The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic: A Multinational Consensus Statement From the Fleischner Society

CT-based Assessment at 6-Month Follow-up of COVID-19 Pneumonia patients in China

This study aimed to assess pulmonary changes at 6-month follow-up CT and predictors of pulmonary residual abnormalities and fibrotic-like changes in COVID-19 pneumonia patients in China following relaxation of COVID restrictions in 2022. A total of 271 hospitalized patients with COVID-19 pneumonia admitted between November 29, 2022 and February 10, 2023 were prospectively evaluated at 6 months. CT characteristics and Chest CT scores of pulmonary abnormalities were compared between the initial and the 6-month CT. The association of demographic and clinical factors with CT residual abnormalities or fibrotic-like changes were assessed using logistic regression. Follow-up CT scans were obtained at a median of 177 days (IQR, 170-185 days) after hospital admission. Pulmonary residual abnormalities and fibrotic-like changes were found in 98 (36.2%) and 39 (14.4%) participants. In multivariable analysis of pulmonary residual abnormalities and fibrotic-like changes, the top three predictive factors were invasive ventilation (OR 13.6; 95% CI 1.9, 45; P < .001), age > 60 years (OR 9.1; 95% CI 2.3, 39; P = .01), paxlovid (OR 0.11; 95% CI 0.04, 0.48; P = .01) and invasive ventilation (OR 10.3; 95% CI 2.9, 33; P = .002), paxlovid (OR 0.1; 95% CI 0.03, 0.48; P = .01), smoker (OR 9.9; 95% CI 2.4, 31; P = .01), respectively. The 6-month follow-up CT of recent COVID-19 pneumonia cases in China showed a considerable proportion of the patients with pulmonary residual abnormalities and fibrotic-like changes. Antivirals against SARS-CoV-2 like paxlovid may be beneficial for long-term regression of COVID-19 pneumonia.

Standardization of a CT Protocol for Imaging Patients with Suspected COVID-19-A RACOON Project

CT protocols that diagnose COVID-19 vary in regard to the associated radiation exposure and the desired image quality (IQ). This study aims to evaluate CT protocols of hospitals participating in the RACOON (Radiological Cooperative Network) project, consolidating CT protocols to provide recommendations and strategies for future pandemics. In this retrospective study, CT acquisitions of COVID-19 patients scanned between March 2020 and October 2020 (RACOON phase 1) were included, and all non-contrast protocols were evaluated. For this purpose, CT protocol parameters, IQ ratings, radiation exposure (CTDIvol), and central patient diameters were sampled. Eventually, the data from 14 sites and 534 CT acquisitions were analyzed. IQ was rated good for 81% of the evaluated examinations. Motion, beam-hardening artefacts, or image noise were reasons for a suboptimal IQ. The tube potential ranged between 80 and 140 kVp, with the majority between 100 and 120 kVp. CTDIvol was 3.7 ± 3.4 mGy. Most healthcare facilities included did not have a specific non-contrast CT protocol. Furthermore, CT protocols for chest imaging varied in their settings and radiation exposure. In future, it will be necessary to make recommendations regarding the required IQ and protocol parameters for the majority of CT scanners to enable comparable IQ as well as radiation exposure for different sites but identical diagnostic questions.

Derivation and validation of novel integrated inpatient mortality prediction score for COVID-19 (IMPACT) using clinical, laboratory, and AI-processed radiological parameter upon admission: a multicentre study

Limited studies explore the use of AI for COVID-19 prognostication. This study investigates the relationship between AI-aided radiographic parameters, clinical and laboratory data, and mortality in hospitalized COVID-19 patients. We conducted a multicentre retrospective study. The derivation and validation cohort comprised of 512 and 137 confirmed COVID-19 patients, respectively. Variable selection for constructing an in-hospital mortality scoring model was performed using the least absolute shrinkage and selection operator, followed by logistic regression. The accuracy of the scoring model was assessed using the area under the receiver operating characteristic curve. The final model included eight variables: anosmia (OR: 0.280; 95%CI 0.095-0.826), dyspnoea (OR: 1.684; 95%CI 1.049-2.705), loss of consciousness (OR: 4.593; 95%CI 1.702-12.396), mean arterial pressure (OR: 0.928; 95%CI 0.900-0.957), peripheral oxygen saturation (OR: 0.981; 95%CI 0.967-0.996), neutrophil % (OR: 1.034; 95%CI 1.013-1.055), serum urea (OR: 1.018; 95%CI 1.010-1.026), affected lung area score (OR: 1.026; 95%CI 1.014-1.038). The Integrated Inpatient Mortality Prediction Score for COVID-19 (IMPACT) demonstrated a predictive value of 0.815 (95% CI 0.774-0.856) in the derivation cohort. Internal validation resulted in an AUROC of 0.770 (95% CI 0.661-0.879). Our study provides valuable evidence of the real-world application of AI in clinical settings. However, it is imperative to conduct prospective validation of our findings, preferably utilizing a control group and extending the application to broader populations.

The evolution of lung computed tomography findings in COVID-19 from 2020 to 2023: more signs of co-infection

Significant changes were observed in the lung imaging of hospitalised COVID-19 patients from 2020 to 2023, with the emergence of more signs of co-infection https://bit.ly/3TaQlJ2.

Characteristics of patients with COVID-19 who have deteriorating chest X-ray findings within 48 h: a retrospective cohort study

The severity of chest X-ray (CXR) findings is a prognostic factor in patients with coronavirus disease 2019 (COVID-19). We investigated the clinical and genetic characteristics and prognosis of patients with worsening CXR findings during early hospitalization. We retrospectively included 1656 consecutive Japanese patients with COVID-19 recruited through the Japan COVID-19 Task Force. Rapid deterioration of CXR findings was defined as increased pulmonary infiltrates in ≥ 50% of the lung fields within 48 h of admission. Rapid deterioration of CXR findings was an independent risk factor for death, most severe illness, tracheal intubation, and intensive care unit admission. The presence of consolidation on CXR, comorbid cardiovascular and chronic obstructive pulmonary diseases, high body temperature, and increased serum aspartate aminotransferase, potassium, and C-reactive protein levels were independent risk factors for rapid deterioration of CXR findings. Risk variant at the ABO locus (rs529565-C) was associated with rapid deterioration of CXR findings in all patients. This study revealed the clinical features, genetic features, and risk factors associated with rapid deterioration of CXR findings, a poor prognostic factor in patients with COVID-19.

Lung Ultrasound Elastography by SWE2D and "Fibrosis-like" Computed Tomography Signs after COVID-19 Pneumonia: A Follow-Up Study

The aim of this study was to assess the shear wave velocity by LUS elastography (SWE2D) for the evaluation of superficial lung stiffness after COVID-19 pneumonia, according to "fibrosis-like" signs found by Computed Tomography (CT), considering the respiratory function. Seventy-nine adults participated in the study 42 to 353 days from symptom onset. Paired evaluations (SWE2D and CT) were performed along with the assessment of arterial blood gases and spirometry, three times with 100 days in between. During the follow-up and within each evaluation, the SWE2D velocity changed over time (MANOVA, p < 0.05) according to the extent of "fibrosis-like" CT signs by lung lobe (ANOVA, p < 0.05). The variability of the SWE2D velocity was consistently related to the first-second forced expiratory volume and the forced vital capacity (MANCOVA, p < 0.05), which changed over time with no change in blood gases. Covariance was also observed with age and patients' body mass index, the time from symptom onset until hospital admission, and the history of diabetes in those who required intensive care during the acute phase (MANCOVA, p < 0.05). After COVID-19 pneumonia, SWE2D velocity can be related to the extent and regression of "fibrotic-like" involvement of the lung lobes, and it could be a complementary tool in the follow-up after COVID-19 pneumonia.

Computed tomography-determined skeletal muscle density predicts 3-year mortality in initial-dialysis patients in China

BACKGROUND

Skeletal muscle mass and quality assessed by computed tomography (CT) images of the third lumbar vertebra (L3) level have been established as risk factors for poor clinical outcomes in several illnesses, but the relevance for dialysis patients is unclear. A few studies have suggested a correlation between CT-determined skeletal muscle mass and quality at the first lumbar vertebra (L1) level and adverse outcomes. Generally, chest CT does not reach beyond L1. We aimed to determine whether opportunistic CT scan (chest CT)-determined skeletal muscle mass and quality at L1 are associated with mortality in initial-dialysis patients.

METHODS

This 3-year multicentric retrospective study included initial-dialysis patients from four centres between 2014 and 2017 in China. Unenhanced CT images of the L1 and L3 levels were obtained to assess skeletal muscle mass [by skeletal muscle index, (SMI), cm2 /m2 ] and quality [by skeletal muscle density (SMD), HU]. Skeletal muscle measures at L1 were compared with those at L3. The sex-specific optimal cutoff values of L1 SMI and L1 SMD were determined in relation to all-cause mortality. The outcomes were all-cause death and cardiac death. Cox regression models were applied to investigate the risk factors for death.

RESULTS

A total of 485 patients were enrolled, of whom 257 had both L1 and L3 images. Pearson's correlation coefficient between L1 and L3 SMI was 0.84 (P < 0.001), and that between L1 and L3 SMD was 0.90 (P < 0.001). No significant association between L1 SMI and mortality was observed (P > 0.05). Low L1 SMD (n = 280, 57.73%) was diagnosed based on the optimal cutoff value (<39.56 HU for males and <33.06 HU for females). Multivariate regression analysis revealed that the low L1 SMD group had higher risks of all-cause death (hazard ratio 1.80; 95% confidence interval 1.05-3.11, P = 0.034) and cardiac death (hazard ratio 3.74; 95% confidence interval 1.43-9.79, P = 0.007).

CONCLUSIONS

In initial-dialysis patients, there is high agreement between the L1 and L3 measures for SMI and SMD. Low SMD measured at L1, but not low SMI, is an independent predictor of both all-cause death and cardiac death.

