Teleultrasound has gained significant traction in clinical practice in recent years. However, studies focusing on remote interventional ultrasound remain limited.

To evaluate the feasibility and accuracy of percutaneous puncture using a robot-assisted teleultrasound-guided interventional system (RTIS).

This study was approved by the institutional animal ethics committee and human research review board. Written informed consent was obtained from all patients. Two experienced interventional ultrasound physicians performed percutaneous punctures using both RTIS and conventional ultrasound guidance (CUG) in phantom and swine liver models, as well as in clinical settings. Puncture distance errors and operation durations were compared between the RTIS and CUG groups in the experimental models. For clinical applications, operation duration, success rates, and complications were recorded.

No significant differences were observed in puncture distance errors between the RTIS and CUG groups in the phantom study (2.85 ± 2.07 mm vs. 1.79 ± 1.93 mm; p = 0.158) or the swine liver study (3.28 ± 1.20 mm vs. 2.56 ± 0.98 mm; p = 0.148). However, puncture operation durations were significantly longer in the RTIS group compared to the CUG group across all scenarios: phantom study (50 ± 19 s vs. 19 ± 7 s; p < 0.001), swine liver study (106 ± 19 s vs. 61 ± 32 s; p = 0.001), and clinical application (200 ± 27.02 s vs. 104.8 ± 33.92 s; p < 0.001). All six patients in the RTIS group and ten patients in the CUG group successfully underwent percutaneous puncture without complications.

The RTIS demonstrated safety and feasibility for percutaneous puncture, providing comparable accuracy to conventional methods.

The RTIS offers a safe and effective solution for percutaneous puncture, with the potential to address the scarcity of medical resources in remote and underserved regions.

Post-COVID-19 lung disease (PCLD) is a significant concern following the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. PCLD encompasses persistent debilitating respiratory symptoms and radiological changes beyond the acute disease phase. It highlights the ongoing search to identify and manage lingering diseases. This prospective study utilizes F18-Fludeoxyglucose (FDG) PET/CT to identify residual inflammatory lung lesions in PCLD. Treatment response was assessed after anti-inflammatory and antifibrotic therapies.

Thirty patients post-severe COVID-19 pneumonia enrolled. They underwent baseline 18F-FDG PET/CT scans to unveil residual lung inflammation lesions on FDG and CT. They received antifibrotic (Pirfenidone) and anti-inflammatory (Methylprednisolone) drugs for 6-12 weeks. They were followed up for clinical, biochemical, and imaging treatment responses.

Baseline 18F-FDG PET/CT revealed ongoing lung inflammation in all PCLD (mean SUVmax: 3.8 ± 2.3 and number of segments: 8±3 ). The mean CT severity score was 17.7 ± 3.4 with moderate (n = 16) or severe (n = 14) disease involvement. Mild, moderate, and severe 18F-FDG PET/CT categories were noted in the 8, 14, and 8 patients, respectively. Following treatment, a PET scan showed a significant decrease in disease extent (segments) and severity (FDG uptake) and an improvement in disease grading on imaging (97% of patients). In PET concordance, there was a significant clinical and radiological improvement with a fall in inflammatory markers (p < 0.005). Serum Ferritin and total leukocyte counts were significantly associated with PCLD severity on 18F-FDG PET/CT(p < 0.05).

This prospective study identifies and quantifies ongoing significant residual lung inflammation in PCLD on 18F-FDG PET/CT. Anti-inflammatory and antifibrotic drug therapy led to clinical and radiological improvement. 18F-FDG PET/CT as a non-invasive biomarker helped manage and follow up PCLD patients.

Not applicable.

The utility of lung ultrasound (LUS) in evaluation of coronavirus disease (COVID-19) with pneumonia has not yet been elucidated. The main objective of study is to determine whether LUS can effectively predict the prognosis in intensive care unit (ICU), including mortality and disease severity. It's also aimed to determine whether LUS will provide a threshold value to predict mortality in COVID-19 cases. In this prospective observational study, 90 patients admitted to the ICU with COVID-19 pneumonia and respiratory failure were included. A LUS cutoff score of 21 on admission demonstrated sensitivity of 97% and specificity of 68% for predicting mortality. Baseline LUS scores were found to be significantly higher in nonsurvivor group(P < .001) whereas APACHE II, sequential organ failure assessment (SOFA), charlson comorbidity index (CCI), nutrition risk in critically ill (NUTRIC) scores, serum lactate, procalcitonin, ferritin, D-dimer levels and heart rate were also significantly found to be higher in nonsurvivor group(P < .05). Overall mean progression-free-survival (PFS) rate was significantly longer in patients with LUS scores < 21, (mean-survival 23.8 days) compared to those with LUS scores ≥ 21 (mean-survival 12.5 days) (P < .05). Multivariate Cox-regression analysis identified a LUS score ≥ 21 was an independent risk factor for mortality during ICU stay (P = .002). LUS performed at ICU admission can serve as a prognostic indicator for patients with COVID-19 pneumonia. By identifying high-risk groups and monitoring these patients closely using LUS, healthcare providers may enhance resource utilization and potentially improve patient outcomes.

This study introduces a novel hybrid framework for classifying COVID-19 severity using chest X-rays (CXR) and computed tomography (CT) scans by integrating Vision Transformers (ViT) with metaheuristic optimization techniques. The framework employs the Grey Wolf Optimizer (GWO) for hyperparameter tuning and Particle Swarm Optimization (PSO) for feature selection, leveraging the ViT model's self-attention mechanism to extract global and local image features crucial for severity classification. A multi-phase classification strategy refines predictions by progressively distinguishing normal, mild, moderate, and severe COVID-19 cases. The proposed GWO_ViT_PSO_MLP model achieves outstanding accuracy, with 99.14% for 2-class CXR classification and 98.89% for 2-class CT classification, outperforming traditional CNN-based approaches such as ResNet34 (84.22%) and VGG19 (93.24%). Furthermore, it demonstrates superior performance in multi-class severity classification, especially in differentiating challenging cases like mild and moderate infections. Compared to existing studies, this framework significantly improves accuracy and computational efficiency, highlighting its potential as a scalable and reliable solution for automated COVID-19 severity detection in clinical applications.

The use of lung ultrasound (LUS) has recently become vital in the diagnosis and prognosis of various respiratory diseases. Its role in COVID-19 requires further investigation.

