COVID-19 severity detection using chest X-ray segmentation and deep learning.

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.

The chest X-ray score baseline in predicting continuous oxygen therapy failure in low-risk aged patients after thoracic surgery.

Background: Radiographic severity assessment can be instrumental in diagnosing postoperative pulmonary complications (PPCs) and guiding oxygen therapy. The radiographic assessment of lung edema (RALE) and Brixia scores correlate with disease severity, but research on low-risk elderly patients is lacking. This study aimed to assess the efficacy of two chest X-ray scores in predicting continuous oxygen therapy (COT) treatment failure in patients over 70 years of age after thoracic surgery. Methods: From January 2019 to December 2021, we searched for patients aged 70 years and above who underwent thoracic surgery and received COT treatment, with a focus on those at low risk of respiratory complications. Bedside chest X-rays, RALE, Brixia scores, and patient data were collected. Univariate, multivariate analyses, and 1:2 matching identified risk factors. Receiver operating characteristic (ROC) curves determined score sensitivity, specificity, and predictive values. Results: Among the 242 patients surviving to discharge, 19 (7.9%) patients experienced COT failure. COT failure correlated with esophageal cancer surgeries, thoracotomies (36.8% vs. 9%, P=0.003; 26.3% vs. 9.4%, P=0.004), and longer operation time (3.4 vs. 2.8 h, P=0.003). Surgical approach and RALE score were independent risk factors. The prediction model had an area under the curve (AUC) of 0.839 [95% confidence interval (CI), 0.740-0.938]. Brixia and RALE scores predicted COT failure with AUCs of 0.764 (95% CI, 0.650-0.878) with a cut-off value of 6.027 and 0.710 (95% CI, 0.588-0.832) with a cut-off value of 17.134, respectively, after 1:2 matching. Conclusions: The RALE score predict the risk of COT failure in elderly, low-risk thoracic patients better than the Brixia score. This simple, cheap, and noninvasive method helps evaluate postoperative lung damage, monitor treatment response, and provide early warning for oxygen therapy escalation. Further studies are required to confirm the validity and applicability of this model in different settings and populations.

Comparison of the Discrimination Performance of AI Scoring and the Brixia Score in Predicting COVID-19 Severity on Chest X-Ray Imaging: Diagnostic Accuracy Study.

BACKGROUND: The artificial intelligence (AI) analysis of chest x-rays can increase the precision of binary COVID-19 diagnosis. However, it is unknown if AI-based chest x-rays can predict who will develop severe COVID-19, especially in low- and middle-income countries. OBJECTIVE: The study aims to compare the performance of human radiologist Brixia scores versus 2 AI scoring systems in predicting the severity of COVID-19 pneumonia. METHODS: We performed a cross-sectional study of 300 patients suspected with and with confirmed COVID-19 infection in Jakarta, Indonesia. A total of 2 AI scores were generated using CAD4COVID x-ray software. RESULTS: The AI probability score had slightly lower discrimination (area under the curve [AUC] 0.787, 95% CI 0.722-0.852). The AI score for the affected lung area (AUC 0.857, 95% CI 0.809-0.905) was almost as good as the human Brixia score (AUC 0.863, 95% CI 0.818-0.908). CONCLUSIONS: The AI score for the affected lung area and the human radiologist Brixia score had similar and good discrimination performance in predicting COVID-19 severity. Our study demonstrated that using AI-based diagnostic tools is possible, even in low-resource settings. However, before it is widely adopted in daily practice, more studies with a larger scale and that are prospective in nature are needed to confirm our findings.

Perioperative Factors and Radiographic Severity Scores for Predicting the Duration of Mechanical Ventilation After Arterial Switch Surgery.

