Severity Assessment by Lung CT and Clinical Scoring Using Pneumonia Severity Index In COVID-19 Patients', Suez Canal University Medical Journal, 26(5), pp. 0-0. doi: 10.21608/scumj.2023.306936
Khairy, A., ElSerafi, A., Gad, A., Khattab, Y. Correlation Between Radiologic Severity Assessment by Lung CT and Clinical Scoring Using Pneumonia Severity Index In COVID-19 Patients. Suez Canal University Medical Journal, 2023; 26(5): 0-0. doi: 10.21608/scumj.2023.306936
Correlation Between Radiologic Severity Assessment by Lung CT and Clinical Scoring Using Pneumonia Severity Index In COVID-19 Patients
1Department of Diagnostic Radiology, Ismailia Fever Hospital, Egypt
2Department of Diagnostic Radiology, Faculty of Medicine, Suez Canal University, Egypt
Abstract
Background: The coronavirus disease 2019 (COVID-19) Reporting and Data System (CO-RADS) provides very good performance and gives radiologists a good probability index for COVID-19 prediction. But we need other classification systems that will help us to detect the prognosis and severity of the disease. Aim:To explore the relationship between the HRCT chest findings, severity, and clinical scoring of COVID-19 patients at the time of presentation. Subjects and Methods: A descriptive cross-sectional study was conducted on 95 Patients at Suez Canal University who presented with community-acquired Pneumonia and scored clinically with Pneumonia Severity Index, all patients were assessed radiologically by HRCT and scored their CT lesions. Results: For CT features, GGO (100%) and consolidation (53.6%) are the most common. There were significant correlations between the degree of CT severity and the clinical severity (P value = 0.01) with sensitivity of 60.4% and specificity of 69% at the cut point of 11.5. There was a correlation between the observer and the artificial intelligence in radiological evaluation (P value < 0.001). Conclusions:Computed tomography plays an important role in the diagnosis and disease severity evaluation of COVID-19, Artificial intelligence can be used to help overworked doctors during a pandemic.
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