Twenty-five consecutive, non-ICU hospitalized COVID-19 patients were included. LUS was performed on admission and sequentially every 3 days at 8 points in the chest. Based on the LUS findings a score was designed. Logarithmic regression models and ROC curve analysis were applied.

A statistically significant positive correlation was found between LUS score at admission and the severity of SARS-COV-2 infection. Higher LUS score was significantly associated with lower PaO2/FiO2 ratio, use of HFNC, longer hospitalization and greater extent of chest CT infiltrates. A significant association between LUS score and risk of death or intubation or HFNC was found. For one point of increase in the score, risk of death or intubation or HFNC increased 1.93-fold (95% CI 1.02 to 3.65). The predictive role of the score was very satisfactory (area under the ROC curve = 0.87).

Lung ultrasound findings were significantly positively associated with clinical and radiological markers of severity of SARS-CoV-2 pneumonia. It therefore constitutes a promising and reliable technique for assessing pneumonia, comparable to chest CT.

The prognostic role of computed tomography (CT)-defined skeletal muscle features in COVID-19 is still under investigation. The aim of the present study was to evaluate the prognostic role of CT-defined skeletal muscle area and density in patients with COVID-19 in a multicenter setting.

This retrospective study is a part of the German multicenter project RACOON (Radiological Cooperative Network of the COVID-19 pandemic). The acquired sample included 1379 patients, 389 (28.2%) women and 990 (71.8%) men. In each case, chest CT was analyzed and pectoralis muscle area and density were calculated. Data were analyzed by means of descriptive statistics. Group differences were calculated using the Mann-Whitney-U test and Fisher's exact test. Univariable and multivariable logistic regression analyses were performed.

The 30-day mortality was 17.9%. Using median values as thresholds, low pectoralis muscle density (LPMD) was a strong and independent predictor of 30-day mortality, HR=2.97, 95%-CI: 1.52-5.80, p=0.001. Also in male patients, LPMD predicted independently 30-day mortality, HR=2.96, 95%-CI: 1.42-6.18, p=0.004. In female patients, the analyzed pectoralis muscle parameters did not predict 30-day mortality. For patients under 60 years of age, LPMD was strongly associated with 30-day mortality, HR=2.72, 95%-CI: 1.17;6.30, p=0.019. For patients over 60 years of age, pectoralis muscle parameters could not predict 30-day mortality.

In male patients with COVID-19, low pectoralis muscle density is strongly associated with 30-day mortality and can be used for risk stratification. In female patients with COVID-19, pectoralis muscle parameters cannot predict 30-day mortality.

Objectives: Predicting intensive care unit (ICU) admissions during pandemic outbreaks such as COVID-19 can assist clinicians in early intervention and the better allocation of medical resources. Artificial intelligence (AI) tools are promising for this task, but their development can be hindered by the limited availability of training data. This study aims to explore model development strategies in data-limited scenarios, specifically in detecting the need for ICU admission using chest X-rays of COVID-19 patients by leveraging transfer learning and data extension to improve model performance. Methods: We explored convolutional neural networks (CNNs) pre-trained on either natural images or chest X-rays, fine-tuning them on a relatively limited dataset (COVID-19-NY-SBU, n = 899) of lung-segmented X-ray images for ICU admission classification. To further address data scarcity, we introduced a dataset extension strategy that integrates an additional dataset (MIDRC-RICORD-1c, n = 417) with different but clinically relevant labels. Results: The TorchX-SBU-RSNA and ELIXR-SBU-RSNA models, leveraging X-ray-pre-trained models with our training data extension approach, enhanced ICU admission classification performance from a baseline AUC of 0.66 (56% sensitivity and 68% specificity) to AUCs of 0.77-0.78 (58-62% sensitivity and 78-80% specificity). The gradient-weighted class activation mapping (Grad-CAM) analysis demonstrated that the TorchX-SBU-RSNA model focused more precisely on the relevant lung regions and reduced the distractions from non-relevant areas compared to the natural image-pre-trained model without data expansion. Conclusions: This study demonstrates the benefits of medical image-specific pre-training and strategic dataset expansion in enhancing the model performance of imaging AI models. Moreover, this approach demonstrates the potential of using diverse but limited data sources to alleviate the limitations of model development for medical imaging AI. The developed AI models and training strategies may facilitate more effective and efficient patient management and resource allocation in future outbreaks of infectious respiratory diseases.

Objectives: We aimed to describe the cardiopulmonary function during exercise and the health-related quality of life (HRQoL) in patients with a history of COVID-19 pneumonia, stratified by chest computed tomography (CT) findings at baseline. Methods: Among 77 consecutive patients with COVID-19 who were discharged from the Pulmonology Ward between March 2020 and April 2021, 28 (mean age 54.3 ± 8.6 years, 8 females) agreed to participate to the current study. The participants were analyzed in two groups based on pulmonary involvement (PI) at baseline chest CT applying a threshold of 25%. A consequent artificial intelligence (AI)-guided total opacity score (TOS) was calculated in a subgroup of 22 patients. A cardiopulmonary exercise test (CPET) was conducted on average 8.4 (±1.9) months after discharge from the hospital. HRQoL was defined using the short-form (SF-36) questionnaire. The primary outcome was exercise intolerance that was defined as a peak oxygen uptake (V'O2peak) < 80% predicted. Secondary outcomes were ventilatory limitation, defined as breathing reserve < 15%, circulatory limitation, defined as oxygen pulse predicted below 80%, and deconditioning, defined as exercise intolerance in the absence of ventilatory and circulatory limitations. Other secondary outcomes included the SF-36 domains. Results: In all, 15 patients had at least 25% PI (53.6%) at baseline chest CT. Exercise intolerance was observed in ten patients (35.7%), six due to deconditioning and four due to circulatory limitation; none had ventilatory limitation. AI-guided TOS was 30.1 ± 24.4% vs. 6.1 ± 4.8% (p < 0.001) at baseline, and 1.7 ± 3.0% vs. 0.2 ± 0.7% (nonsignificant) at follow-up in high and low PI groups, respectively. The physical functioning (PF) domain score of the SF-36 questionnaire was 66.3 ± 19.4 vs. 85.0 ± 13.1 in high and low PI groups, respectively (p = 0.007). Other SF-36 domains did not differ significantly between the groups. A high PI at baseline was inversely correlated with V'O2peak (standardized β coefficient = -0.436; 95% CI -26.1; -0.7; p = 0.040) and with PF scores (standardized β coefficient -0.654; 95% CI -41.3; -7.6; p = 0.006) adjusted for age, sex, body mass index and lung diffusion capacity. Conclusions: One-third of participants experienced exercise intolerance eight months after COVID-19 pneumonia. A higher PI at baseline was significantly associated with exercise intolerance and PF. Notwithstanding, the radiological PI was resolved, and the exercise intolerance was mainly explained not by ventilatory limitation but by circulatory limitation and deconditioning.