OBJECTIVES: Cardiac surgery on cardiopulmonary bypass (CPB) during the neonatal period can cause perioperative organ injuries. The primary aim of this study was to determine the incidence and risk factors associated with postoperative mechanical ventilation duration and acute lung injury after the arterial switch operation (ASO). The secondary aim was to examine the utility of the Brixia score for characterizing postoperative acute lung injury (ALI). DESIGN: A retrospective study. SETTING: A single-center university hospital. PARTICIPANTS: A total of 93 neonates with transposition of great arteries with intact ventricular septum (dTGA IVS) underwent ASO. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: From January 2015 to December 2022, 93 neonates with dTGA IVS were included in the study. The cohort had a median age of 4.0 (3.0-5.0) days and a mean weight of 3.3 ± 0.5 kg. About 63% of patients had ≥48 hours of postoperative mechanical ventilation after ASO. Risk factors included prematurity, post-CPB transfusion of salvaged red cells, platelets and cryoprecipitate, and postoperative fluid balance by univariate analysis. The larger transfused platelet volume was associated with the risk of ALI by multivariate analysis. The median baseline Brixia scores were 11.0 (9.0-12.0) and increased significantly in the postoperative day 1 in patients who developed moderate ALI 24 hours after admission to the intensive care unit (15.0 [13.0-16.0] v 12.0 [10.0-14.0], p = 0.046). CONCLUSIONS: Arterial switch operation results in a high incidence of ≥48-hour postoperative mechanical ventilation. Blood component transfusion is a potentially modifiable risk factor. The Brixia scores also may be used to characterize postoperative acute lung injury.

Early association between respiratory mechanics and radiological changes in mechanically ventilated critically ill patients with COVID-19.

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.

Chest X-ray at Emergency Admission and Potential Association with Barotrauma in Mechanically Ventilated Patients: Experience from the Italian Core of the First Pandemic Peak.

Barotrauma occurs in a significant number of patients with COVID-19 interstitial pneumonia undergoing mechanical ventilation. The aim of the current study was to investigate whether the Brixia score (BS) calculated on chest-X-rays acquired at the Emergency Room was associated with barotrauma. We retrospectively evaluated 117 SARS-CoV-2 patients presented to the Emergency Department (ED) and then admitted to the intensive care unit (ICU) for mechanical ventilation between February and April 2020. Subjects were divided into two groups according to the occurrence of barotrauma during their hospitalization. CXRs performed at ED admittance were assessed using the Brixia score. Distribution of barotrauma (pneumomediastinum, pneumothorax, subcutaneous emphysema) was identified in chest CT scans. Thirty-eight subjects (32.5%) developed barotrauma (25 pneumomediastinum, 24 pneumothorax, 24 subcutaneous emphysema). In the barotrauma group we observed higher Brixia score values compared to the non-barotrauma group (mean value 12.18 vs. 9.28), and logistic regression analysis confirmed that Brixia score is associated with the risk of barotrauma. In this work, we also evaluated the relationship between barotrauma and clinical and ventilatory parameters: SOFA score calculated at ICU admittance and number of days of non-invasive ventilation (NIV) prior to intubation emerged as other potential predictors of barotrauma.

Clinical variables associated with late-onset thrombotic and cardiovascular events, after SARS-CoV-2 infection, in a cohort of patients from the first epidemic wave: an 18-month analysis on the "Surviving-COVID" cohort from Bergamo, Italy.

Importance: Population studies have recorded an increased, unexplained risk of post-acute cardiovascular and thrombotic events, up to 1 year after acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Objectives: To search for clinical variables and biomarkers associated with late post-acute thrombotic and cardiovascular events after SARS-CoV-2 infection. Design: Retrospective cohort study. Setting: Third-level referral hospital in Bergamo (Italy). Participants: Analysis of an existing database of adult patients, who received care for SARS-CoV-2 infection at our institution between 20 February and 30 September 2020, followed up on a single date ("entry date") at 3-6 months. Exposure: Initial infection by SARS-CoV-2. Main outcomes and measures: Primary outcome: occurrence, in the 18 months after entry date, of a composite endpoint, defined by the International Classification of Diseases-9th edition (ICD-9) codes for at least one of: cerebral/cardiac ischemia, venous/arterial thrombosis (any site), pulmonary embolism, cardiac arrhythmia, heart failure. Measures (as recorded on entry date): history of initial infection, symptoms, current medications, pulmonary function test, blood tests results, and semi-quantitative radiographic lung damage (BRIXIA score). Individual clinical data were matched to hospitalizations, voluntary vaccination against SARS-CoV-2 (according to regulations and product availability), and documented reinfections in the following 18 months, as recorded in the provincial Health Authority database. A multivariable Cox proportional hazard model (including vaccine doses as a time-dependent variable) was fitted, adjusting for potential confounders. We report associations as hazard ratios (HR) and 95% confidence intervals (CI). Results: Among 1,515 patients (948 men, 62.6%, median age 59; interquartile range: 50-69), we identified 84 endpoint events, occurring to 75 patients (5%): 30 arterial thromboses, 11 venous thromboses, 28 arrhythmic and 24 heart failure events. From a multivariable Cox model, we found the following significant associations with the outcome: previous occurrence of any outcome event, in the 18 months before infection (HR: 2.38; 95% CI: 1.23-4.62); BRIXIA score ≥ 3 (HR: 2.43; 95% CI: 1.30-4.55); neutrophils-to-lymphocytes ratio ≥ 3.3 (HR: 2.60; 95% CI: 1.43-4.72), and estimated glomerular filtration rate < 45 ml/min/1.73 m2 (HR: 3.84; 95% CI: 1.49-9.91). Conclusions and relevance: We identified four clinical variables, associated with the occurrence of post-acute thrombotic and cardiovascular events, after SARS-CoV-2 infection. Further research is needed, to confirm these results.