Segmentation of lung lobes and lesions in chest CT for the classification of COVID-19 severity

To precisely determine the severity of COVID-19-related pneumonia, computed tomography (CT) is an imaging modality beneficial for patient monitoring and therapy planning. Thus, we aimed to develop a deep learning-based image segmentation model to automatically assess lung lesions related to COVID-19 infection and calculate the total severity score (TSS). The entire dataset consisted of 124 COVID-19 patients acquired from Chulabhorn Hospital, divided into 28 cases without lung lesions and 96 cases with lung lesions categorized severity by radiologists regarding TSS. The model used a 3D-UNet along with DenseNet and ResNet models that had already been trained to separate the lobes of the lungs and figure out the percentage of lung involvement due to COVID-19 infection. It also used the Dice similarity coefficient (DSC) to measure TSS. Our final model, consisting of 3D-UNet integrated with DenseNet169, achieved segmentation of lung lobes and lesions with the Dice similarity coefficients of 91.52% and 76.89%, respectively. The calculated TSS values were similar to those evaluated by radiologists, with an R2 of 0.842. The correlation between the ground-truth TSS and model prediction was greater than that of the radiologist, which was 0.890 and 0.709, respectively.

Doubts and concerns about COVID-19 uncertainties on imaging data, clinical score, and outcomes

BACKGROUND

COVID-19 is a pandemic disease affecting predominantly the respiratory apparatus with clinical manifestations ranging from asymptomatic to respiratory failure. Chest CT is a crucial tool in diagnosing and evaluating the severity of pulmonary involvement through dedicated scoring systems. Nonetheless, many questions regarding the relationship of radiologic and clinical features of the disease have emerged in multidisciplinary meetings. The aim of this retrospective study was to explore such relationship throughout an innovative and alternative approach.

MATERIALS AND METHODS

This study included 550 patients (range 25-98 years; 354 males, mean age 66.1; 196 females, mean age 70.9) hospitalized for COVID-19 with available radiological and clinical data between 1 March 2021 and 30 April 2022. Radiological data included CO-RADS, chest CT score, dominant pattern, and typical/atypical findings detected on CT examinations. Clinical data included clinical score and outcome. The relationship between such features was investigated through the development of the main four frequently asked questions summarizing the many issues arisen in multidisciplinary meetings, as follows 1) CO-RADS, chest CT score, clinical score, and outcomes; 2) the involvement of a specific lung lobe and outcomes; 3) dominant pattern/distribution and severity score for the same chest CT score; 4) additional factors and outcomes.

RESULTS

1) If CT was suggestive for COVID, a strong correlation between CT/clinical score and prognosis was found; 2) Middle lobe CT involvement was an unfavorable prognostic criterion; 3) If CT score < 50%, the pattern was not influential, whereas if CT score > 50%, crazy paving as dominant pattern leaded to a 15% increased death rate, stacked up against other patterns, thus almost doubling it; 4) Additional factors usually did not matter, but lymph-nodes and pleural effusion worsened prognosis.

CONCLUSIONS

This study outlined those radiological features of COVID-19 most relevant towards disease severity and outcome with an innovative approach.

Ultra-short echo time (UTE) MR imaging: A brief review on technical considerations and clinical applications

BACKGROUND

 With the availability of MRI sequences with ultrashort echo times (UTE sequences), a signal can be gained from tissue, which was formerly only indirectly accessible. While already extensively employed in various research settings, the widespread transition of UTE imaging to clinical practice is just starting.

METHODS

 Based on a systematic literature search as well as knowledge gained through annual participation in conferences dedicated to advances in MRI, this review aims to give a brief overview of technical considerations and challenges of UTE imaging and summarizes the major areas of application of UTE imaging.

RESULTS

 UTE is already employed in clinical practice for structural lung imaging as well as the characterization of tissue composition and its alterations in selected musculoskeletal, cardiovascular, or neurodegenerative diseases. In specific contexts it can replace CT examinations with ionizing radiation and is especially attractive for pediatric patients and longitudinal monitoring of disease progression and treatment.

CONCLUSION

 UTE imaging provides an interesting and very valuable tool for various clinical purposes and promises a multitude of new insights into tissue properties. While some challenges remain, ongoing adoption in the clinical routine can be expected, as UTE approaches provide a new contrast and capture a signal in tissue formerly invisible on MR imaging.

KEY POINTS

  · UTE imaging gains relevance in clinical settings. · UTE imaging is employed for the characterization of tissue composition and its alterations in selected musculoskeletal, cardiovascular, or neurodegenerative diseases. · UTE imaging is employed in the clinical routine for structural lung imaging. · UTE imaging promises a multitude of new insights into tissue properties.

Investigation of the diagnostic importance and accuracy of CT in the chest compared to the RT-PCR test for suspected COVID-19 patients in Jordan

This article aims to synthesize the existing literature on the implementation of public policies to incentivize the development of treatments for rare diseases, (diseases with very low prevalence and therefore with low commercial interest) otherwise known as orphan drugs. The implementation of these incentives in the United States (US), Japan, and in the European Union (EU) seems to be related to a substantial increase in treatments for these diseases, and has influenced the way the pharmaceutical research & development (R&D) system operates beyond this policy area. Despite the success of the Orphan Drug model, the academic literature also highlights the negative implications that these public policies have on affordability and access to orphan drugs, as well as on the prioritization of certain disease rare areas over others. The synthesis focuses mostly on the United States' Orphan Drug Act (ODA) as a model for subsequent policies in other regions and countries. It starts with a historical overview of the creation of the term "rare diseases", continues with a summary of the evidence available on the US ODA's positive and negative impacts, and provides a summary of the different proposals to reform these incentives in light of the negative outcomes described. Finally, it describes some key aspects of the Japanese and European policies, as well as some of the challenges captured in the literature related to their impact in Low- and Middle-Income Countries (LMICs).

Performance in prognostic capacity and efficiency of the Thoracic Care Suite GE AI tool applied to chest radiography of patients with COVID-19 pneumonia

OBJECTIVE

Rapid progression of COVID-19 pneumonia may put patients at risk of requiring ventilatory support, such as non-invasive mechanical ventilation or endotracheal intubation. Implementing tools that detect COVID-19 pneumonia can improve the patient's healthcare. We aim to evaluate the efficacy and efficiency of the artificial intelligence (AI) tool GE Healthcare's Thoracic Care Suite (featuring Lunit INSIGHT CXR, TCS) to predict the ventilatory support need based on pneumonic progression of COVID-19 on consecutive chest X-rays.

METHODS

Outpatients with confirmed SARS-CoV-2 infection, with chest X-ray (CXR) findings probable or indeterminate for COVID-19 pneumonia, who required a second CXR due to unfavorableclinical course, were collected. The number of affected lung fields for the two CXRs was assessed using the AI tool.

RESULTS

One hundred fourteen patients (57.4±14.2 years, 65-57%-men) were retrospectively collected. Fifteen (13.2%) required ventilatory support. Progression of pneumonic extension ≥0.5 lung fields per day compared to pneumonia onset, detected using the TCS tool, increased the risk of requiring ventilatory support by 4-fold. Analyzing the AI output required 26s of radiological time.

CONCLUSIONS

Applying the AI tool, Thoracic Care Suite, to CXR of patients with COVID-19 pneumonia allows us to anticipate ventilatory support requirements requiring less than half a minute.

COVID-19 and cholesterol biosynthesis: Towards innovative decision support systems

With COVID-19 becoming endemic, there is a continuing need to find biomarkers characterizing the disease and aiding in patient stratification. We studied the relation between COVID-19 and cholesterol biosynthesis by comparing 10 intermediates of cholesterol biosynthesis during the hospitalization of 164 patients (admission, disease deterioration, discharge) admitted to the University Medical Center of Ljubljana. The concentrations of zymosterol, 24-dehydrolathosterol, desmosterol, and zymostenol were significantly altered in COVID-19 patients. We further developed a predictive model for disease severity based on clinical parameters alone and their combination with a subset of sterols. Our machine learning models applying 8 clinical parameters predicted disease severity with excellent accuracy (AUC = 0.96), showing substantial improvement over current clinical risk scores. After including sterols, model performance remained better than COVID-GRAM. This is the first study to examine cholesterol biosynthesis during COVID-19 and shows that a subset of cholesterol-related sterols is associated with the severity of COVID-19.

Pleural clinic: where thoracic ultrasound meets respiratory medicine

Thoracic ultrasound (TUS) has become an essential procedure in respiratory medicine. Due to its intrinsic safety and versatility, it has been applied in patients affected by several respiratory diseases both in intensive care and outpatient settings. TUS can complement and often exceed stethoscope and radiological findings, especially in managing pleural diseases. We hereby aimed to describe the establishment, development, and optimization in a large, tertiary care hospital of a pleural clinic, which is dedicated to the evaluation and monitoring of patients with pleural diseases, including, among others, pleural effusion and/or thickening, pneumothorax and subpleural consolidation. The clinic was initially meant to follow outpatients undergoing medical thoracoscopy. In this scenario, TUS allowed rapid and regular assessment of these patients, promptly diagnosing recurrence of pleural effusion and other complications that could be appropriately managed. Over time, our clinic has rapidly expanded its initial indications thus becoming the place to handle more complex respiratory patients in collaboration with, among others, thoracic surgeons and oncologists. In this article, we critically describe the strengths and pitfalls of our "pleural clinic" and propose an organizational model that results from a synergy between respiratory physicians and other professionals. This model can inspire other healthcare professionals to develop a similar organization based on their local setting.

Which sequence should be used in the thorax magnetic resonance imaging of COVID-19: a comparative study

Background and aim

To evaluate and compare magnetic resonance imaging (MRI) sequences that could potentially be used in the diagnosis of coronavirus disease 2019 (COVID-19).

Materials and methods

Included in the study were 42 patient who underwent thorax computed tomography (CT) for COVID-19 pneumonia and thorax MRI for any reason within 24 h after CT. The T2-weighted fast spin echo periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) (T2W-FSE-P), fast imaging employing steady-state acquisition, T2 fat-saturated FSE, axial T1 liver acquisition with volume acceleration (LAVA) and single-shot FSE images were compared in terms of their ability to show COVID-19 findings.