This study evaluates the prognostic significance of pleural effusion (PE) in COVID-19 patients across thirteen centers in Germany, aiming to clarify its role in predicting clinical outcomes.

In this retrospective analysis within the RACOON project (Radiological Cooperative Network of the COVID-19 pandemic), 1183 patients (29.3 % women, 70.7 % men) underwent chest CT to assess PE. We investigated PE's association with 30-day mortality, ICU admission, and the need for mechanical ventilation.

PE was detected in 31.5 % of patients, showing a significant correlation with 30-day mortality (47.5 % in non-survivors vs. 27.3 % in survivors, p < 0.001), with a hazard ratio of 2.22 (95 % CI 1.65-2.99, p < 0.001). No significant association was found between PE volume or density and mortality. ICU admissions were noted in 46.8 % of patients, while mechanical ventilation was required for 26.7 %.

Pleural effusion is present in a significant portion of COVID-19 patients and independently predicts increased 30-day mortality, underscoring its value as a prognostic marker. Its identification, irrespective of volume or density, should be a priority in radiological reports to guide clinical decision-making.

Tube thoracostomy, utilized through conventional methodologies in the context of pleural disorders such as pleural effusion and pneumothorax, constitutes one of the primary therapeutic interventions. Nonetheless, it is imperative to recognize that invasive procedures, including tube thoracostomy, are classified as aerosol-generating activities during the management of pleural conditions in patients afflicted with COVID-19, thus raising substantial concerns regarding the potential exposure of healthcare personnel to the virus. The objective of this investigation was to assess the SARS-CoV-2 viral load by detecting viral RNA in pleural drainage specimens from patients who underwent tube thoracostomy due to either pleural effusion or pneumothorax.

In this single-center prospective cross-sectional analysis, a real-time reverse transcriptase (RT) polymerase chain reaction (PCR) assay was employed to conduct swab tests for the qualitative identification of nucleic acid from SARS-CoV-2 in pleural fluids acquired during tube thoracostomy between August 2021 and December 2021.

All pleural drainage specimens from 21 patients who tested positive for COVID-19 via nasopharyngeal PCR, of which 14 underwent tube thoracostomy due to pneumothorax, 4 due to both pneumothorax and pleural effusion, and 3 due to pleural effusion, were found to be negative for SARS-CoV-2 RNA. Moreover, individuals exhibiting pleural effusion were admitted to the intensive care unit with a notably higher incidence, yet demonstrated significantly more radiological anomalies in patients diagnosed with pneumothorax.

The current findings, inclusive of the results from this study, do not furnish scientific evidence to support the notion that SARS-CoV-2 is transmitted via aerosolization during tube thoracostomy, and it remains uncertain whether the virus can be adequately contained within pleural fluids.

Objectives: To investigate the relationship between COVID-19 pneumonia outcomes and three chest CT analysis approaches. Methods: Patients with COVID-19 pneumonia who underwent chest CT were included and divided into survivors/non-survivors and intubated/not-intubated. Chest CTs were analyzed through a (1) Total Severity Score visually quantified by an emergency (TSS1) and a thoracic radiologist (TSS2); (2) density mask technique quantifying normal parenchyma (DM_Norm 1) and ground glass opacities (DM_GGO1) repeated after the manual delineation of consolidations (DM_Norm2, DM_GGO2, DM_Consolidation); (3) texture analysis quantifying normal parenchyma (TA_Norm) and interstitial lung disease (TA_ILD). Association with outcomes was assessed through Chi-square and the Mann-Whitney test. The TSS inter-reader variability was assessed through intraclass correlation coefficient (ICC) and Bland-Altman analysis. The relationship between quantitative variables and outcomes was investigated through multivariate logistic regression analysis. Variables correlation was investigated using Spearman analysis. Results: Overall, 192 patients (mean age, 66.8 ± 15.4 years) were included. TSS was significantly higher in intubated patients but only TSS1 in survivors. TSS presented an ICC of 0.83 (0.76; 0.88) and a bias (LOA) of 1.55 (-4.69, 7.78). DM_Consolidation showed the greatest median difference between survivors/not survivors (p = 0.002). The strongest independent predictor for mortality was DM_Consolidation (AUC 0.688), while the strongest independent predictor for the intensity of care was TSS2 (0.7498). DM_Norm 2 was the singular feature independently associated with both the outcomes. DM_GGO1 strongly correlated with TA_ILD (ρ = 0.977). Conclusions: The DM technique and TA achieved consistent measurements and a better correlation with patient outcomes.

Introduction. The lung CT images of COVID-19 patients can be typically characterized by three different findings- Ground Glass Opacity (GGO), consolidation and pleural effusion. GGOs have been shown to precede consolidations and has different heterogeneous appearance. Conventional severity scoring only uses total area of lung involvement ignoring appearance of the effected regions. This study proposes a baseline to select heterogeneity/radiomic features that can distinguish these three pathological lung findings.Methods. Four approaches were implemented to select features from a pool of 44 features. First one is a manual feature selection method. The rest are automatic feature selection methods based on Genetic Algorithm (GA) coupled with (1) K-Nearest-Neighbor (GA-KNN), (2) binary-decision-tree (GA-BDT) and (3) Artificial-Neural-Network (GA-ANN). For the purpose of validation, an ANN was trained using the selected features and tested on a completely independent data set.Results. Manual selection of nine radiomic features was found to provide the most accurate results with the highest sensitivity, specificity and accuracy (85.7% overall accuracy and 0.90 area under receiver operating characteristic curve) followed by GA-BDT, GA-KNN and GA-ANN (accuracy 78%, 77.5% and 76.8%).Conclusion. Manually selected nine radiomic features can be used in accurate severity scoring allowing the clinician to plan for more effective personalized treatment. They can also be useful for monitoring the progression of COVID-19 and response to therapy for clinical trials.