Radiographic patterns on Chest X-ray as a supporting imaging tool in triaging of suspected Corona Virus Disease (COVID) patients.

Objective: To evaluate the radiographic patterns on Chest X-Ray (CXR) in accordance with Modified Brixia Scoring as supporting imaging tool in triaging of Corona Virus Disease (COVID-19) pneumonia. Methods: In this cross-sectional study, chest radiographs of suspected COVID patients at emergency triage, Lady Reading Hospital (LRH) from April 18th to July 22nd 2020 were evaluated for patterns of COVID pneumonia and scored in accordance with modified Brixia score. Each zone was categorized as score of "one" for interstitial pattern, "two" for mixed interstitial /alveolar pattern and "three" for alveolar pattern. Radiographic patterns consistent with COVID pneumonia or patients having strong clinical suspicion were advised Polymerase Chain Reaction (PCR) tests. Results: Total of 2,225 individuals were screened for patterns of COVID-19 pneumonia on chest radiograph. Out of these 1465(65.8%) had normal chest radiograph and 760(34.2%) had abnormal findings. Out of the total, 648 suspected COVID patients were selected for PCR. The radiographic patterns ranged from mixed interstitial/alveolar pattern in 261(40.3%) patients, alveolar pattern in 231(35.6%), interstitial pattern in 87(13.4%), pleural effusion in 12(1.9%), other findings in 5(0.8%) while 52(8%) suspected Covid patients had normal radiographs. The PCR was positive in 326(50.3%), negative in 100(15.4%) and inconclusive in 60(9.3%) while 162(25%) were lost to follow up. Amongst the 52 suspected Covid patients having normal chest radiographs, 10 were positive on PCR, 21 negative, seven suspected and two inconclusive, while 12 were lost to follow up. Conclusion: Chest radiograph is used for triaging of suspected COVID pneumonia patients in emergency settings. It assesses the severity of disease according to modified Brixia scoring for treatment plan.

Brixia and qSOFA Scores, Coagulation Factors and Blood Values in Spring versus Autumn 2021 Infection in Pregnant Critical COVID-19 Patients: A Preliminary Study.

(1) Background: From the recent variants of concern of the SARS-CoV-2 virus, in which the delta variant generated more negative outcomes than the alpha, we hypothesized that lung involvement, clinical condition deterioration and blood alterations were also more severe in autumn infection, when the delta variant dominated (compared with spring infections, when the alpha variant dominated), in severely infected pregnant patients. (2) Methods: In a prospective study, all pregnant patients admitted to the ICU of the Elena Doamna Obstetrics and Gynecology Hospital with a critical form of COVID-19 infection-spring group (n = 11) and autumn group (n = 7)-between 1 January 2021 and 1 December 2021 were included. Brixia scores were calculated for every patient: A score, upon admittance; H score, the highest score throughout hospitalization; and E score, at the end of hospitalization. For each day of Brixia A, H or E score, the qSOFA (quick sepsis-related organ failure assessment) score was calculated, and the blood values were also considered. (3) Results: Brixia E score, C-reactive protein, GGT and LDH were much higher, while neutrophil count was much lower in autumn compared with spring critical-form pregnant patients. (4) Conclusions: the autumn infection generated more dramatic alterations than the spring infection in pregnant patients with critical forms of COVID-19. Larger studies with more numerous participants are required to confirm these results.

Chest X-Ray pattern and lung severity score in COVID-19 patients with diabetes mellitus: A cross sectional study.

