Comparison between original SARS-CoV-2 strain and omicron variant on thin-section chest CT imaging of COVID-19 pneumonia.

OBJECTIVES: We investigated different computed tomography (CT) features between Omicron-variant and original-strain SARS-CoV­2 pneumonia to facilitate the clinical management. MATERIALS AND METHODS: Medical records were retrospectively reviewed to select patients with original-strain SARS-CoV­2 pneumonia from February 22 to April 22, 2020, or Omicron-variant SARS-CoV­2 pneumonia from March 26 to May 31, 2022. Data on the demographics, comorbidities, symptoms, clinical types, and CT features were compared between the two groups. RESULTS: There were 62 and 78 patients with original-strain or Omicron-variant SARS-CoV­2 pneumonia, respectively. There were no differences between the two groups in terms of age, sex, clinical types, symptoms, and comorbidities. The main CT features differed between the two groups (p = 0.003). There were 37 (59.7%) and 20 (25.6%) patients with ground-glass opacities (GGO) in the original-strain and Omicron-variant pneumonia, respectively. A consolidation pattern was more frequently observed in the Omicron-variant than original-strain pneumonia (62.8% vs. 24.2%). There was no difference in crazy-paving pattern between the original-strain and Omicron-variant pneumonia (16.1% vs. 11.6%). Pleural effusion was observed more often in Omicron-variant pneumonia, while subpleural lesions were more common in the original-strain pneumonia. The CT score in the Omicron-variant group was higher than that in the original-strain group for critical-type (17.00, 16.00-18.00 vs. 16.00, 14.00-17.00, p = 0.031) and for severe-type (13.00, 12.00-14.00 vs 12.00, 10.75-13.00, p = 0.027) pneumonia. CONCLUSION: The main CT finding of the Omicron-variant SARS-CoV­2 pneumonia included consolidations and pleural effusion. By contrast, CT findings of original-strain SARS-CoV­2 pneumonia showed frequent GGO and subpleural lesions, but without pleural effusion. The CT scores were also higher in the critical and severe types of Omicron-variant than original-strain pneumonia.

Attention-augmented U-Net (AA-U-Net) for semantic segmentation.

Deep learning-based image segmentation models rely strongly on capturing sufficient spatial context without requiring complex models that are hard to train with limited labeled data. For COVID-19 infection segmentation on CT images, training data are currently scarce. Attention models, in particular the most recent self-attention methods, have shown to help gather contextual information within deep networks and benefit semantic segmentation tasks. The recent attention-augmented convolution model aims to capture long range interactions by concatenating self-attention and convolution feature maps. This work proposes a novel attention-augmented convolution U-Net (AA-U-Net) that enables a more accurate spatial aggregation of contextual information by integrating attention-augmented convolution in the bottleneck of an encoder-decoder segmentation architecture. A deep segmentation network (U-Net) with this attention mechanism significantly improves the performance of semantic segmentation tasks on challenging COVID-19 lesion segmentation. The validation experiments show that the performance gain of the attention-augmented U-Net comes from their ability to capture dynamic and precise (wider) attention context. The AA-U-Net achieves Dice scores of 72.3% and 61.4% for ground-glass opacity and consolidation lesions for COVID-19 segmentation and improves the accuracy by 4.2% points against a baseline U-Net and 3.09% points compared to a baseline U-Net with matched parameters. Supplementary Information: The online version contains supplementary material available at 10.1007/s11760-022-02302-3.

A Review of Crucial Radiological Investigations in the Management of COVID-19 Cases.

Chest X-ray, chest CT, and lung ultrasound are the most common radiological interventions used in the diagnosis and management of coronavirus disease 2019 (COVID-19) patients. The purpose of this literature review, which was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, is to determine which radiological investigation is crucial for that purpose. PubMed, Medline, American Journal of Radiology (AJR), Public Library of Science (PLOS), Elsevier, National Center for Biotechnology Information (NCBI), and ScienceDirect were explored. Seventy-two articles were reviewed for potential inclusion, including 50 discussing chest CT, 15 discussing chest X-ray, five discussing lung ultrasound, and two discussing COVID-19 epidemiology. The reported sensitivities and specificities for chest CT ranged from 64 to 98% and 25 to 88%, respectively. The reported sensitivities and specificities for chest X-rays ranged from 33 to 89% and 11.1 to 88.9%, respectively. The reported sensitivities and specificities for lung ultrasound ranged from 93 to 96.8% and 21.3 to 95%, respectively. The most common findings on chest CT include ground glass opacities and consolidation. The most common findings on chest X-rays include opacities, consolidation, and pleural effusion. The data indicate that chest CT is the most effective radiological tool for the diagnosis and management of COVID-19 patients. The authors support the continued use of reverse transcription polymerase chain reaction (RT-PCR), along with physical examination and contact history, for such diagnosis. Chest CT could be more appropriate in emergency situations when quick triage of patients is necessary before RT-PCR results are available. CT can also be used to visualize the progression of COVID-19 pneumonia and to identify potential false positive RT-PCR results. Chest X-ray and lung ultrasound are acceptable in situations where chest CT is unavailable or contraindicated.