Federated learning (FL) has attracted increasing attention to building models without accessing raw user data, especially in healthcare. In real applications, different federations can seldom work together due to possible reasons such as data heterogeneity and distrust/inexistence of the central server. In this article, we propose a novel framework called MetaFed to facilitate trustworthy FL between different federations. MetaFed obtains a personalized model for each federation without a central server via the proposed cyclic knowledge distillation. Specifically, MetaFed treats each federation as a meta distribution and aggregates knowledge of each federation in a cyclic manner. The training is split into two parts: common knowledge accumulation and personalization. Comprehensive experiments on seven benchmarks demonstrate that MetaFed without a server achieves better accuracy compared with state-of-the-art methods [e.g., 10%+ accuracy improvement compared with the baseline for physical activity monitoring dataset (PAMAP2)] with fewer communication costs. More importantly, MetaFed shows remarkable performance in real-healthcare-related applications.

Chest CT imaging holds a major role in the diagnosis of lung diseases, many of which affect the peribronchovascular region. Identification and categorization of peribronchovascular abnormalities on CT imaging can assist in formulating a differential diagnosis and directing further diagnostic evaluation.

The peribronchovascular region of the lung encompasses the pulmonary arteries, airways, and lung interstitium. Understanding disease processes associated with structures of the peribronchovascular region and their appearances on CT imaging aids in prompt diagnosis. This article reviews current knowledge in anatomic and pathologic features of the lung interstitium composed of intercommunicating prelymphatic spaces, lymphatics, collagen bundles, lymph nodes, and bronchial arteries; diffuse lung diseases that present in a peribronchovascular distribution; and an approach to classifying diseases according to patterns of imaging presentations. Lung peribronchovascular diseases can appear on CT imaging as diffuse thickening, fibrosis, masses or masslike consolidation, ground-glass or air space consolidation, and cysts, acknowledging that some diseases may have multiple presentations.

A category approach to peribronchovascular diseases on CT imaging can be integrated with clinical features as part of a multidisciplinary approach for disease diagnosis.

Although chest X-ray is commonly used to diagnose COVID-19 pneumonia, few studies have explored findings in pediatric patients. This study aimed to reveal chest X-ray characteristics in children with COVID-19 pneumonia and compare between non-severe and severe cases.

This multicenter, nationwide retrospective study included all children aged 0 to 15 years who were admitted to 13 medical facilities throughout Thailand with COVID-19 pneumonia between January 2020 and October 2021. We analyzed the demographics, clinical features, and chest X-ray results of these children, and compared differences between the non-severe and severe groups.

During the study period, 1018 children (52% male, median age 5 years) were admitted with COVID-19 pneumonia. Most chest radiographic findings showed bilateral (51%) patchy/ground glass opacities (61%) in the central area (64%). Only 12% of the children exhibited typical classification for COVID-19 pneumonia, whereas 74% of chest radiographs were categorized as indeterminate. Comorbidities including chronic lung diseases [adjusted OR (95%CI): 14.56 (3.80-55.75), P-value <0.001], cardiovascular diseases [adjusted OR (95%CI): 7.54 (1.44-39.48), P-value 0.017], genetic diseases [adjusted OR (95%CI): 28.39 (4.55-177.23), P-value <0.001], clinical dyspnea [adjusted OR (95%CI): 12.13 (5.94-24.77), P-value <0.001], tachypnea [adjusted OR (95%CI): 3.92 (1.79-8.55), P-value 0.001], and bilateral chest X-ray infiltrations [adjusted OR (95%CI): 1.99 (1.05-3.78), P-value 0.036] were factors associated with severe COVID-19 pneumonia.

Most children with COVID-19 pneumonia had indeterminate chest X-rays according to the previous classification. We suggest using chest X-rays in conjunction with clinical presentation to screen high-risk patients for early detection of COVID-19 pneumonia.

A hydropneumothorax is an uncommon complication of pneumonia, particularly in pediatric patients, and typically arises secondary to conditions such as malignancies, esophageal-pleural fistula, thoracic trauma, or thoracocentesis. While pneumothorax is rarely reported in adults with COVID-19 and is even less common in children, isolated cases have been noted in those with Multisystem Inflammatory Syndrome in Children (MIS-C). A recent alert has also been issued about increased Group A Streptococcus (GAS) infections in Europe. Against this background, the primary aim of this case report is to describe a rare and severe complication of pneumonia in a previously healthy child with MIS-C and a positive throat culture for GAS.

Background and Objectives: The aging process has always been associated with a higher susceptibility to chronic inflammatory lung diseases. Several studies have demonstrated the gut microbiome's influence on the lungs through cross-talk or the gut-lungs axis maintaining nutrient-rich microenvironments. Taiwan djulis (Chenopodium formosanum Koidz.) provides antioxidant and anti-inflammatory characteristics that could modulate the gut microbiome. This could induce the gut-lung axis through microbial cross-talk, thus favoring the modulation of lung inflammation. Materials and Methods: Here, we investigate the immune mRNA expression in the spleen, fecal microbiome composition, and hyperplasia of the bronchial epithelium in aged 2-year-old BALB/c mice after 60 days of supplementation of djulis. Results: The pro-inflammatory cytokines IFN-γ, TNF-α, and IL-1β, T; cells CD4 and CD8; and TLRs TLR3, TLR4, TLR5, TLR7, TLR8, and TLR9 were reduced in their mRNA expression levels, while the anti-inflammatory cytokines IL-2, IL-4, and IL-10 were highly expressed in the C. formosanum-treated group. Interestingly, the fecal microbiome composition analysis indicated higher diversity in the C. formosanum-treated group and the presence of butyrate-producing bacteria that are beneficial in the gut microbiome. The histopathology showed reduced hyperplasia of the bronchial epithelium based on the degree of lesions. Conclusions: Our findings suggest that Taiwan djulis can modulate the gut microbiome, leading to microbial cross-talk; reducing the mRNA expression of pro-inflammatory cytokines, T cells, and TLRs; and increasing anti-inflammatory cytokines in the spleen, as cytokines migrate in the lungs, preventing lung inflammation damage in aged mice or the gut-lung axis. Thus, Taiwan djulis could be considered a beneficial dietary component for the older adult population. The major limitation includes a lack of protein validation of cytokines and TLRs and quantification of the T cell population in the spleen as a marker of the gut-lung axis.

Long COVID is defined as persistency of symptoms, such as exertional dyspnea, twelve weeks after recovery from SARS-CoV-2 infection.

To investigate ventilatory efficiency by the use of cardiopulmonary exercise testing (CPET) in patients with exertional dyspnea despite normal basal spirometry after 18 (T18) and 36 months (T36) from COVID-19 pneumonia.