Diabetes mellitus is a chronic hyperglycemic condition that can affect the body's immune response to SARS-CoV-2 This study aimed to determine the relationship between diabetes mellitus and lung severity in COVID-19 patients. METHODS: A cross-sectional design was conducted at Hasan Sadikin General Hospital during the January-May 2021 period. Data were based on medical records of patients aged 18 years and over with COVID-19. The chi-square test was performed to assess the relationship between diabetes mellitus and lung severity based on the BRIXIA score. RESULTS: This study included 538 subjects, mostly aged <60 years (71.9%) and female (60.2%). A total of 125 subjects had abnormal blood glucose levels with an average HbA1c of 9.00 ± 1.77% in patients with diabetes mellitus and a median HbA1c of 5.85% (4.5-6.4%) in patients with reactive hyperglycemia. Lung abnormalities were found in 357 subjects (66.4%). The results of the BRIXIA score to assess lung severity found as many as 77 subjects (14.3%) had a score of 11-18 with 14 people with diabetes mellitus, five people with reactive hyperglycemia. In the population aged ≥60 years, as many as 32 people had a score of 11-18 with three people with diabetes mellitus, two with reactive hyperglycemia and 27 with normal blood glucose. A significant relationship was found between diabetes mellitus and lung severity (p = 0.024). CONCLUSION: There is a significant relationship between diabetes mellitus and lung severity in COVID-19 patients aged ≥60 years.

Correlation of Chest X-Ray Scores in SARS-CoV-2 Patients With the Clinical Severity Classification and the Quick COVID-19 Severity Index.

Objectives This study aimed to assess the role of chest X-ray (CXR) scoring methods and their correlations with the clinical severity categories and the Quick COVID-19 Severity Index (qCSI). Methods We conducted a retrospective study of 159 COVID-19 patients who were diagnosed and treated at the University Medical Center between July and September 2021. Chest X-ray findings were evaluated, and severity scores were calculated using the modified CXR (mCXR), Radiographic Assessment of Lung Edema (RALE), and Brixia scoring systems. The three scores were then compared to the clinical severity categories and the qCSI using Spearman's correlation coefficient. Results Overall, 159 patients (63 males and 96 females) (mean age: 58.3 ± 15.7 years) were included. The correlation coefficients between the mCXR score and the Brixia and RALE scores were 0.9438 and 0.9450, respectively. The correlation coefficient between the RALE and Brixia scores was marginally higher, at 0.9625. The correlation coefficients between the qCSI and the Brixia, RALE, and mCXR scores were 0.7298, 0.7408, and 0.7156, respectively. The significant difference in the mean values of the three CXR scores between asymptomatic, mild, moderate, severe, and critical groups was also noted. Conclusions There were strong correlations between the three CXR scores and the clinical severity classification and the qCSI.

Chest X-ray Severity Score as a Putative Predictor of Clinical Outcome in Hospitalized Patients: An Experience From a Vietnamese COVID-19 Field Hospital.

Background Through the coronavirus disease 2019 (COVID-19) pandemic, portable radiography was particularly useful for assessing and monitoring the COVID-19 disease in Vietnamese field hospitals. It provides a convenient and precise picture of the progression of the disease. The purpose of this study was to evaluate the predictive value of chest radiograph reporting systems (Brixia and total severity score (TSS)) and the National Early Warning Score (NEWS) clinical score in a group of hospitalized patients with COVID-19. Methods This retrospective cohort study used routinely collected clinical data from polymerase chain reaction (PCR)-positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients admitted to Field Hospital District 8, Ho Chi Minh City, Vietnam, from August 2021 to September 2021. The initial chest radiographs were scored based on the TSS and Brixia scoring systems to quantify the extent of lung involvement. After the chest radiograph score was reported, two residents calculated the rate of all-cause in-hospital mortality with the consultation of expert radiologists. In this study, NEWS2 scores on hospital admission were calculated. The gradient boosting machines (GBMs) and Shapley additive exPlanations (SHAP) were applied to access the important variable and improve the accuracy of mortality prediction. The adjusted odds ratio for predictor was presented by univariate analysis and multivariate analysis. Results The chest X-rays (CXRs) at the admission of 273 patients (mean age 59 years +/-16, 42.1% were male) were scored. In the univariate analysis, age, vaccination status, previous disease, NEWS2, a saturation of peripheral oxygen (Sp02), the Brixia and TSS scores were significant predictors of mortality (p-value < 0.05). In multivariate analysis, there were statistically significant differences in mortality between age, Sp02, Brixia score, and patients with previous diseases were independent predictors of mortality and hospitalization. A gradient boosting machine was performed in the train data set, which showed that the best hyperparameters for predicting the mortality of patients are the Brixia score (exclude TSS score). In the top five predictors, an increase in Brixia, age, and BMI increased the logarithmic number of probability clarifying as death status. Although the TSS and Brixia scores evaluated chest imaging, the TSS score was not essential as the Brixia score (rank 6/11). It was clear that the BMI and NEWS2 score was positively correlated with the Brixia score, and age did not affect this correlation. Meanwhile, we did not find any trend between the TSS score versus BMI and NEWS2. Conclusion When integrated with the BMI and NEWS2 clinical classification systems, the severity score of COVID-19 chest radiographs, particularly the Brixia score, was an excellent predictor of all-cause in-hospital mortality.

