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COVID-19 has resulted in a significant global impact on health, the economy, education, and daily life. The disease can range from mild to severe, with individuals over 65 or those with underlying medical conditions being more susceptible to severe illness. Early testing and isolation are vital due to the virus’s variable incubation period. Chest radiographs (CXR) have gained importance as a diagnostic tool due to their efficiency and reduced radiation exposure compared to CT scans. However, the sensitivity of CXR in detecting COVID-19 may be lower. This paper introduces a deep learning framework for accurate COVID-19 classification and severity prediction using CXR images. U-Net is used for lung segmentation, achieving a precision of 0.9924. Classification is performed using a Convulation-capsule network, with high true positive rates of 86% for COVID-19, 93% for pneumonia, and 85% for normal cases. Severity assessment employs ResNet50, VGG-16, and DenseNet201, with DenseNet201 showing superior accuracy. Empirical results, validated with 95% confidence intervals, confirm the framework’s reliability and robustness. This integration of advanced deep learning techniques with radiological imaging enhances early detection and severity assessment, improving patient management and resource allocation in clinical settings. [ABSTRACT FROM AUTHOR]

The chest X-ray (CXR) Brixia scoring system was developed exclusively for COVID-19 severity assessment. However, no association between the score and respiratory mechanics during mechanical ventilation has been examined. Our aim was to evaluate the association between the CXR Brixia score and respiratory mechanics on the first day of mechanical ventilation in critically ill COVID-19 patients. A total of 77 COVID-19 patients who underwent mechanical ventilation and CXR in the ICU setting were retrospectively included. The CXR Brixia scoring system was applied, and respiratory mechanics data were recorded on the first day of invasive mechanical ventilation. Median Simplified Acute Physiologic Score II (SAPSII) and Sequential Organ Failure Assessment (SOFA) scores were 40 (31–54) and 6 (4–8), respectively. The median Brixia score was 14 (11–16). The correlation between the Brixia score and static compliance or driving pressure was significant, at r = -0.38, p < 0.001 and r = 0.33, p = 0.003, respectively. Using multivariable linear regression, the model with the B zone was significantly better associated with static compliance (F = 11.5, R2 = 0.14, p = 0.001) and driving pressure (F = 11.3, R2 = 0.13, p = 0.001). In logistic regression analysis, the Brixia score (OR 1.24; 95% CI 1.07, 1.45; p = 0.005), B zone (OR 2.60; 95% CI 1.30, 5.20; p = 0.007), C zone (OR 2.50; 95% CI 1.23, 5.11; p = 0.012), A zone (OR 2.01; 95% CI 1.16, 3.44; p = 0.012), and D zone (OR 1.84; 95% CI 1.07, 3.17; p = 0.027) significantly predicted a driving pressure > 14 cmH2O. There is a relationship between changes in Brixia-scored chest X-ray images and compliance and driving pressure on the first day of invasive mechanical ventilation. We identified some CXR areas using the Brixia score, and evaluation of the Brixia score may provide additional information for predicting respiratory mechanics. [ABSTRACT FROM AUTHOR]

Objectives: Identifying early markers of poor prognosis of coronavirus disease 2019 (COVID-19) is mandatory. Our purpose is to analyze by chest radiography if rapid worsening of COVID-19 pneumonia in the initial days has predictive value for ventilatory support (VS) need. Methods: Ambispective observational ethically approved study in COVID-19 pneumonia inpatients, validated in a second outpatient sample. Brixia score (BS) was applied to the first and second chest radiography required for suspected COVID-19 pneumonia to determine the predictive capacity of BS worsening for VS need. Intraclass correlation coefficient (ICC) was previously analyzed among three radiologists. Sensitivity, specificity, likelihood ratios, AUC, and odds ratio were calculated using ROC curves and binary logistic regression analysis. A value of p <.05 was considered statistically significant. Results: A total of 120 inpatients (55 ± 14 years, 68 men) and 112 outpatients (56 ± 13 years, 61 men) were recruited. The average ICC of the BS was between 0.812 (95% confidence interval 0.745–0.878) and 0.906 (95% confidence interval 0.844–0.940). According to the multivariate analysis, a BS worsening per day > 1.3 points within 10 days of the onset of symptoms doubles the risk for requiring VS in inpatients and 5 times in outpatients (p <.001). The findings from the second chest radiography were always better predictors of VS requirement than those from the first one. Conclusion: The early radiological worsening of SARS-CoV-2 pneumonia after symptoms onset is a determining factor of the final prognosis. In elderly patients with some comorbidity and pneumonia, a 48–72-h follow-up radiograph is recommended. Key Points: • An early worsening on chest X-ray in patients with SARS-CoV-2 pneumonia is highly predictive of the need for ventilatory support. • This radiological worsening rate can be easily assessed by comparing the first and the second chest X-ray. • In elderly patients with some comorbidity and SARS-CoV-2 pneumonia, close early radiological follow-up is recommended. [ABSTRACT FROM AUTHOR]