One hundred patients with moderate-critical COVID-19 were prospectively enrolled in our Long COVID program. Medical history, physical examination and lung high-resolution computed tomography (HRCT) were obtained at hospitalization (T0), 3 (T3) and 15 months (T15). All HRCTs were revised using a semi-quantitative CT severity score (CSS). Pulmonary function tests were obtained at T3 and T15. CPET was performed in a subset of patients with residual dyspnea (mMRC ≥ 1), at T18 and at T36.

Remarkably, at CPET, ventilatory efficiency was reduced both at T18 (V'E/V'CO2 slope = 31.4±3.9 SD) and T36 (V'E/V'CO2 slope = 31.28±3.70 SD). Furthermore, we identified positive correlations between V'E/V'CO2 slope at T18 and T36 and both percentage of involvement and CSS at HRCT at T0, T3 and T15. Also, negative linear correlations were found between V'E/V'CO2 slope at T18 and T36 and DLCO at T3 and T15.

At eighteen months from COVID-19 pneumonia, 20 % of subjects still complains of exertional dyspnea. At CPET this may be explained by persistently reduced ventilatory efficiency, possibly related to the degree of lung parenchymal involvement in the acute phase of infection, likely reflecting a damage in the pulmonary circulation.

COVID-19 poses a global health crisis, necessitating precise diagnostic methods for timely containment. However, accurately delineating COVID-19-affected regions in lung CT scans is challenging due to contrast variations and significant texture diversity. In this regard, this study introduces a novel two-stage classification and segmentation CNN approach for COVID-19 lung radiological pattern analysis. A novel Residual-BRNet is developed to integrate boundary and regional operations with residual learning, capturing key COVID-19 radiological homogeneous regions, texture variations, and structural contrast patterns in the classification stage. Subsequently, infectious CT images undergo lesion segmentation using the newly proposed RESeg segmentation CNN in the second stage. The RESeg leverages both average and max-pooling implementations to simultaneously learn region homogeneity and boundary-related patterns. Furthermore, novel pixel attention (PA) blocks are integrated into RESeg to effectively address mildly COVID-19-infected regions. The evaluation of the proposed Residual-BRNet CNN in the classification stage demonstrates promising performance metrics, achieving an accuracy of 97.97%, F1-score of 98.01%, sensitivity of 98.42%, and MCC of 96.81%. Meanwhile, PA-RESeg in the segmentation phase achieves an optimal segmentation performance with an IoU score of 98.43% and a dice similarity score of 95.96% of the lesion region. The framework's effectiveness in detecting and segmenting COVID-19 lesions highlights its potential for clinical applications.

Pulmonary infections contribute substantially to emergency department (ED) visits, posing a considerable health burden. Lower respiratory tract infections are prevalent, particularly among the elderly, constituting a significant percentage of infectious disease-related ED visits. Timely recognition and treatment are crucial to mitigate morbidity and mortality. Imaging studies, primarily chest radiographs and less frequently CT chests, play a pivotal role in diagnosis. This article aims to elucidate the imaging patterns of both common and rare pulmonary infections (bacterial and viral) in the post COVID-19 era, emphasizing the importance of recognizing distinct radiological manifestations. The integration of clinical and microbiological evidence aids in achieving accurate diagnoses, and guiding optimal therapeutic interventions. Despite potential overlapping manifestations, a nuanced understanding of radiological patterns, coupled with comprehensive clinical and microbiological information, enhances diagnostic precision in majority cases.

This review explores imaging's crucial role in acute Coronavirus Disease 2019 (COVID-19) assessment. High Resolution Computer Tomography is especially effective in detection of lung abnormalities. Chest radiography has limited utility in the initial stages of COVID-19 infection. Lung Ultrasound has emerged as a valuable, radiation-free tool in critical care, and Magnetic Resonance Imaging shows promise as a Computed Tomography alternative. Typical and atypical findings of COVID-19 by each of these modalities are discussed with emphasis on their prognostic value. Considerations for pediatric and immunocompromised cases are outlined. A comprehensive diagnostic approach is recommended, as radiological diagnosis remains challenging in the acute phase.

When RT-PCR is ineffective in early diagnosis and understanding of COVID-19 severity, Computed Tomography (CT) scans are needed for COVID diagnosis, especially in patients having high ground-glass opacities, consolidations, and crazy paving. Radiologists find the manual method for lesion detection in CT very challenging and tedious. Previously solo deep learning (SDL) was tried but they had low to moderate-level performance. This study presents two new cloud-based quantized deep learning UNet3+ hybrid (HDL) models, which incorporated full-scale skip connections to enhance and improve the detections.

Annotations from expert radiologists were used to train one SDL (UNet3+), and two HDL models, namely, VGG-UNet3+ and ResNet-UNet3+. For accuracy, 5-fold cross-validation protocols, training on 3,500 CT scans, and testing on unseen 500 CT scans were adopted in the cloud framework. Two kinds of loss functions were used: Dice Similarity (DS) and binary cross-entropy (BCE). Performance was evaluated using (i) Area error, (ii) DS, (iii) Jaccard Index, (iii) Bland-Altman, and (iv) Correlation plots.

Among the two HDL models, ResNet-UNet3+ was superior to UNet3+ by 17 and 10% for Dice and BCE loss. The models were further compressed using quantization showing a percentage size reduction of 66.76, 36.64, and 46.23%, respectively, for UNet3+, VGG-UNet3+, and ResNet-UNet3+. Its stability and reliability were proved by statistical tests such as the Mann-Whitney, Paired t-Test, Wilcoxon test, and Friedman test all of which had a p < 0.001.

Full-scale skip connections of UNet3+ with VGG and ResNet in HDL framework proved the hypothesis showing powerful results improving the detection accuracy of COVID-19.

Reverse transcription polymerase chain reaction (RT-PCR) is the main technique used to identify COVID-19 from respiratory samples. It has been suggested in several articles that chest CTs could offer a possible alternate diagnostic tool for COVID-19; however, no professional medical body recommends using chest CTs as an early COVID-19 detection modality. This literature review examines the use of CT scans as a diagnostic tool for COVID-19.

A comprehensive search of research works published in peer-reviewed journals was carried out utilizing precisely stated criteria. The search was limited to English-language publications, and studies of COVID-19-positive patients diagnosed using both chest CT scans and RT-PCR tests were sought. For this review, four databases were consulted: these were the Cochrane and ScienceDirect catalogs, and the CINAHL and Medline databases made available by EBSCOhost.