Chest radiography evaluation in patients admitted with confirmed COVID-19 infection, in a resource limited South African isolation hospital.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the subsequent global outbreak (coronavirus disease 2019 [COVID-19]) was declared a public health emergency in January 2020. Recent radiologic literature regarding COVID-19 has primarily focused on Computed Tomography (CT) chest findings, with chest radiography lacking in comparison. OBJECTIVES: To describe the demographic profile of adult patients with COVID-19 pneumonia requiring hospital admission. To describe and quantify the imaging spectrum on chest radiography using a severity index, and to correlate the severity of disease with prognosis. METHOD: Retrospective review of chest radiographs and laboratory records in patients admitted to a South African tertiary hospital with confirmed COVID-19 infection. The chest X-rays were systematically reviewed for several radiographic features, which were then quantified using the Brixia scoring system, and correlated to the patient's outcome. RESULTS: A total of 175 patients (mean age: 53.34 years) admitted with COVID-19 were included. Ground glass opacification (98.9%), consolidation (86.3%), and pleural effusion (29.1%) was commonly found. Involvement of bilateral lung fields (96.6%) with no zonal predominance (61.7%), was most prevalent. Correlation between the Brixia score and outcome was found between severe disease and death (odds ratio [OR]: 12.86; 95% confidence interval [CI]: 1.58-104.61). Many patients had unknown TB (71.4%) and HIV (72.6%) statuses. CONCLUSION: In this study population, ground glass opacification, consolidation, and pleural effusions, with bilateral lung involvement and no zonal predominance were the most prevalent findings in proven COVID-19 infection. Quantification using the Brixia scoring system may assist with timeous assessment of disease severity in COVID-19 positive patients, as an overall predicator of clinical outcome.

Early radiological worsening of SARS-CoV-2 pneumonia predicts the need for ventilatory support.

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.

Modified Brixia chest X-ray severity scoring system and correlation with intubation, non-invasive ventilation and death in a hospitalised COVID-19 cohort.

INTRODUCTION: There are few existing severity scoring systems in the literature, and no formally widely accepted chest X-ray template for reporting COVID-19 infection. We aimed to modify the chest X-ray COVID-19 severity scoring system from the Brixia scoring system with placement of more emphasis on consolidation and to assess if the scoring tool could help predict intubation. METHODS: A severity chest X-ray scoring system was modified from the Brixia scoring system. PCR positive COVID-19 positive patient's chest X-rays admitted to our hospital over 3 months were reviewed and correlated with; non-invasive ventilation, intubation and death. An analysis was performed using a receiver operating curve to predict intubation from all admission chest X-rays. RESULTS: The median score of all 325 admission chest X-rays was 3 (Interquartile range (IQR) 0-6.5). The median score of admission chest X-rays of those who did not require ICU admission and survived was 1.5 (IQR 0-5); and 9 (IQR 4.75-12) was median admission score of those requiring intubation. The median scores of the pre-intubation ICU chest X-rays was 11.5 (IQR 9-14.125), this increased from a median admission chest X-ray score for this group of 9 (P-value < 0.01). A cut-off score of 6 had a sensitivity of 77% and specificity of 73% in predicting the need for intubation. CONCLUSION: Higher chest X-ray severity scores are associated with intubation, need for non-invasive ventilation and death. This tool may also be helpful in predicting intubation.

Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the COVID-19 pandemic.

