Background:  Epidemiological surveillance is the cornerstone for the prevention and control of any pandemic. The purpose of the study was using clinical and radiological data of COVID-19 positive patients to describe the clinical features, risk factors, grading of severity on the basis of chest X-ray and their survival outcome. Methods: A retrospective observational study comprising 9100 COVID-19 positive patients was done in the Department of Radiodiagnosis, M.G.M. Medical College and M.Y. Hospital, Indore. Patients' data including demography, clinical findings, vaccination status and imaging findings was collected and assessed in between March 2020 and March 2022. In the descriptive statistical analysis, continuous variables were noted in terms of the mean and standard deviation and nominal variables were noted in terms of percentage.Results: In our study, there were 9100 patients proven with positive COVID-19 disease had abnormal CXRs were detected in 7553 of 9100 patients (83%). In our study, B/L lung involvement (69%) was found to be more with lower lung zone predominance (86.5%) and peripheral predominance opacities (83.7%). The most common finding of chest X-ray pattern is consolidation (65.7%), followed by ground glass opacity (29.0%). Most of the vaccinated patients were found to be in mild category and majority of mild cases didn’t require oxygen support. The chi-square statistic is 79.3372. The result is significant at p<0.05Conclusions: The chest x-ray severity scoring (CXR-SS) system used in this study is a valuable method of disease prognostication in COVID-19. In our study, we found a significant reverse relationship between chest X-ray severity score and oxygen saturation, which has great clinical importance.

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Purpose COVID-19 is not going anywhere and is slowly becoming a part of our life. The World Health Organization declared it a pandemic in 2020, and it has affected all of us in many ways. Several deep learning techniques have been developed to detect COVID-19 from Chest X-Ray images. COVID-19 infection severity scoring can aid in establishing the optimum course of treatment and care for a positive patient, as all COVID-19 positive patients do not require special medical attention. Still, very few works are reported to estimate the severity of the disease from the Chest X-Ray images. The unavailability of the large-scale dataset might be a reason. Methods We aim to propose CoVSeverity-Net, a deep learning-based architecture for predicting the severity of COVID-19 from Chest X-ray images. CoVSeverity-Net is trained on a public COVID-19 dataset, curated by experienced radiologists for severity estimation. For that, a large publicly available dataset is collected and divided into three levels of severity, namely Mild, Moderate, and Severe. Results An accuracy of 85.71% is reported. Conducting 5-fold cross-validation, we have obtained an accuracy of 87.82 ± 6.25%. Similarly, conducting 10-fold cross-validation we obtained accuracy of 91.26 ± 3.42. The results were better when compared with other state-of-the-art architectures. Conclusion We strongly believe that this study has a high chance of reducing the workload of overworked front-line radiologists, speeding up patient diagnosis and treatment, and easing pandemic control. Future work would be to train a novel deep learning-based architecture on a larger dataset for severity estimation.