Results

The mean age of the patients was 47.2 ± 24 years. Of the patients, 22 were male (52.4%) and 20 (47.6%) were female. The interobserver intraclass coefficient (ICC) for the image quality score was the highest in the T2W-FSE-P sequence and lowest in the T1 LAVA sequence. All of the lesion-based evaluations of the interobserver agreement were statistically significant, with the kappa value varying between 0.798 and 0.998.

Conclusion

All 5 sequences evaluated in the study were successful in showing the parenchymal findings of COVID-19. Since the T2W-FSE-P sequence had the best scores in both interobserver agreement and ICC for the image quality score, it was considered that it can be included in thorax MRI examinations to assist the diagnosis of COVID-19.

Diagnostic Performance of the Radiological Society of North America Consensus Statement for Reporting COVID-19 Chest CT Findings: A Revisit

Coronavirus disease 2019 (COVID-19) is a highly contagious respiratory disease that leads to variable degrees of illness, and which may be fatal. We evaluated the diagnostic performance of each chest computed tomography (CT) reporting category recommended by the Expert Consensus of the Radiological Society of North America (RSNA) in comparison with that of reverse transcription polymerase chain reaction (RT-PCR). We aimed to add an analysis of this form of reporting in the Middle East, as few studies have been performed there. Between July 2021 and February 2022, 184 patients with a mean age of 55.56 ± 16.71 years and probable COVID-19 infections were included in this retrospective study. Approximately 64.67% (119 patients) were male, while 35.33% (65 patients) were female. Within 7 days, all patients underwent CT and RT-PCR examinations. According to a statement by the RSNA, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of each CT reporting category were calculated, and the RT-PCR results were used as a standard reference. The RT-PCR results confirmed a final diagnosis of COVID-19 infection in 60.33% of the patients. For COVID-19 diagnoses, the typical category (n = 88) had a sensitivity, specificity, PPV, and accuracy of 74.8%, 93.2%, 94.3%, and 92.5%, respectively. For non-COVID-19 diagnoses, the PPVs for the atypical (n = 22) and negative (n = 46) categories were 81.8% and 89.1%, respectively. The PPV for the indeterminate (n = 28) category was 67.9%, with a low sensitivity of 17.1%. However, the RSNA's four chest CT reporting categories provide a strong diagnostic foundation and are highly correlated with the RT-PCR results for the typical, atypical, and negative categories, but they are weaker for the indeterminate category.

Digital tomosynthesis improves chest radiograph accuracy and reduces microbiological false negatives in COVID-19 diagnosis

PURPOSE

Diagnosing pneumonia by radiograph is improvable. We aimed (a) to compare radiograph and digital thoracic tomosynthesis (DTT) performances and agreement for COVID-19 pneumonia diagnosis, and (b) to assess the DTT ability for COVID-19 diagnosis when polymerase chain reaction (PCR) and radiograph are negative.

METHODS

Two emergency radiologists with 11 (ER1) and 14 experience-years (ER2) retrospectively evaluated radiograph and DTT images acquired simultaneously in consecutively clinically suspected COVID-19 pneumonia patients in March 2020-January 2021. Considering PCR and/or serology as reference standard, DTT and radiograph diagnostic performance and interobserver agreement, and DTT contributions in unequivocal, equivocal, and absent radiograph opacities were analysed by the area under the curve (AUC), Cohen's Kappa, Mc-Nemar's and Wilcoxon tests.

RESULTS

We recruited 480 patients (49 ± 15 years, 277 female). DTT increased ER1 (from 0.76, CI95% 0.7-0.8 to 0.79, CI95% 0.7-0.8; P=.04) and ER2 (from 0.77 CI95% 0.7-0.8 to 0.80 CI95% 0.8-0.8, P=.02) radiograph-AUCs, sensitivity, specificity, predictive values, and positive likelihood ratio. In false negative microbiological cases, DTT suggested COVID-19 pneumonia in 13% (4/30; P=.052, ER1) and 20% (6/30; P=.020, ER2) more than radiograph. DTT showed new or larger opacities in 33-47% of cases with unequivocal opacities in radiograph, new opacities in 2-6% of normal radiographs and reduced equivocal opacities by 13-16%. Kappa increased from 0.64 (CI95% 0.6-0.8) to 0.7 (CI95% 0.7-0.8) for COVID-19 pneumonia probability, and from 0.69 (CI95% 0.6-0.7) to 0.76 (CI95% 0.7-0.8) for pneumonic extension.

CONCLUSION

DTT improves radiograph performance and agreement for COVID-19 pneumonia diagnosis and reduces PCR false negatives.

Intermittent body composition analysis as monitoring tool for muscle wasting in critically ill COVID-19 patients

OBJECTIVES

SARS-CoV-2 virus infection can lead to acute respiratory distress syndrome (ARDS), which can be complicated by severe muscle wasting. Until now, data on muscle loss of critically ill COVID-19 patients are limited, while computed tomography (CT) scans for clinical follow-up are available. We sought to investigate the parameters of muscle wasting in these patients by being the first to test the clinical application of body composition analysis (BCA) as an intermittent monitoring tool.

MATERIALS

BCA was conducted on 54 patients, with a minimum of three measurements taken during hospitalization, totaling 239 assessments. Changes in psoas- (PMA) and total abdominal muscle area (TAMA) were assessed by linear mixed model analysis. PMA was calculated as relative muscle loss per day for the entire monitoring period, as well as for the interval between each consecutive scan. Cox regression was applied to analyze associations with survival. Receiver operating characteristic (ROC) analysis and Youden index were used to define a decay cut-off.

RESULTS

Intermittent BCA revealed significantly higher long-term PMA loss rates of 2.62% (vs. 1.16%, p < 0.001) and maximum muscle decay of 5.48% (vs. 3.66%, p = 0.039) per day in non-survivors. The first available decay rate did not significantly differ between survival groups but showed significant associations with survival in Cox regression (p = 0.011). In ROC analysis, PMA loss averaged over the stay had the greatest discriminatory power (AUC = 0.777) for survival. A long-term PMA decline per day of 1.84% was defined as a threshold; muscle loss beyond this cut-off proved to be a significant BCA-derived predictor of mortality.

CONCLUSION

Muscle wasting in critically ill COVID-19 patients is severe and correlates with survival. Intermittent BCA derived from clinically indicated CT scans proved to be a valuable monitoring tool, which allows identification of individuals at risk for adverse outcomes and has great potential to support critical care decision-making.

Assessment of the Suitability of the Fleischner Society Imaging Guidelines in Evaluating Chest Radiographs of COVID-19 Patients

BACKGROUND

The Fleischner Society established consensus guidelines for imaging in patients with coronavirus disease 2019 (COVID-19). We investigated the prevalence of pneumonia and the adverse outcomes by dividing groups according to the symptoms and risk factors of patients and assessed the suitability of the Fleischner society imaging guidelines in evaluating chest radiographs of COVID-19 patients.

METHODS

From February 2020 to May 2020, 685 patients (204 males, mean 58 ± 17.9 years) who were diagnosed with COVID-19 and hospitalized were included. We divided patients into four groups according to the severity of symptoms and presence of risk factors (age > 65 years and presence of comorbidities). The patient groups were defined as follows: group 1 (asymptomatic patients), group 2 (patients with mild symptoms without risk factors), group 3 (patients with mild symptoms and risk factors), and group 4 (patients with moderate to severe symptoms). According to the Fleischner society, chest imaging is not indicated for groups 1-2 but is indicated for groups 3-4. We compared the prevalence and score of pneumonia on chest radiographs and compare the adverse outcomes (progress to severe pneumonia, intensive care unit admission, and death) between groups.

RESULTS

Among the 685 COVID-19 patients, 138 (20.1%), 396 (57.8%), 102 (14.9%), and 49 (7.1%) patients corresponded to groups 1 to 4, respectively. Patients in groups 3-4 were significantly older and showed significantly higher prevalence rates of pneumonia (group 1-4: 37.7%, 51.3%, 71.6%, and 98%, respectively, P < 0.001) than those in groups 1-2. Adverse outcomes were also higher in groups 3-4 than in groups 1-2 (group 1-4: 8.0%, 3.5%, 6.9%, and 51%, respectively, P < 0.001). Patients with adverse outcomes in group 1 were initially asymptomatic but symptoms developed during follow-up. They were older (mean age, 80 years) and most of them had comorbidities (81.8%). Consistently asymptomatic patients had no adverse events.

CONCLUSION

The prevalence of pneumonia and adverse outcomes were different according to the symptoms and risk factors in COVID-19 patients. Therefore, as the Fleischner Society recommended, evaluation and monitoring of COVID-19 pneumonia using chest radiographs is necessary for old symptomatic patients with comorbidities.

Predicting intensive care need for COVID-19 patients using deep learning on chest radiography

Purpose

Image-based prediction of coronavirus disease 2019 (COVID-19) severity and resource needs can be an important means to address the COVID-19 pandemic. In this study, we propose an artificial intelligence/machine learning (AI/ML) COVID-19 prognosis method to predict patients' needs for intensive care by analyzing chest X-ray radiography (CXR) images using deep learning.

Approach

The dataset consisted of 8357 CXR exams from 5046 COVID-19-positive patients as confirmed by reverse transcription polymerase chain reaction (RT-PCR) tests for the SARS-CoV-2 virus with a training/validation/test split of 64%/16%/20% on a by patient level. Our model involved a DenseNet121 network with a sequential transfer learning technique employed to train on a sequence of gradually more specific and complex tasks: (1) fine-tuning a model pretrained on ImageNet using a previously established CXR dataset with a broad spectrum of pathologies; (2) refining on another established dataset to detect pneumonia; and (3) fine-tuning using our in-house training/validation datasets to predict patients' needs for intensive care within 24, 48, 72, and 96 h following the CXR exams. The classification performances were evaluated on our independent test set (CXR exams of 1048 patients) using the area under the receiver operating characteristic curve (AUC) as the figure of merit in the task of distinguishing between those COVID-19-positive patients who required intensive care following the imaging exam and those who did not.