In total, 285 possibly pertinent studies were found during an initial search. After applying inclusion and exclusion criteria, six studies remained for analysis. According to the included studies, chest CT scans were shown to have a 44 to 98% sensitivity and 25 to 96% specificity in terms of COVID-19 diagnosis. However, methodological limitations were identified in all studies included in this review.

RT-PCR is still the suggested first-line diagnostic technique for COVID-19; while chest CT is adequate for use in symptomatic patients, it is not a sufficiently robust diagnostic tool for the primary screening of COVID-19.

This study aims to automatically analyze and extract abnormalities in the lung field due to Coronavirus Disease 2019 (COVID-19). Types of abnormalities that can be detected are Ground Glass Opacity (GGO) and consolidation. The proposed method can also identify the location of the abnormality in the lung field, that is, the central and peripheral lung area. The location and type of these abnormalities affect the severity and confidence level of a patient suffering from COVID-19. The detection results using the proposed method are compared with the results of manual detection by radiologists. From the experimental results, the proposed system can provide an average error of 0.059 for the severity score and 0.069 for the confidence level. This method has been implemented in a web-based application for general users.•A method to detect the appearance of viral pneumonia imaging features, namely Ground Glass Opacity (GGO) and consolidation on the chest Computed Tomography (CT) scan images.•This method can separate the lung field to the right lung and the left lung, and it also can identify the detected imaging feature's location in the central or peripheral of the lung field.•Severity level and confidence level of the patient's suffering are measured.

Primary cardiac tumors are a rarity, and sarcomas emerge as the prevailing form of primary malignant cardiac tumors across age groups, encompassing both children and adults. Within this category, angiosarcoma stands out, constituting around 31% of all primary malignant cardiac tumors. Primary cardiac angiosarcoma displays a notably aggressive nature, characterized by early systemic metastasis, and is accompanied by a generally unfavorable prognosis. We describe a case concerning a previously healthy teenage girl who displayed persistent constitutional symptoms and hemoptysis for 15 days. Subsequent investigation uncovered alveolar hemorrhage, ultimately linked to a cardiac angiosarcoma. The difficulty in this instance arose from the vague nature of the initial symptoms, posing a challenge to promptly and accurately diagnose the condition. This case highlights the aggressive nature of primary cardiac angiosarcoma. The vague initial symptoms underscore the need for early detection and optimized treatment to improve the generally unfavorable prognosis associated with this condition. Increased awareness and a multidisciplinary approach are crucial in addressing the diagnostic and therapeutic challenges posed by primary cardiac angiosarcoma.

We systematically reviewed radiological abnormalities in patients with prolonged SARS-CoV-2 infection, defined as persistently positive polymerase chain reaction (PCR) results for SARS-CoV-2 for > 21 days, with either persistent or relapsed symptoms. We extracted data from 24 patients (median age, 54.5 [interquartile range, 44-64 years]) reported in the literature and analyzed their representative CT images based on the timing of the CT scan relative to the initial PCR positivity. Our analysis focused on the patterns and distribution of CT findings, severity scores of lung involvement on a scale of 0-4, and the presence of migration. All patients were immunocompromised, including 62.5% (15/24) with underlying lymphoma and 83.3% (20/24) who had received anti-CD20 therapy within one year. Median duration of infection was 90 days. Most patients exhibited typical CT appearance of coronavirus disease 19 (COVID-19), including ground-glass opacities with or without consolidation, throughout the follow-up period. Notably, CT severity scores were significantly lower during ≤ 21 days than during > 21 days (P < 0.001). Migration was observed on CT in 22.7% (5/22) of patients at ≤ 21 days and in 68.2% (15/22) to 87.5% (14/16) of patients at > 21 days, with rare instances of parenchymal bands in previously affected areas. Prolonged SARS-CoV-2 infection usually presents as migrating typical COVID-19 pneumonia in immunocompromised patients, especially those with impaired B-cell immunity.

Air leak syndrome, including pneumomediastinum (PM), pneumopericardium, pneumothorax, or subcutaneous emphysema, is primarily caused by chest trauma, cardiothoracic surgery, esophageal perforation, and mechanical ventilation. Secondary pneumomediastinum (SP) is a rare complication, with a much lower incidence reported in patients with coronavirus disease 2019 (COVID-19). Our patient was a 44-year-old nonsmoker male with a previous history of obesity (Body Mass Index [BMI] 35 kg/m2), hyperthyroidism, hypokinetic cardiopathy and atrial fibrillation in treatment with flecainide, who presented to the emergency department with 6 days of fever, cough, dyspnea, and respiratory distress. The COVID-19 diagnosis was confirmed based on a polymerase chain reaction (PCR) test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). After initiation of mechanical ventilation, a chest computed tomography (CT) on the first day revealed bilateral multifocal ground-glass opacities, consolidation and an extensive SP and pneumoperitoneum. Our therapeutic strategy was initiation of veno-venous extracorporeal membrane oxygenation (VV-ECMO) as a bridge to recovery after positioning 2 drains (mediastinal and pleural), for both oxygenation and carbon dioxide clearance, to allow protective and ultra-protective ventilation to limit ventilator-induced lung injury (VILI) and the intensity of mechanical power for lung recovery. After another chest CT scan which showed a clear reduction of the PM, 2 pronation and neuromuscular relaxation cycles were also required, with improvement of gas exchange and respiratory mechanics. On the 15th day, lung function recovered and the patient was then weaned from VV-ECMO, and ultimately made a good recovery and was discharged. In conclusion, SP may be a reflection of extensive alveolar damage and should be considered as a potential predictive factor for adverse outcome in critically ill SARS-CoV2 patients.

To evaluate the correlation between dry eye symptoms and coronavirus disease 2019 (COVID-19) infection and to assess the real-time reverse transcription-polymerase chain reaction (RT‒PCR) of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) from the conjunctival swab.

A prospective observational case series study was conducted of all suspected and confirmed COVID-19 patients from Dr. Cipto Mangunkusumo Hospital (RSCM) and the Universitas Indonesia Hospital (RSUI). On the first day of the visit (day 0), systemic clinical symptoms and naso-oropharyngeal (NO) RT‒PCR results will classify all subjects as non-, suspected, or confirmed (mild, moderate, and severe) COVID-19. In all patients, we determined the dry eye symptoms based on the Ocular Surface Disease Index (OSDI) and followed up 7(day 7) and 14 days (day 14) after the first visit. When it was technically possible, we also examined the objective dry eye measurements: tear meniscus height (TMH), noninvasive Keratograph® break-up time (NIKBUT), and ocular redness. Additionally, we took conjunctival swab samples for SARS-CoV-2 RT-PCR in all patients.