Primary SARS-CoV-2 Pneumonia Screening in Adults: Analysis of the Correlation between High-Resolution Computed Tomography Pulmonary Patterns and Initial Oxygen Saturation Levels.

BACKGROUND: Chest high-resolution computed tomography (HRCT) is mandatory for patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and a high respiratory rate (RR) because sublobar consolidation is the likely pathological pattern in addition to ground glass opacities (GGOs). OBJECTIVE: The present study determined the correlation between the percentage extent of typical pulmonary lesions on HRCT, as a representation of severity, and the RR and peripheral oxygen saturation level (SpO2), as measured through pulse oximetry, in patients with reverse transcriptase polymerase chain reaction (RT-PCR)-confirmed primary (noncomplicated) SARS-CoV-2 pneumonia. METHODS: The present retrospective study was conducted in 332 adult patients who presented withzdyspnea and hypoxemia and were admitted to Prince Mohammed bin Abdulaziz Hospital, Riyadh, Saudi Arabia between May 15, 2020 and December 15, 2020. All the patients underwent chest HRCT. Of the total, 198 patients with primary noncomplicated SARS-CoV-2 pneumonia were finally selected based on the typical chest HRCT patterns. The main CT patterns, GGO and sublobar consolidation, were individually quantified as a percentage of the total pulmonary involvement through algebraic summation of the percentage of the 19 pulmonary segments affected. Additionally, the statistical correlation strength between the total percentage pulmonary involvement and the age, initial RR, and percentage SpO2 of the patients was determined. RESULTS: The mean ± standard deviation (SD) age of the 198 patients was 48.9 ± 11.4 years. GGO magnitude alone exhibited a significant weak positive correlation with patients' age (r = 0.2; p = 0.04). Sublobar consolidation extent exhibited a relatively stronger positive correlation with RR than GGO magnitude (r = 0.23; p = 0.002). A relatively stronger negative correlation was observed between the GGO extent and SpO2 (r = - 0.38; p = 0.002) than that between sublobar consolidation and SpO2 (r = - 0.2; p = 0.04). An increase in the correlation strength was demonstrated with increased case segregation with GGO extent (r = - 0.34; p = 0.01). CONCLUSION: The correlation between the magnitudes of typical pulmonary lesion patterns, particularly GGO, which exhibited an incremental correlation pattern on chest HRCT, and the SpO2 percentage, may allow the establishment of an artificial intelligence program to differentiate primary SARS-CoV-2 pneumonia from other complications and associated pathology influencing SpO2.

Novel Corona Virus 2019 Disease: Assessment on High-Resolution Computed Tomography Thorax.

Objectives This particular study was undertaken to assess the role of high-resolution computed tomography (HRCT) thorax in diagnosing patients with novel Corona virus-2019 disease and screening suspected COVID-19 cases. It also involves an assessment of the severity of bilateral lung involvement in proven and suspected cases of COVID-19 infection. Materials and methods Two hundred and fourteen symptomatic cases referred to the department of radio-diagnosis were evaluated in this study. HRCT thorax was performed on SIEMENS Somatom Emotion 16-slice spiral CT. Initially, a tomogram was taken, followed by sections in the lung window at B90s, kVp 130, with a pitch of 1.15. The images are then reconstructed into 1.0-mm-thin slices. Radiologists then interpreted the scans for features of COVID-19 disease. Various imaging features and the severity of the disease were analysed in all patients. Results We observed that the male population was more affected by the disease (72% of the total cases). The most consistent and common HRCT finding is that of ground-glass opacity (GGO), which was present in 172 cases, corresponding to 78.4% of the cases. Crazy pavement appearance was seen in 41.2 % of the cases. Other findings included consolidation, discrete nodules surrounded by ground-glass opacification, subpleural linear opacities, and tubular bronchiectasis. Conclusion HRCT thorax plays an ideal role in diagnosing COVID-19 disease with high sensitivity and also provides prompt results as compared to RT-PCR. It also helps in grading the severity of the disease based on various patterns and the extent of lung parenchyma involved. Therefore, because of the immediate results and the ability to grade the disease, HRCT became invaluable in directing the treatment of COVID-19 disease.