Results

Our proposed AI/ML model achieved an AUC (95% confidence interval) of 0.78 (0.74, 0.81) when predicting the need for intensive care 24 h in advance, and at least 0.76 (0.73, 0.80) for 48 h or more in advance using predictions based on the AI prognostic marker derived from CXR images.

Conclusions

This AI/ML prediction model for patients' needs for intensive care has the potential to support both clinical decision-making and resource management.

Chest CT features of COVID-19 in vaccinated versus unvaccinated patients: use of CT severity score and outcome analysis

OBJECTIVES

To evaluate the impact of vaccination on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and moreover on coronavirus disease 2019 (COVID-19) pneumonia, by assessing the extent of lung disease using the CT severity score (CTSS).

METHODS

Between September 2021 and February 2022, SARS-CoV-2 positive patients who underwent chest CT were retrospectively enrolled. Anamnestic and clinical data, including vaccination status, were obtained. All CT scans were evaluated by two readers using the CTSS, based on a 25-point scale. Univariate and multivariate logistic regression analyses were performed to evaluate the associations between CTSS and clinical or demographic variables. An outcome analysis was used to differentiate clinical outcome between vaccinated and unvaccinated patients.

RESULTS

Of the 1040 patients (537 males, 503 females; median age 58 years), 678 (65.2%) were vaccinated and 362 (34.8%) unvaccinated. Vaccinated patients showed significantly lower CTSS compared to unvaccinated patients (p < 0.001), also when patients without lung involvement (CTSS = 0) were excluded (p < 0.001). Older age, male gender and lower number of doses administered were associated with higher CTSS, however, in the multivariate analysis, vaccination status resulted to be the variable with the strongest association with CTSS. Clinical outcomes were significantly worse in unvaccinated patients, including higher number of ICU admissions and higher mortality rates.

CONCLUSIONS

Lung involvement during COVID-19 was significantly less severe in vaccinated patients compared with unvaccinated patients, who also showed worse clinical outcomes. Vaccination status was the strongest variable associated to the severity of COVID-related, more than age, gender, and number of doses administered.

Prognostic value of respiratory compliance course on mortality in COVID-19 patients with vv-ECMO

BACKGROUND

COVID-19-associated acute respiratory distress syndrome (ARDS) supported by veno-venous extra-corporal membrane oxygenation (vv-ECMO) results in a high in-hospital mortality rate of more than 35%. However, after cannulation, no prognostic factor has been described to guide the management of these patients. The objective was to assess the association between static respiratory compliance over the first 10 days post-vv-ECMO implantation on 180-day mortality.

RESULTS

In this multicentric retrospective study in three ECMO referral centers, all patients with COVID-19-associated ARDS supported by vv-ECMO were included from 03/01/2020 to 12/31/2021. Patients were ventilated with ultra-protective settings targeting a driving pressure lower than 15 cmH₂O. 122 patients were included. Median age was 59 IQR (52-64), 83 (68%) were male, with a median body mass index of 33 (28-37) kg/m². Delay between first symptoms to vv-ECMO implantation was 16 (10-21) days. Six-month death was 48%. Over the first ten days, compliance increased in 180 day survivors [from 18 (12-25) to 20 (15-27) mL/cmH₂O] compared to non-survivors [from 12 (9-20) to 10 (8-14) mL/cmH₂O, p interaction < 0.0001]. A time varying multivariable Cox model found age, history of chronic lung disease, compliance from day one to day ten and sweep gas flow from day one to day ten as independent factors associated with 180-day mortality.

CONCLUSIONS

In COVID-19-associated ARDS, static respiratory compliance course over the first ten days post-vv-ECMO implantation is associated with 180-day mortality. This new information may provide crucial information on the patient's prognosis for intensivists.

Identification of Asymptomatic COVID-19 Patients on Chest CT Images Using Transformer-Based or Convolutional Neural Network-Based Deep Learning Models

Novel coronavirus disease 2019 (COVID-19) has rapidly spread throughout the world; however, it is difficult for clinicians to make early diagnoses. This study is to evaluate the feasibility of using deep learning (DL) models to identify asymptomatic COVID-19 patients based on chest CT images. In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs) or transformer architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou were randomly split into a training set (n = 2140) and an internal-validation set (n = 360). Data from Suzhou was the external-test set (n = 200). Model performance was assessed by the metrics accuracy, recall, and specificity and was compared with the assessments of two radiologists. A total of 2700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by the EfficientNet model (0.954). The recall and the precision of the Swin model were 0.989 and 1.000, respectively. In the test dataset, the Swin model was still the best and achieved the highest accuracy (0.980). All the DL models performed remarkably better than the two experts. Last, the time on the test set diagnosis spent by two experts-42 min, 17 s (junior); and 29 min, 43 s (senior)-was significantly higher than those of the DL models (all below 2 min). This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found that a transformer-based model, the Swin model, performed best.

Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China

Background

In December 2022, there was a large Omicron epidemic in Hangzhou, China. Many people were diagnosed with Omicron pneumonia with variable symptom severity and outcome. Computed tomography (CT) imaging has been proven to be an important tool for COVID-19 pneumonia screening and quantification. We hypothesized that CT-based machine learning algorithms can predict disease severity and outcome in Omicron pneumonia, and we compared its performance with the pneumonia severity index (PSI)-related clinical and biological features.

Methods

Our study included 238 patients with the Omicron variant who have been admitted to our hospital in China from 15 December 2022 to 16 January 2023 (the first wave after the dynamic zero-COVID strategy stopped). All patients had a positive real-time polymerase chain reaction (PCR) or lateral flow antigen test for SARS-CoV-2 after vaccination and no previous SARS-CoV-2 infections. We recorded patient baseline information pertaining to demographics, comorbid conditions, vital signs, and available laboratory data. All CT images were processed with a commercial artificial intelligence (AI) algorithm to obtain the volume and percentage of consolidation and infiltration related to Omicron pneumonia. The support vector machine (SVM) model was used to predict the disease severity and outcome.

Results

The receiver operating characteristic (ROC) area under the curve (AUC) of the machine learning classifier using PSI-related features was 0.85 (accuracy = 87.40%, p < 0.001) for predicting severity while that using CT-based features was only 0.70 (accuracy = 76.47%, p = 0.014). If combined, the AUC was not increased, showing 0.84 (accuracy = 84.03%, p < 0.001). Trained on outcome prediction, the classifier reached the AUC of 0.85 using PSI-related features (accuracy = 85.29%, p < 0.001), which was higher than using CT-based features (AUC = 0.67, accuracy = 75.21%, p < 0.001). If combined, the integrated model showed a slightly higher AUC of 0.86 (accuracy = 86.13%, p < 0.001). Oxygen saturation, IL-6, and CT infiltration showed great importance in both predicting severity and outcome.

Conclusion

Our study provided a comprehensive analysis and comparison between baseline chest CT and clinical assessment in disease severity and outcome prediction in Omicron pneumonia. The predictive model accurately predicts the severity and outcome of Omicron infection. Oxygen saturation, IL-6, and infiltration in chest CT were found to be important biomarkers. This approach has the potential to provide frontline physicians with an objective tool to manage Omicron patients more effectively in time-sensitive, stressful, and potentially resource-constrained environments.

What can we learn from the Baduanjin rehabilitation as COVID-19 treatment?: A narrative review

AIM

To understand Baduanjin rehabilitation therapy in mild COVID-19 patients.

DESIGN

A narrative review.

METHODS

A literature search for COVID-19 and Baduanjin treatments was conducted on Chinese and English electronic databases: China National Knowledge Infrastructure, Wanfang Data, Embase, PubMed, Scopus, Science Direct, Ebscohost, SPORTDiscus and ProQuest.

RESULTS

Twelve studies on the Baduanjin rehabilitation for COVID-19 patients have been included. We acknowledged the considerable published research and current clinical practice using Baduanjin for COVID-19 treatment in the following areas: anxiety, depression, insomnia, lung function rehabilitation, immunity and activity endurance.

CONCLUSION

The use of Baduanjin as adjuvant therapy for COVID-19 patients' rehabilitation is still limited, therefore, more clinical studies are needed to confirm its efficacy.

3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients

Chest CT is a useful initial exam in patients with coronavirus disease 2019 (COVID-19) for assessing lung damage. AI-powered predictive models could be useful to better allocate resources in the midst of the pandemic. Our aim was to build a deep-learning (DL) model for COVID-19 outcome prediction inclusive of 3D chest CT images acquired at hospital admission. This retrospective multicentric study included 1051 patients (mean age 69, SD = 15) who presented to the emergency department of three different institutions between 20th March 2020 and 20th January 2021 with COVID-19 confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR). Chest CT at hospital admission were evaluated by a 3D residual neural network algorithm. Training, internal validation, and external validation groups included 608, 153, and 290 patients, respectively. Images, clinical, and laboratory data were fed into different customizations of a dense neural network to choose the best performing architecture for the prediction of mortality, intubation, and intensive care unit (ICU) admission. The AI model tested on CT and clinical features displayed accuracy, sensitivity, specificity, and ROC-AUC, respectively, of 91.7%, 90.5%, 92.4%, and 95% for the prediction of patient's mortality; 91.3%, 91.5%, 89.8%, and 95% for intubation; and 89.6%, 90.2%, 86.5%, and 94% for ICU admission (internal validation) in the testing cohort. The performance was lower in the validation cohort for mortality (71.7%, 55.6%, 74.8%, 72%), intubation (72.6%, 74.7%, 45.7%, 64%), and ICU admission (74.7%, 77%, 46%, 70%) prediction. The addition of the available laboratory data led to an increase in sensitivity for patient's mortality (66%) and specificity for intubation and ICU admission (50%, 52%, respectively), while the other metrics maintained similar performance results. We present a deep-learning model to predict mortality, ICU admittance, and intubation in COVID-19 patients. KEY POINTS: • 3D CT-based deep learning model predicted the internal validation set with high accuracy, sensibility and specificity (> 90%) mortality, ICU admittance, and intubation in COVID-19 patients. • The model slightly increased prediction results when laboratory data were added to the analysis, despite data imbalance. However, the model accuracy dropped when CT images were not considered in the analysis, implying an important role of CT in predicting outcomes.