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, over 103214008 cases have been reported, with more than 2231158 deaths as of January 31, 2021. Although the gold standard for diagnosis of this disease remains the reverse-transcription polymerase chain reaction of nasopharyngeal and oropharyngeal swabs, its false-negative rates have ignited the use of medical imaging as an important adjunct or alternative. Medical imaging assists in identifying the pathogenesis, the degree of pulmonary damage, and the characteristic features in each imaging modality. This literature review collates the characteristic radiographic findings of COVID-19 in various imaging modalities while keeping the preliminary focus on chest radiography, computed tomography (CT), and ultrasound scans. Given the higher sensitivity and greater proficiency in detecting characteristic findings during the early stages, CT scans are more reliable in diagnosis and serve as a practical method in following up the disease time course. As research rapidly expands, we have emphasized the CO-RADS classification system as a tool to aid in communicating the likelihood of COVID-19 suspicion among healthcare workers. Additionally, the utilization of other scoring systems such as MuLBSTA, Radiological Assessment of Lung Edema, and Brixia in this pandemic are reviewed as they integrate the radiographic findings into an objective scoring system to risk stratify the patients and predict the severity of disease. Furthermore, current progress in the utilization of artificial intelligence via radiomics is evaluated. Lastly, the lesson from the first wave and preparation for the second wave from the point of view of radiology are summarized.

Is chest X-ray severity scoring for COVID-19 pneumonia reliable?

PURPOSE: To explore whether chest X-ray severity scoring (CX-SS) could be reliable to assess the severity of pulmonary parenchymal disease in COVID-19 patients. MATERIAL AND METHODS: The study consisted of 325 patients whose COVID-19 was confirmed by RT-PCR test and who underwent chest X-ray and computed tomography (CT) studies to assess parenchymal disease severity. Only 195 cases included in the final analysis after exclusion of cases with previous chest disease and cases having more than 24 hours interval between their X-ray and CT chest studies. Both chest X-ray and CT severity scores (CT-SS) were recorded by 2 experienced radiologists and were compared to the clinical severity. Interobserver agreement was assessed for CX-SS and CT-SS. RESULTS: In relation to the clinical severity, the sensitivity of the CX-SS for diagnosis of moderate to severe parenchymal disease was high (90.4% and 100%) and low for mild cases (66.2%), while the specificity was high for mild to moderate parenchymal disease (100%) compared to severe cases (86.7%). The sensitivity, specificity, and diagnostic accuracy of the CT-SS were higher than CX-SS. Pearson correlation coefficient demonstrated a strong positive correlation between CX-SS and CT-SS (rs = 0.88, p < 0.001). The inter-observer agreement for CX-SS was good (k = 0.79, p = 0.001), and it was excellent for CT-SS (k = 0.85, p = 0.001). CONCLUSIONS: CX-SS is reliable to assess the severity of COVID-19 pulmonary parenchymal disease, especially in moderate and severe cases, with the tendency of overestimation of severe cases.

Modified Chest X-Ray Scoring System in Evaluating Severity of COVID-19 Patient in Dr. Soetomo General Hospital Surabaya, Indonesia.

INTRODUCTION: The management of COVID-19 patients requires efficiency and accuracy in methods of detection, identification, monitoring, and treatment feasible in every hospital. Aside from clinical presentations and laboratory markers, chest x-ray imaging could also detect pneumonia caused by COVID-19. It is also a fast, simple, cheap, and safe modality used for the management of COVID-19 patients. Established scoring systems of COVID-19 chest x-ray imaging include Radiographic Assessment of Lung Edema (RALE) and Brixia classification. A modified scoring system has been adopted from BRIXIA and RALE scoring systems and has been made to adjust the scoring system needs at Dr. Soetomo General Hospital, Indonesia. This study aims to determine the value of scoring systems through chest x-ray imaging in evaluating the severity of COVID-19. METHODS: Data were collected from May to June of 2020 who underwent chest x-ray evaluation. Each image is then scored using three types of classifications: modified score, RALE score, and Brixia score. The scores are then analyzed and compared with the clinical conditions and laboratory markers to determine their value in evaluating the severity of COVID-19 infection in patients. RESULTS: A total of 115 patients were males (51.1%) and 110 were females (48.9%). All three scoring systems are significantly correlated with the clinical severity of the disease, with the strengths of correlation in order from the strongest to weakest as Brixia score (p<0.01, correlation coefficient 0.232), RALE score (p<0.01, correlation coefficient 0.209), and Dr. Soetomo General Hospital score (p<0.01, correlation coefficient 0.194). All three scoring systems correlate significantly with each other. Dr. Soetomo General Hospital score correlates more towards Brixia score (p<0.01, correlation coefficient 0.865) than RALE score (p<0.01, correlation coefficient 0.855). Brixia to RALE score correlates with a coefficient of 0.857 (p<0.01). CONCLUSION: The modified scoring system can help determine the severity of the disease progression in COVID-19 patients especially in areas with shortages of facilities and specialists.