The OSDI scores for 157 patients decreased across days 0, 7, and 14 (median (interquartile range): 2.3 (0-8), 0 (0-3), and 0 (0-0), p value < 0.0001 (D0 vs D14). The moderate-severe COVID-19 group had a higher OSDI score than the other groups at median D0 (15.6 vs 0-2.3), p value < 0.0001 and this pattern was consistently seen at follow-up D7 and D14. However, dry eye complaints were not correlated with the three objective dry eye measurements in mild-moderate COVID-19 patients. NO RT‒PCR results were positive in 32 (20.4%) patients, namely, 13 and 19 moderate-severe and mild COVID-19 patients, respectively. Positive RT‒PCR results were observed in 7/157 (4.5%) conjunctival swab samples from 1 in non-COVID-19 group and 6 in mild group.

In the early phase of infection, COVID-19 patients experience dry eye symptoms, which have no correlation with objective dry eye measurements. SARS-CoV-2 in conjunctival swab samples can be detected in patients with normal-to-mild COVID-19, which shows the risk of ocular transmission.

Body composition on computed tomography can predict prognosis in patients with COVID-19. The reported data are based on small retrospective studies. The aim of the present study was to analyze the prognostic relevance of skeletal muscle parameter derived from chest computed tomography for prediction of 30-d mortality in patients with COVID-19 in a multicenter setting.

The clinical databases of three centers were screened for patients with COVID-19 between 2020 and 2022. Overall, 447 patients (142 female; 31.7%) were included into the study. The mean age at the time of computed tomography acquisition was 63.8 ± 14.7 y and median age was 65 y. Skeletal muscle area and skeletal muscle density were defined on level T12 of the chest.

Overall, 118 patients (26.3%) died within the 30-d observation period. Of the patient sample, 255 patients (57.0%) were admitted to an intensive care unit and 122 patients needed mechanical ventilation (27.3%). The mean skeletal muscle area of all patients was 96.1 ± 27.2 cm² (range = 23.2-200.7 cm²). For skeletal muscle density, the mean was 24.3 ± 11.1 Hounsfield units (range = -5.6 to 55.8 Hounsfield units). In survivors, the mean skeletal muscle density was higher compared with the lethal cases (mean 25.8 ± 11.2 versus 20.1 ± 9.6; P < 0.0001). Presence of myosteatosis was independently associated with 30-d mortality: odds ratio = 2.72 (95% CI, 1.71-4.32); P = 0.0001.

Myosteatosis is strongly associated with 30-d mortality in patients COVID-19. Patients with COVID-19 with myosteatosis should be considered a risk group.

The performance of chest x-ray (CXR) features for Pneumocystis pneumonia (PCP) diagnosis has been evaluated in small studies. We conducted a systematic review and meta-analysis to describe CXR changes in adults with HIV-associated laboratory-confirmed PCP, comparing these with non-PCP respiratory disease.

We searched databases for studies reporting CXR changes in people >15 years old with HIV and laboratory-confirmed PCP and those with non-PCP respiratory disease. CXR features were grouped using consensus terms. Proportions were pooled and odds ratios (ORs) generated using random-effects meta-analysis, with subgroup analyses by CD4 count, study period, radiology review method, and study region.

Fifty-one studies (with 1821 PCP and 1052 non-PCP cases) were included. Interstitial infiltrate (59%; 95% CI, 52%-66%; 36 studies, n = 1380; I2 = 85%) and ground-glass opacification (48%; 95% CI, 15%-83%; 4 studies, n = 57; I2 = 86%) were common in PCP. Cystic lesions, central lymphadenopathy, and pneumothorax were infrequent. Pleural effusion was rare in PCP (0%; 95% CI, 0%-2%). Interstitial infiltrate (OR, 2.3; 95% CI, 1.4-3.9; I2 = 60%), interstitial-alveolar infiltrate (OR, 10.2; 95% CI, 3.2-32.4; I2 = 0%), and diffuse CXR changes (OR, 7.3; 95% CI, 2.7-20.2; I2 = 87%) were associated with PCP diagnosis. There was loss of association with alveolar infiltrate in African studies.

Diffuse CXR changes and interstitial-alveolar infiltrates indicate a higher likelihood of PCP. Pleural effusion, lymphadenopathy, and focal alveolar infiltrates suggest alternative causes. These findings could be incorporated into clinical algorithms to improve diagnosis of HIV-associated PCP.

Patients with COVID-19 may experience a lung injury without presenting clinical symptoms. Early detection of lung injury in patients with COVID-19 is required to enhance prediction and prevent severe progression.

Lung lesions in patients with COVID-19 were defined using the Fleischner Society terminology. Chest computed tomography lesions and their correlation with demographic characteristics and medical variables were identified.

Patients with mild and moderate COVID-19 had up to 45% lung injuries, whereas critical patients had 55%. However, patients with mild and moderate COVID-19 typically had low-level lung injuries. Ground-glass (68.1%), consolidation (48.8%), opacity (36.3%), and nodular (6.9%) lung lesions were the most prevalent in patients with COVID-19. Patients with COVID-19 infected with the Delta variant had worse lung injury than those infected with the Alpha and Omicron. People vaccinated with ≥2 doses showed a lower risk of lung injury than those vaccinated with <1 dose. Patients <18 years old were less likely to have a lung injury than patients >18 years old. The treatment outcomes were unaffected by the severity of the lung injury.

Patients with mild COVID-19 had a similar risk of lung injury as patients with severe COVID-19. Thus, using chest computed tomography to detect lung injury can enhance the treatment outcomes and reduce the patient's risk of pulmonary complications.

Milk is renowned for its nutritional richness but also serves as a remarkable reservoir of bioactive compounds, particularly milk proteins and their derived peptides. Recent studies have showcased several robust antiviral activities of these proteins, evidencing promising potential within zoonotic viral diseases. While several publications focus on milk's bioactivities, antiviral peptides remain largely neglected in reviews. This knowledge is critical for identifying novel research directions and analyzing potential nutraceuticals within the One Health context. Our review aims to gather the existing scientific information on milk-derived antiviral proteins and peptides against several zoonotic viral diseases, and their possible mechanisms. Overall, in-depth research has increasingly revealed them as a promising and novel strategy against viruses, principally for those constituting a plausible pandemic threat. The underlying mechanisms of the bioactivity of milk's proteins include inhibiting viral entry and attachment to the host cells, blocking replication, or even viral inactivation via peptide-membrane interactions. Their marked versatility and effectiveness stand out compared to other antiviral peptides and can support future research and development in the post-COVID-19 era. Overall, our review helps to emphasize the importance of potentially effective milk-derived peptides, and their significance for veterinary and human medicines, along with the pharmaceutical, nutraceutical, and dairy industry.