Did radiation exposure increase with chest computed tomography use among different ages during the COVID-19 pandemic? A multi-center study with 42028 chest computed tomography scans

PURPOSE

To determine whether radiation exposure increased among different ages with chest computed tomography (CT) use during the coronavirus disease-2019 (COVID-19) pandemic.

METHODS

Patients with chest CT scans in an 8-month period of the pandemic between March 15, 2020, and November 15, 2020, and the same period of the preceding year were included in the study. Indications of chest CT scans were obtained from the clinical notes and categorized as infectious diseases, neoplastic disorders, trauma, and other diseases. Chest CT scans for infectious diseases during the pandemic were compared with those with the same indications in 2019. The dose-length product values were obtained from the protocol screen individually.

RESULTS

The total number of chest CT scans with an indication of infectious disease was 21746 in 2020 and 4318 in 2019. Total radiation exposure increased by 573% with the use of chest CT for infectious indications but decreased by 19% for neoplasia, 12% for trauma, and 43% for other reasons. The mean age of the patients scanned in 2019 was significantly higher than those scanned during the pandemic (64.6 vs. 50.3 years). A striking increase was seen in the 10-59 age group during the pandemic (P < 0.001). The highest increase was seen in the 20-29 age group, being 18.6 fold. One death was recorded per 58 chest CT scans during the pandemic. Chest CT use was substantially higher at the beginning of the pandemic.

CONCLUSION

Chest CT was excessively used during the COVID-19 pandemic. Young and middle-aged people were exposed more than others. The impact of COVID-19-pandemic-related radiation exposure on public health should be followed carefully in future years.

Evidence of a cognitive bias in the quantification of COVID-19 with CT: an artificial intelligence randomised clinical trial

Chest computed tomography (CT) has played a valuable, distinct role in the screening, diagnosis, and follow-up of COVID-19 patients. The quantification of COVID-19 pneumonia on CT has proven to be an important predictor of the treatment course and outcome of the patient although it remains heavily reliant on the radiologist's subjective perceptions. Here, we show that with the adoption of CT for COVID-19 management, a new type of psychophysical bias has emerged in radiology. A preliminary survey of 40 radiologists and a retrospective analysis of CT data from 109 patients from two hospitals revealed that radiologists overestimated the percentage of lung involvement by 10.23 ± 4.65% and 15.8 ± 6.6%, respectively. In the subsequent randomised controlled trial, artificial intelligence (AI) decision support reduced the absolute overestimation error (P < 0.001) from 9.5% ± 6.6 (No-AI analysis arm, n = 38) to 1.0% ± 5.2 (AI analysis arm, n = 38). These results indicate a human perception bias in radiology that has clinically meaningful effects on the quantitative analysis of COVID-19 on CT. The objectivity of AI was shown to be a valuable complement in mitigating the radiologist's subjectivity, reducing the overestimation tenfold.Trial registration: https://Clinicaltrial.gov . Identifier: NCT05282056, Date of registration: 01/02/2022.

Diagnosis of COVID-19 from X-rays using combined CNN-RNN architecture with transfer learning

Combating the COVID-19 pandemic has emerged as one of the most promising issues in global healthcare. Accurate and fast diagnosis of COVID-19 cases is required for the right medical treatment to control this pandemic. Chest radiography imaging techniques are more effective than the reverse-transcription polymerase chain reaction (RT-PCR) method in detecting coronavirus. Due to the limited availability of medical images, transfer learning is better suited to classify patterns in medical images. This paper presents a combined architecture of convolutional neural network (CNN) and recurrent neural network (RNN) to diagnose COVID-19 patients from chest X-rays. The deep transfer techniques used in this experiment are VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2, where CNN is used to extract complex features from samples and classify them using RNN. In our experiments, the VGG19-RNN architecture outperformed all other networks in terms of accuracy. Finally, decision-making regions of images were visualized using gradient-weighted class activation mapping (Grad-CAM). The system achieved promising results compared to other existing systems and might be validated in the future when more samples would be available. The experiment demonstrated a good alternative method to diagnose COVID-19 for medical staff. 

All the data used during the study are openly available from the Mendeley data repository at https://data.mendeley.com/datasets/mxc6vb7svm. For further research, we have made the source code publicly available at https://github.com/Asraf047/COVID19-CNN-RNN.

Prevalence of reported incidental adrenal findings in chest computerized tomography scans performed during the COVID-19 pandemic in a single center in Northeast Brazil

Objective

We investigated the prevalence of adrenal incidentalomas (AIs) in a nonselected Brazilian population in chest computed tomography (CT) performed during the COVID-19 pandemic.

Materials and methods

This was a retrospective cross-sectional observational study using chest CT reports from a tertiary in- and outpatient radiology clinic from March to September 2020. AIs were defined by changes in the shape, size, or density of the gland initially identified in the released report. Individuals with multiple studies were included, and duplicates were removed. Exams with positive findings were reviewed by a single radiologist.

Results

A total of 10,329 chest CTs were reviewed, and after duplicate removal, 8,207 exams were included. The median age was 45 years [IQR 35-59 years], and 4,667 (56.8%) were female. Thirty-eight lesions were identified in 36 patients (prevalence 0.44%). A higher prevalence was observed with age, with 94.4% of the findings in patients aged 40 years and over (RR 9.98 IC 2.39-41.58, p 0.002), but there was no significant difference between the sexes. Seventeen lesions (44.7%) had more than 10 HU, and five lesions (12.1%) were more than 4 cm.

Conclusion

The prevalence of AIs in an unselected and unreviewed population in a Brazilian clinic is low. The impact on the health system caused by AIs discovered during the pandemic should be small regarding the need for specialized follow-up.

Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans

The main objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on volumetric chest CT scans, which are acquired in different imaging centers using different scanners and technical settings. We demonstrated that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, it performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. More specifically, we extracted the subset of the test images for which the model generated a confident prediction and used the extracted subset along with the training set to retrain and update the benchmark model (the model trained on the initial train set). Finally, we adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a single scanning protocol and standard radiation dose. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model's performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low-dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. This dataset is referred to as the "SPGC-COVID" dataset. The entire test dataset used in this study contains 51 COVID-19, 28 CAP, and 51 Normal cases. Experimental results indicate that our proposed framework performs well on all test sets achieving total accuracy of 96.15% (95%CI: [91.25-98.74]), COVID-19 sensitivity of 96.08% (95%CI: [86.54-99.5]), CAP sensitivity of 92.86% (95%CI: [76.50-99.19]), Normal sensitivity of 98.04% (95%CI: [89.55-99.95]) while the confidence intervals are obtained using the significance level of 0.05. The obtained AUC values (One class vs Others) are 0.993 (95%CI: [0.977-1]), 0.989 (95%CI: [0.962-1]), and 0.990 (95%CI: [0.971-1]) for COVID-19, CAP, and Normal classes, respectively. The experimental results also demonstrate the capability of the proposed unsupervised enhancement approach in improving the performance and robustness of the model when being evaluated on varied external test sets.

The impact of the COVID-19 pandemic on the use of diagnostic imaging examinations in the Brazilian unified healthcare system (SUS)

Objectives

To assess the impact of the COVID-19 pandemic on the volumes of use of diagnostic imaging examinations in the Brazilian Unified Health System (SUS), the only healthcare provider for approximately 160 million people.

Methods

We collected the monthly numbers of diagnostic imaging examinations in the years 2019, 2020, and 2021 from a database provided by SUS. Data were collected by specific type of examination across different imaging modalities, both for the outpatient (elective and emergency) and inpatient settings.

Results

There was a large reduction in the annual volume of almost all types of diagnostic imaging examinations in SUS in 2020, compared to 2019. Decreases were generally greater among outpatients than in the hospital setting, in which the annual volume of use of most modalities was similar or even higher in 2021 than in the pre-pandemic period. Computed tomography (CT) was the only modality for which use increased in 2020 compared to 2019. In contrast to other types of examinations, the use of chest CT was much higher in both 2020 and 2021 than in the preceding years. The relative changes in diagnostic imaging use in SUS started around March-April 2020, when the pandemic began to get worse in Brazil, and tended to correlate to COVID-19 incidence in Brazil over the following months.

Conclusions

The COVID-19 pandemic had a large impact on the use of diagnostic imaging examinations in the SUS. Policies and actions are needed to alleviate the resulting potential adverse health effects and to optimize the use of diagnostic tests in the future.

Risk stratification of hospital admissions for COVID-19 pneumonia by chest radiographic scoring in a Johannesburg tertiary hospital

BACKGROUND

Chest radiographic scoring systems for COVID-19 pneumonia have been developed. However, little is published on the utilityof these scoring systems in low- and middle-income countries.

OBJECTIVES

To perform risk stratification of COVID-19 pneumonia in Johannesburg, South Africa (SA), by comparing the Brixia score withclinical parameters, disease course and clinical outcomes. To assess inter-rater reliability and developing predictive models of the clinicaloutcome using the Brixia score and clinical parameters.

METHODS

Retrospective investigation was conducted of adult participants with established COVID-19 pneumonia admitted at a tertiaryinstitution from 1 May to 30 June 2020. Two radiologists, blinded to clinical data, assigned Brixia scores. Brixia scores were compared withclinical parameters, length of stay and clinical outcomes (discharge/death). Inter-rater agreement was determined. Multivariable logisticregression extracted variables predictive of in-hospital demise.

RESULTS

The cohort consisted of 263 patients, 51% male, with a median age of 47 years (interquartile range (IQR) = 20; 95% confidenceinterval (CI) 46.5 - 49.9). Hypertension (38.4%), diabetes (25.1%), obesity (19.4%) and HIV (15.6%) were the most common comorbidities.The median length of stay for 258 patients was 7.5 days (IQR = 7; 95% CI 8.2 - 9.7) and 6.5 days (IQR = 8; 95% CI 6.5 - 12.5) for intensivecare unit stay. Fifty (19%) patients died, with a median age of 55 years (IQR = 23; 95% CI 50.5 - 58.7) compared with survivors, of medianage 46 years (IQR = 20; 95% CI 45 - 48.6) (p=0.01). The presence of one or more comorbidities resulted in a higher death rate (23% v. 9.2%;p=0.01) than without comorbidities. The median Brixia score for the deceased was higher (14.5) than for the discharged patients (9.0)(p<0.001). Inter-rater agreement for Brixia scores was good (intraclass correlation coefficient 0.77; 95% CI 0.6 - 0.85; p<0.001). A modelcombining Brixia score, age, male gender and obesity (sensitivity 84%; specificity 63%) as well as a model with Brixia score and C-reactiveprotein (CRP) count (sensitivity 81%; specificity 63%) conferred the highest risk for in-hospital mortality.

CONCLUSION

We have demonstrated the utility of the Brixia scoring system in a middle-income country setting and developed the first SArisk stratification models incorporating comorbidities and a serological marker. When used in conjunction with age, male gender, obesityand CRP, the Brixia scoring system is a promising and reliable risk stratification tool. This may help inform the clinical decision pathway inresource-limited settings like ours during future waves of COVID-19.

Echocardiography and Lung Ultrasound in Long COVID and Post-COVID Syndrome, a Review Document of the Austrian Society of Pneumology and the Austrian Society of Ultrasound in Medicine

Lung ultrasound has the potential to enable standardized follow-up without radiation exposure and with lower associated costs in comparison to CT scans. It is a valuable tool to follow up on patients after a COVID-19 infection and evaluate if there is pulmonary fibrosis developing. Echocardiography, including strain imaging, is a proven tool to assess various causes of dyspnea and adds valuable information in the context of long COVID care. Including two-dimensional (2D) strain imaging, a better comprehension of myocardial damage in post-COVID syndrome can be made. Especially 2D strain imaging (left and the right ventricular strain) can provide information about prognosis.

Factors Influencing the Length of Hospital Stay Among Pediatric COVID-19 Patients at Queen Rania Al Abdullah Hospital for Children: A Cross-Sectional Study

Background COVID-19 caused by SARS-CoV-2 is a worldwide epidemic. Children are less commonly infected and have less severe symptoms than adults. However, they are at risk for COVID-19-associated severe sickness and hospitalization. The duration of stay is a major driver of effective health treatment during hospitalization; thus, it is only logical to attempt to comprehend the factors influencing the length of stay (LOS) for these patients, particularly in light of the ongoing pandemic caused by the new SARS-CoV-2 virus. As predictors of hospital LOS, several variables, including age, gender, disease severity, hospital mortality, insurance type, and hospital location, have been discovered. In our study, we focused on the severity of the patient's condition, the presence of comorbidities, and the necessary therapeutic regimen to predict the duration of stay. This study aimed to answer the following questions: If a patient has comorbidity and has COVID-19 requiring hospital treatment, will the patient's comorbidity elongate the duration of stay at the hospital for further management in the pediatric age group? What are the risk factors that play a significant role in the hospital stay duration in pediatrics? Methodology We gathered data from 100 hospitalized children aged up to 14 years who tested positive for COVID-19, which was not specific to variants of SARS-CoV-2, over 24 months (February 2020-February 2022) at Queen Rania Al Abdullah Hospital for Children, one of the Health Care Accreditation Council accredited facilities. Clinical symptoms, signs, oxygen demand, imaging study results, laboratory data, and usage of corticosteroid and antiviral medication were all taken from patients' medical records. There were no limitations in taking the sample of patients. All patients in the duration mentioned were included. Results Clinical data of 100 COVID-19-positive pediatric patients were analyzed; 52% of the patients had associated chronic illnesses, while 48% were medically free. The longest duration of LOS was 28 days, the shortest was one day, the median was eight days, and five days was the most frequent among patients owing to 21% of patients, using mean descriptive statistics. We compared LOS to having or not having comorbidities. The mean LOS of patients with the comorbid disease was 6.15 days, with a maximum of 28 days, while for patients without chronic illnesses, the mean was 4.81 days with a maximum of 14 days. The significance was 0.07. Our results also showed a significant correlation between using steroids and LOS, as it had an advantageous effect by decreasing it with a significance value of 0.04. Having abnormal findings on chest computed tomography (CT) scan was also associated with increased LOS with a significant value of 0.00. Conclusions According to our research, there was no direct association between comorbidity and hospital LOS, which is counterintuitive, as it was influenced by multiplayers of variables such as using steroids, which decreased the LOS, and abnormal findings on chest CT, which resulted in lengthening of the hospital stay. Our findings cannot be proven without further research and a larger patient sample.

Fuzzy-logic-based IoMT framework for COVID19 patient monitoring

Smart healthcare is an integral part of a smart city, which provides real time and intelligent remote monitoring and tracking services to patients and elderly persons. In the era of an extraordinary public health crisis due to the spread of the novel coronavirus (2019-nCoV), which caused the deaths of millions and affected a multitude of people worldwide in different ways, the role of smart healthcare has become indispensable. Any modern method that allows for speedy and efficient monitoring of COVID19-affected patients could be highly beneficial to medical staff. Several smart-healthcare systems based on the Internet of Medical Things (IoMT) have attracted worldwide interest in their growing technical assistance in health services, notably in predicting, identifying and preventing, and their remote surveillance of most infectious diseases. In this paper, a real time health monitoring system for COVID19 patients based on edge computing and fuzzy logic technique is proposed. The proposed model makes use of the IoMT architecture to collect real time biological data (or health information) from the patients to monitor and analyze the health conditions of the infected patients and generates alert messages that are transmitted to the concerned parties such as relatives, medical staff and doctors to provide appropriate treatment in a timely fashion. The health data are collected through sensors attached to the patients and transmitted to the edge devices and cloud storage for further processing. The collected data are analyzed through fuzzy logic in edge devices to efficiently identify the risk status (such as low risk, moderate risk and high risk) of the COVID19 patients in real time. The proposed system is also associated with a mobile app that enables the continuous monitoring of the health status of the patients. Moreover, once alerted by the system about the high risk status of a patient, a doctor can fetch all the health records of the patient for a specified period, which can be utilized for a detailed clinical diagnosis.

[Performance in prognostic capacity and efficiency of the Thoracic Care Suite GE AI tool applied to chest radiography of patients with COVID-19 pneumonia]

OBJECTIVE

Rapid progression of COVID-19 pneumonia may put patients at risk of requiring ventilatory support, such as non-invasive mechanical ventilation or endotracheal intubation. Implementing tools that detect COVID-19 pneumonia can improve the patient's healthcare. We aim to evaluate the efficacy and efficiency of the artificial intelligence (AI) tool GE Healthcare's Thoracic Care Suite (featuring Lunit INSIGHT CXR, TCS) to predict the ventilatory support need based on pneumonic progression of COVID-19 on consecutive chest X-rays.

METHODS

Outpatients with confirmed SARS-CoV-2 infection, with chest X-ray (CXR) findings probable or indeterminate for COVID-19 pneumonia, who required a second CXR due to unfavorable clinical course, were collected. The number of affected lung fields for the two CXRs was assessed using the AI tool.

RESULTS

One hundred fourteen patients (57.4 ± 14.2 years, 65 -57%- men) were retrospectively collected. Fifteen (13.2%) required ventilatory support. Progression of pneumonic extension ≥ 0.5 lung fields per day compared to pneumonia onset, detected using the TCS tool, increased the risk of requiring ventilatory support by 4-fold. Analyzing the AI output required 26 seconds of radiological time.

CONCLUSIONS

Applying the AI tool, Thoracic Care Suite, to CXR of patients with COVID-19 pneumonia allows us to anticipate ventilatory support requirements requiring less than half a minute.

Lung and Infection CT-Scan-Based Segmentation with 3D UNet Architecture and Its Modification

COVID-19 is the disease that has spread over the world since December 2019. This disease has a negative impact on individuals, governments, and even the global economy, which has caused the WHO to declare COVID-19 as a PHEIC (Public Health Emergency of International Concern). Until now, there has been no medicine that can completely cure COVID-19. Therefore, to prevent the spread and reduce the negative impact of COVID-19, an accurate and fast test is needed. The use of chest radiography imaging technology, such as CXR and CT-scan, plays a significant role in the diagnosis of COVID-19. In this study, CT-scan segmentation will be carried out using the 3D version of the most recommended segmentation algorithm for bio-medical images, namely 3D UNet, and three other architectures from the 3D UNet modifications, namely 3D ResUNet, 3D VGGUNet, and 3D DenseUNet. These four architectures will be used in two cases of segmentation: binary-class segmentation, where each architecture will segment the lung area from a CT scan; and multi-class segmentation, where each architecture will segment the lung and infection area from a CT scan. Before entering the model, the dataset is preprocessed first by applying a minmax scaler to scale the pixel value to a range of zero to one, and the CLAHE method is also applied to eliminate intensity in homogeneity and noise from the data. Of the four models tested in this study, surprisingly, the original 3D UNet produced the most satisfactory results compared to the other three architectures, although it requires more iterations to obtain the maximum results. For the binary-class segmentation case, 3D UNet produced IoU scores, Dice scores, and accuracy of 94.32%, 97.05%, and 99.37%, respectively. For the case of multi-class segmentation, 3D UNet produced IoU scores, Dice scores, and accuracy of 81.58%, 88.61%, and 98.78%, respectively. The use of 3D segmentation architecture will be very helpful for medical personnel because, apart from helping the process of diagnosing someone with COVID-19, they can also find out the severity of the disease through 3D infection projections.