Since the outbreak of COVID-19, efforts have been made towards semi-quantitative analysis of lung ultrasound (LUS) data to assess the patient's condition. Several methods have been proposed in this regard, with a focus on frame-level analysis, which was then used to assess the condition at the video and prognostic levels. However, no extensive work has been done to analyze lung conditions directly at the video level. This study proposes a novel method for video-level scoring based on compression of LUS video data into a single image and automatic classification to assess patient's condition. The method utilizes maximum, mean, and minimum intensity projection-based compression of LUS video data over time. This enables to preserve hyper- and hypo-echoic data regions, while compressing the video down to a maximum of three images. The resulting images are then classified using a convolutional neural network (CNN). Finally, the worst predicted score given among the images is assigned to the corresponding video. The results show that this compression technique can achieve a promising agreement at the prognostic level (81.62%), while the video-level agreement remains comparable with the state-of-the-art (46.19%). Conclusively, the suggested method lays down the foundation for LUS video compression, shifting from frame-level to direct video-level analysis of LUS data.

To investigate the detection of subsolid nodules (SSNs) on chest CT scans of outpatients before and during the COVID-19 pandemic, as well as to correlate the imaging findings with epidemiological data. We hypothesized that (pre)malignant nonsolid nodules were underdiagnosed during the COVID-19 pandemic because of an overlap of imaging findings between SSNs and COVID-19 pneumonia.

This was a retrospective study including all chest CT scans performed in adult outpatients (> 18 years of age) in September of 2019 (i.e., before the COVID-19 pandemic) and in September of 2020 (i.e., during the COVID-19 pandemic). The images were reviewed by a thoracic radiologist, and epidemiological data were collected from patient-filled questionnaires and clinical referrals. Regression models were used in order to control for confounding factors.

A total of 650 and 760 chest CT scans were reviewed for the 2019 and 2020 samples, respectively. SSNs were found in 10.6% of the patients in the 2019 sample and in 7.9% of those in the 2020 sample (p = 0.10). Multiple SSNs were found in 23 and 11 of the patients in the 2019 and 2020 samples, respectively. Women constituted the majority of the study population. The mean age was 62.8 ± 14.8 years in the 2019 sample and 59.5 ± 15.1 years in the 2020 sample (p < 0.01). COVID-19 accounted for 24% of all referrals for CT examination in 2020.

Fewer SSNs were detected on chest CT scans of outpatients during the COVID-19 pandemic than before the pandemic, although the difference was not significant. In addition to COVID-19, the major difference between the 2019 and 2020 samples was the younger age in the 2020 sample. We can assume that fewer SSNs will be detected in a population with a higher proportion of COVID-19 suspicion or diagnosis.

Computer-aided drug design has been employed to get the medicinal effects against Corona virus from different pyridine derivatives after synthesizing the new compounds. Additionally, various computational studies are also employed between the newly prepared pyridine derivatives and three controls against three proteins (6Y2E, 6M71 and 6M3M). Different methods were employed to synthesize new pyridine derivatives according to the literature using different reaction mediums. MTT was performed for cytotoxicity study and IC50 for inhibitory concentration. Additionally, in-silico studies including DFT, molecular docking, molecular dynamics, MMPBSA, ADME, pharmacokinetics and Lipinski rules were evaluated. The chemical structures of all new compounds were elucidated based on spectroscopic investigation. A molecular docking study demonstrated that compounds 5, 11, and 12 have the best binders of the SARS-CoV-2 main protease enzyme, with energy scores of - 7.5 kcal/mol, - 7.2 kcal/mol, and - 7.9 kcal/mol, respectively. The net binding energy values of the 11-Mpro, 12-Mpro, and 5-Mpro complexes revealed their highly stable nature in terms of both intermolecular interactions and docked conformation across the simulation time. ADME properties, besides the pharmacokinetics and Lipinski rules, showed that all seven newly synthesized compounds follow Lipinski rules with high GI absorption. The In Vitro antiviral study against SARS-CoV-2 using MTT methods confirms that compound 5 has more potential and is safer than other tested compounds. The study shows that the newly synthesized pyridine derivatives have medicinal properties against SARS-CoV-2 without violating Lipinski rules. Compounds 5, 11, and 12, particularly compound 5, may serve as promising potential candidate for COVID-19.

Imaging has been demonstrated to play a crucial role in both the diagnosis and management of COVID-19. Depending on resources, pre-test probability, and risk factors for severe disease progression, real-time polymerase chain reaction (RT-PCR) testing may be followed by chest radiography (CXR) or chest computed tomography (CT) to further aid in diagnosis or excluding COVID-19 disease. SARS-CoV-2 has been shown not only to pathologically impact the pulmonary system, but also the cardiovascular, gastrointestinal, and neurological systems to name a few. Imaging has again proven useful in further investigating and managing extrapulmonary disease, with the use of echocardiogram, CT angiography of the cardiovascular and cerebrovascular structures, MRI of the brain, as well as ultrasound of the abdomen and CT of the abdomen and pelvis proving particularly useful. Research in artificial intelligence and its application in the diagnosis of COVID-19 and disease severity prediction is underway, and point-of-care ultrasound is an emerging bedside technique that may allow for more efficient and timely diagnosis of COVID-19.

Since the first few cases of pneumonia attributed to infection with the highly contagious novel coronavirus 2 (SARs-CoV-2) were detected in Wuhan, China, in December 2019, imaging has proven an invaluable diagnostic tool throughout the resulting global pandemic. This review describes the imaging features of severe pulmonary disease caused by SARs-CoV-2, named COVID-19 by the World Health Organization (WHO), particularly focussing on computed tomography (CT). CT plays an important role in understanding the pathology behind the progression of disease, as well as helping to identify the potential complications of COVID-19 pneumonia and recognising possible alternative or concurrent diagnoses. This review also focusses on follow-up imaging of survivors of COVID-19, which continues to contribute substantially to our understanding of the longer-term pulmonary changes in patients who have survived severe COVID-19 pneumonia.

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.