The use of lung ultrasound in COVID-19

This review article addresses the role of lung ultrasound in patients with coronavirus disease 2019 (COVID-19) for diagnosis and disease management. As a simple imaging procedure, lung ultrasound contributes to the early identification of patients with clinical conditions suggestive of COVID-19, supports decisions about hospital admission and informs therapeutic strategy. It can be performed in various clinical settings (primary care facilities, emergency departments, hospital wards, intensive care units), but also in outpatient settings using portable devices. The article describes typical lung ultrasound findings for COVID-19 pneumonia (interstitial pattern, pleural abnormalities and consolidations), as one component of COVID-19 diagnostic workup that otherwise includes clinical and laboratory evaluation. Advantages and limitations of lung ultrasound use in COVID-19 are described, along with equipment requirements and training needs. To infer on the use of lung ultrasound in different regions, a literature search was performed using key words "COVID-19", "lung ultrasound" and "imaging". Lung ultrasound is a noninvasive, rapid and reproducible procedure; can be performed at the point of care; requires simple sterilisation; and involves non-ionising radiation, allowing repeated exams on the same patient, with special benefit in children and pregnant women. However, physical proximity between the patient and the ultrasound operator is a limitation in the current pandemic context, emphasising the need to implement specific infection prevention and control measures. Availability of qualified staff adequately trained to perform lung ultrasound remains a major barrier to lung ultrasound utilisation. Training, advocacy and awareness rising can help build up capacities of local providers to facilitate lung ultrasound use for COVID-19 management, in particular in low- and middle-income countries.

Fusing clinical and image data for detecting the severity level of hospitalized symptomatic COVID-19 patients using hierarchical model

Purpose

Based on medical reports, it is hard to find levels of different hospitalized symptomatic COVID-19 patients according to their features in a short time. Besides, there are common and special features for COVID-19 patients at different levels based on physicians’ knowledge that make diagnosis difficult. For this purpose, a hierarchical model is proposed in this paper based on experts’ knowledge, fuzzy C-mean (FCM) clustering, and adaptive neuro-fuzzy inference system (ANFIS) classifier.

Methods

Experts considered a special set of features for different groups of COVID-19 patients to find their treatment plans. Accordingly, the structure of the proposed hierarchical model is designed based on experts’ knowledge. In the proposed model, we applied clustering methods to patients’ data to determine some clusters. Then, we learn classifiers for each cluster in a hierarchical model. Regarding different common and special features of patients, FCM is considered for the clustering method. Besides, ANFIS had better performances than other classification methods. Therefore, FCM and ANFIS were considered to design the proposed hierarchical model. FCM finds the membership degree of each patient’s data based on common and special features of different clusters to reinforce the ANFIS classifier. Next, ANFIS identifies the need of hospitalized symptomatic COVID-19 patients to ICU and to find whether or not they are in the end-stage (mortality target class). Two real datasets about COVID-19 patients are analyzed in this paper using the proposed model. One of these datasets had only clinical features and another dataset had both clinical and image features. Therefore, some appropriate features are extracted using some image processing and deep learning methods.

Results

According to the results and statistical test, the proposed model has the best performance among other utilized classifiers. Its accuracies based on clinical features of the first and second datasets are 92% and 90% to find the ICU target class. Extracted features of image data increase the accuracy by 94%.

Conclusion

The accuracy of this model is even better for detecting the mortality target class among different classifiers in this paper and the literature review. Besides, this model is compatible with utilized datasets about COVID-19 patients based on clinical data and both clinical and image data, as well.

Highlights

• A new hierarchical model is proposed using ANFIS classifiers and FCM clustering method in this paper. Its structure is designed based on experts’ knowledge and real medical process. FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients.

• Two real datasets about COVID-19 patients are studied in this paper. One of these datasets has both clinical and image data. Therefore, appropriate features are extracted based on its image data and considered with available meaningful clinical data. Different levels of hospitalized symptomatic COVID-19 patients are considered in this paper including the need of patients to ICU and whether or not they are in end-stage.

• Well-known classification methods including case-based reasoning (CBR), decision tree, convolutional neural networks (CNN), K-nearest neighbors (KNN), learning vector quantization (LVQ), multi-layer perceptron (MLP), Naive Bayes (NB), radial basis function network (RBF), support vector machine (SVM), recurrent neural networks (RNN), fuzzy type-I inference system (FIS), and adaptive neuro-fuzzy inference system (ANFIS) are designed for these datasets and their results are analyzed for different random groups of the train and test data;

• According to unbalanced utilized datasets, different performances of classifiers including accuracy, sensitivity, specificity, precision, F-score, and G-mean are compared to find the best classifier. ANFIS classifiers have the best results for both datasets.

• To reduce the computational time, the effects of the Principal Component Analysis (PCA) feature reduction method are studied on the performances of the proposed model and classifiers. According to the results and statistical test, the proposed hierarchical model has the best performances among other utilized classifiers.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s42600-023-00268-w.

Radiology During the COVID-19 Pandemic: Mapping Radiology Literature in 2020

OBJECTIVES

Our aim was to assess articles published in the field of radiology, nuclear medicine, and medical imaging in 2020 and analyze the linkage of radiology-related topics with coronavirus disease 2019 (COVID-19) through literature mapping along with a bibliometric analysis for publications.

METHODS

We performed a search on the Web of Science Core Collection database for articles in the field of radiology, nuclear medicine, and medical imaging published in 2020. We analyzed the included articles using VOS viewer software, where we analyzed the co-occurrence of keywords, representing major topics discussed. Of the resulting topics, a literature map was created and linkage analysis was done.

RESULTS

A total of 24,748 articles were published in the field of radiology, nuclear medicine, and medical imaging in 2020. We found a total of 61,267 keywords; only 78 keywords occurred more than 250 times. COVID-19 had 449 occurrences, 29 links, with a total link strength of 271. MRI was the topic most commonly appearing in 2020 radiology publications, while "computed tomography" had the highest linkage strength with COVID-19, with a linkage strength of 149, representing 54.98% of the total COVID-19 linkage strength, followed by "radiotherapy, and "deep and machine learning". The top cited paper had a total of 1,687 citations. Nine out of the 10 most cited articles discussed COVID-19 and included "COVID-19" or "coronavirus" in their title, including the top cited paper.

CONCLUSION

While MRI was the topic that dominated, CT had the highest linkage strength with COVID-19 and represented the topic of top cited articles in 2020 radiology publications.

Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network

Covid-19 has become a worldwide epidemic which has caused the death of millions in a very short time. This disease, which is transmitted rapidly, has mutated and different variations have emerged. Early diagnosis is important to prevent the spread of this disease. In this study, a new deep learning-based architecture is proposed for rapid detection of Covid-19 and other symptoms using CT and X-ray chest images. This method, called CovidDWNet, is based on a structure based on feature reuse residual block (FRB) and depthwise dilated convolutions (DDC) units. The FRB and DDC units efficiently acquired various features in the chest scan images and it was seen that the proposed architecture significantly improved its performance. In addition, the feature maps obtained with the CovidDWNet architecture were estimated with the Gradient boosting (GB) algorithm. With the CovidDWNet+GB architecture, which is a combination of CovidDWNet and GB, a performance increase of approximately 7% in CT images and between 3% and 4% in X-ray images has been achieved. The CovidDWNet+GB architecture achieved the highest success compared to other architectures, with 99.84% and 100% accuracy rates, respectively, on different datasets containing binary class (Covid-19 and Normal) CT images. Similarly, the proposed architecture showed the highest success with 96.81% accuracy in multi-class (Covid-19, Lung Opacity, Normal and Viral Pneumonia) X-ray images and 96.32% accuracy in the dataset containing X-ray and CT images. When the time to predict the disease in CT or X-ray images is examined, it is possible to say that it has a high speed because the CovidDWNet+GB method predicts thousands of images within seconds.

A lightweight network for COVID-19 detection in X-ray images

The Novel Coronavirus 2019 (COVID-19) is a global pandemic which has a devastating impact. Due to its quick transmission, a prominent challenge in confronting this pandemic is the rapid diagnosis. Currently, the commonly-used diagnosis is the specific molecular tests aided with the medical imaging modalities such as chest X-ray (CXR). However, with the large demand, the diagnoses of CXR are time-consuming and laborious. Deep learning is promising for automatically diagnosing COVID-19 to ease the burden on medical systems. At present, the most applied neural networks are large, which hardly satisfy the rapid yet inexpensive requirements of COVID-19 detection. To reduce huge computation and memory demands, in this paper, we focus on implementing lightweight networks for COVID-19 detection in CXR. Concretely, we first augment data based on clinical visual features of CXR from expertise. Then, according to the fact that all the input data are CXR, we design a targeted four-layer network with either 11 × 11 or 3 × 3 kernels to recognize regional features and detail features. A pruning criterion based on the weights importance is also proposed to further prune the network. Experiments on a public COVID-19 dataset validate the effectiveness and efficiency of the proposed method.

Relation between Cytokine Levels and Pulmonary Dysfunction in COVID-19 Patients: A Case-Control Study

Aim: The study aimed to assess the relationships between serum cytokine levels and pulmonary dysfunctions in individuals with COVID-19. These correlations may help to suggest strategies for prevention and therapies of coronavirus disease. Patients and methods: Fifty healthy participants and one hundred COVID-19 patients participated in this study. COVID-19 participants were subdivided into moderate and severe groups based on the severity of their symptoms. In both patients and healthy controls, white blood cells (WBCs) and lymphocytes counts and serum C-reactive protein (CRP), interleukin (IL)-1, IL-4, IL-6, IL-18, and IL-35 levels were estimated. All the patients were examined by chest computed tomography (CT) and the COVID-19 Reporting and Data System (CO-RADS) score was assessed. Results: All COVID-19 patients had increased WBCs count and CRP, IL-1β, IL-4, IL-6, IL-18, and IL-35 serum levels than healthy controls. Whereas WBCs, CRP, and cytokines like IL-6 showed significantly higher levels in the severe group as compared to moderate patients, IL-4, IL-35, and IL-18 showed comparable levels in both disease groups. Lymphocytes count in all patient groups exhibited a significant decrease as compared to the heathy controls and it was significantly lower in severe COVID-19 than moderate. Furthermore, CO-RADS score was positively connected with WBCs count as well as CRP and cytokine (IL-35, IL-18, IL-6, IL-4 and IL-1β) levels in both groups. CO-RADS score, also demonstrated a positive correlation with lymphocytes count in the moderate COVID-19 patients, whereas it demonstrated a negative correlation in the severe patients. The receiver operator characteristic (ROC) curve analysis indicated that IL-1β, IL-4, IL-18, and IL-35 were fair (acceptable) predictors for COVID-19 in moderate cases. Whereas IL-6 was good predictor of COVID-19 in severe cases (AUC > 0.800), IL-18 and IL-35 were fair. Conclusion: Severe COVID-19 patients, compared to individuals with moderate illness and healthy controls, had lower lymphocyte counts and increased CRP with greater WBCs counts. In contrast to moderate COVID-19 patients, severe COVID-19 patients had higher levels of IL-6, but IL-4, IL-18, and IL-35 between both illness categories were at close levels. IL-6 level was the most potent predictor of COVID-19 progress and severity. CO-RADS 5 was the most frequent category in both moderate and severe cases. Patients with a typical CO-RADS involvement had a higher CRP and cytokine (IL-1β, IL-6, IL-4, IL-18, and IL-35) levels and WBCs count with a lower lymphocyte number than the others. Cytokine and CRP levels as well as WBCs and lymphocyte counts were considered surrogate markers of severe lung affection and pneumonia in COVID 19 patients.

Clinical Characteristics and Management of Patients with a Suspected COVID-19 Infection in Emergency Departments: A European Retrospective Multicenter Study

Background: Our aim is to describe and compare the profile and outcome of patients attending the ED with a confirmed COVID-19 infection with patients with a suspected COVID-19 infection. Methods: We conducted a multicentric retrospective study including adults who were seen in 21 European emergency departments (ED) with suspected COVID-19 between 9 March and 8 April 2020. Patients with either a clinical suspicion of COVID-19 or confirmed COVID-19, detected using either a RT-PCR or a chest CT scan, formed the C+ group. Patients with non-confirmed COVID-19 (C− group) were defined as patients with a clinical presentation in the ED suggestive of COVID-19, but if tests were performed, they showed a negative RT-PCR and/or a negative chest CT scan. Results: A total of 7432 patients were included in the analysis: 1764 (23.7%) in the C+ group and 5668 (76.3%) in the C− group. The population was older (63.8 y.o. ±17.5 vs. 51.8 y.o. +/− 21.1, p < 0.01), with more males (54.6% vs. 46.1%, p < 0.01) in the C+ group. Patients in the C+ group had more chronic diseases. Half of the patients (n = 998, 56.6%) in the C+ group needed oxygen, compared to only 15% in the C− group (n = 877). Two-thirds of patients from the C+ group were hospitalized in ward (n = 1128, 63.9%), whereas two-thirds of patients in the C− group were discharged after their ED visit (n = 3883, 68.5%). Conclusion: Our study was the first in Europe to examine the emergency department’s perspective on the management of patients with a suspected COVID-19 infection. We showed an overall more critical clinical situation group of patients with a confirmed COVID-19 infection.

Correlation of chest CT severity score with clinical parameters in COVID-19 pulmonary disease in a tertiary care hospital in Delhi during the pandemic period

Background

Since November 2019, the rapid outbreak of coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern. COVID-19 disease is caused by a new variant of coronavirus, named as ‘severe acute respiratory syndrome coronavirus 2.’ Chest CT has a potential role in the diagnosis, detection of complications and in predicting clinical recovery of patients or progression of coronavirus disease 2019. Degree and severity of lung involvement can be assessed by 25 point CT severity score. This quantification plays an important role to modify the treatment plan at times in critically ill patient of COVID-19. Hence, the purpose of present study was to describe and quantify the severity of COVID-19 infection on chest computed tomography (CT) by 25-point CT severity score and to determine the relationship of CT severity score with clinical and laboratory parameters.

Results

A total of 150 patients with COVID-19 disease were assessed. Mean age of the study group was 54.46 years (62.7% males and 37.3% females). The most common comorbidity present in the study group was diabetes mellitus, which was present in 17.3% cases. Severity of disease was significantly associated with age of the patient. CT severity score was positively correlated with lymphopenia and raised CRP, D-dimer and serum ferritin levels. A significant statistical correlation was found between CT severity grade and patient survival.

Conclusions

This is a large comprehensive study, collecting data from 150 cases of COVID-19 pneumonia patients, in a tertiary care hospital in India to describe the correlation of CT severity score with clinical land laboratory parameters. Chest CT severity score correlates well with laboratory parameters and can aid in predicting COVID-19 disease outcome.

Imaging in the Intensive Care Unit

Radiology plays an important role in the management of the most seriously ill patients in the hospital. Over the years, continued advances in imaging technology have contributed to an improvement in patient care. However, even with such advances, the portable chest radiograph (CXR) remains one of the most commonly requested radiographic examinations. While they provide valuable information, CXRs remain relatively insensitive at revealing abnormalities and are often nonspecific. Chest computed tomography (CT) can display findings that are occult on CXR and is particularly useful at identifying and characterizing pleural effusions, detecting barotrauma including small pneumothoraces, distinguishing pneumonia from atelectasis, and revealing unsuspected or additional abnormalities which could result in increased morbidity and mortality if left untreated. CT pulmonary angiography is the modality of choice in the evaluation of pulmonary emboli which can complicate the hospital course of the ICU patient. This article will provide guidance for interpretation of CXR and thoracic CT images, discuss some of the invasive devices routinely used, and review the radiologic manifestations of common pathologic disease states encountered in ICU patients. In addition, imaging findings and complications of more specific clinical scenarios in which the incidence has increased in the ICU setting, such as patients who are immunocompromised, have interstitial lung disease, or COVID-19, will also be discussed. Communication between the radiologist and intensivist, particularly on complicated cases, is important to help increase diagnostic accuracy and leads to an improvement in the management of the most critically ill patients.

Analysis of CT signs, radiomic features and clinical characteristics for delta variant COVID-19 patients with different vaccination status

OBJECTIVE

To explore the characteristics of peripheral blood, high resolution computed tomography (HRCT) imaging and the radiomics signature (RadScore) in patients infected with delta variant virus under different coronavirus disease (COVID-19) vaccination status.

METHODS

123 patients with delta variant virus infection collected from November 1, 2021 to March 1, 2022 were analyzed retrospectively. According to COVID-19 vaccination Status, they were divided into three groups: Unvaccinated group, partially vaccinated group and full vaccination group. The peripheral blood, chest HRCT manifestations and RadScore of each group were analyzed and compared.

RESULTS

The mean lymphocyte count 1.22 ± 0.49 × 10^⁹/L, CT score 7.29 ± 3.48, RadScore 0.75 ± 0.63 in the unvaccinated group; The mean lymphocyte count 1.55 ± 0.70 × 10^⁹/L, CT score 5.27 ± 2.72, RadScore 1.03 ± 0.46 in the partially vaccinated group; The mean lymphocyte count 1.87 ± 0.70 × 10^9/L, CT score 3.59 ± 3.14, RadScore 1.23 ± 0.29 in the fully vaccinated group. There were significant differences in lymphocyte count, CT score and RadScore among the three groups (all p < 0.05); Compared with the other two groups, the lung lesions in the unvaccinated group were more involved in multiple lobes, of which 26 cases involved the whole lung.

CONCLUSIONS

Through the analysis of clinical features, pulmonary imaging features and radiomics, we confirmed the positive effect of COVID-19 vaccine on pulmonary inflammatory symptoms and lymphocyte count (immune system) during delta mutant infection.

Dark-field chest X-ray imaging for the assessment of COVID-19-pneumonia

Background

Currently, alternative medical imaging methods for the assessment of pulmonary involvement in patients infected with COVID-19 are sought that combine a higher sensitivity than conventional (attenuation-based) chest radiography with a lower radiation dose than CT imaging.

Methods

Sixty patients with COVID-19-associated lung changes in a CT scan and 40 subjects without pathologic lung changes visible in the CT scan were included (in total, 100, 59 male, mean age 58 ± 14 years). All patients gave written informed consent. We employed a clinical setup for grating-based dark-field chest radiography, obtaining both a dark-field and a conventional attenuation image in one image acquisition. Attenuation images alone, dark-field images alone, and both displayed simultaneously were assessed for the presence of COVID-19-associated lung changes on a scale from 1 to 6 (1 = surely not, 6 = surely) by four blinded radiologists. Statistical analysis was performed by evaluation of the area under the receiver–operator-characteristics curves (AUC) using Obuchowski’s method with a 0.05 level of significance.

Results

We show that dark-field imaging has a higher sensitivity for COVID-19-pneumonia than attenuation-based imaging and that the combination of both is superior to one imaging modality alone. Furthermore, a quantitative image analysis shows a significant reduction of dark-field signals for COVID-19-patients.

Conclusions

Dark-field imaging complements and improves conventional radiography for the visualisation and detection of COVID-19-pneumonia.

Computed tomography (CT) imaging uses X-rays to obtain images of the inside of the body. It is used to look at lung damage in patients with COVID-19. However, CT imaging exposes the patient to a considerable amount of radiation. As radiation exposure can lead to the development of cancer, exposure should be minimised. Conventional plain X-ray imaging uses lower amounts of radiation but lacks sensitivity. We used dark-field chest X-ray imaging, which also uses low amounts of radiation, to assess the lungs of patients with COVID-19. Radiologists identified pneumonia in patients more easily from dark-field images than from usual plain X-ray images. We anticipate dark-field X-ray imaging will be useful to follow-up patients suspected of having lung damage.

Frank, Gassert et al. use dark-field chest X-ray imaging to assess COVID-19-pneumonia. Dark-field imaging has a higher sensitivity for COVID-19-pneumonia than attenuation-based imaging and provides an ultralow dose alternative to computed tomography imaging for that purpose.