Successful Diagnosis by Video-Assisted Thoracoscopic Surgical Lung Biopsy in a Case of Progressive Primary Pulmonary Extranodal Natural Killer/T-cell Lymphoma, Nasal Type: A Case Report.

Malignant pulmonary lymphoma is very rare and the majority of which are B-cell lymphomas. Since primary pulmonary extranodal natural killer (NK)/T-cell lymphoma, nasal type (ENKL) is difficult to diagnose and associated with poor prognosis and aggressive course, some cases are diagnosed at the postmortem autopsy. We report a case of a 53-year-old man with primary pulmonary ENKL diagnosed by video-assisted thoracoscopic surgery (VATS) lung biopsy. This case explains the importance of VATS lung biopsy and in-depth evaluation, including flow cytometry, chromosome, genetic, and immunostaining tests, when primary pulmonary malignant lymphoma is suspected.

COVID-19 chest X-ray image analysis by threshold-based segmentation.

COVID-19 is a severe acute respiratory syndrome that has caused a major ongoing pandemic worldwide. Imaging systems such as conventional chest X-ray (CXR) and computed tomography (CT) were proven essential for patients due to the lack of information about the complications that could result from this disease. In this study, the aim was to develop and evaluate a method for automatic diagnosis of COVID-19 using binary segmentation of chest X-ray images. The study used frontal chest X-ray images of 27 infected and 19 uninfected individuals from Kaggle COVID-19 Radiography Database, and applied binary segmentation and quartering in MATLAB to analyze the images. The binary images of the lung were split into four quarters; Q1 = right upper quarter, Q2 = left upper quarter, Q3 = right lower, and Q4 = left lower. The results showed that COVID-19 patients had a higher percentage of attenuation in the lower lobes of the lungs (p-value < 0.00001) compared to healthy individuals, which is likely due to ground-glass opacities and consolidations caused by the infection. The ratios of white pixels in the four quarters of the X-ray images were calculated, and it was found that the left lower quarter had the highest number of white pixels but without a statistical significance compared to right lower quarter (p-value = 0.102792). This supports the theory that COVID-19 primarily affects the lower and lateral fields of the lungs, and suggests that the virus is accumulated mostly in the lower left quarter of the lungs. Overall, this study contributes to the understanding of the impact of COVID-19 on the respiratory system and can help in the development of accurate diagnostic methods.

Pattern of Lung Involvement in Predicting Severity and Sequelae in Patients With COVID-19.

Background During the COVID pandemic, high-resolution CT scan has played a pivotal role in detecting lung involvement and severity based on the segments of the lung involved. The pattern of involvement was not considered, and our aim is to observe the pattern of lung involvement in predicting severity and guiding management protocol in patients with COVID-19. Methodology It was a prospective observational study conducted with 151 patients admitted with COVID-19 with a positive reverse transcriptase polymerase chain reaction test (RT-PCR) in a single tertiary care hospital in south India. Patients with pre-existing lung pathologies were excluded from the study. Eligible patients were then divided into mild, moderate, and severe categories based on Indian Council of Medical Research (ICMR) guidelines, and high-resolution computed tomography (HRCT) chest was done, findings of which were then categorized based on lung involvement; into ground glass opacities (GGO), interstitial involvement and mixture of both. These were then analyzed to determine their importance with respect to the duration of stay and severity of the disease. Results The data collected was analyzed by IBM SPSS software version 23.0 (IBM Corp., Armonk, NY, USA). The study population included 114 males (75.5%) and 37 females (24.5%). HRCT chest was done which showed 62.3% of patients had GGO, 14.6% had interstitial lung involvement, 18.5% had a mixture of both and 4.6% had normal lung findings. These findings, when compared to clinical categories of severity, showed a significant co-relation between pattern of involvement of the lung and the severity of the disease. It also showed significant co-relation with the duration of stay. Conclusion HRCT chest has proven to be useful in the determination of patient's severity and can guide with management. We suggest earlier initiation of steroids and anticoagulants in patients with interstitial involvement even for the patients not on oxygen therapy yet. It can be used as a triage modality for screening due to the advantage of presenting with immediate results as opposed to RT-PCR which might take hours and can delay treatment which can prevent worsening.

The Accuracy 0f CT Chest in Determining Between Ground Glass Opacities And Those Caused by Covid-19 And Other Medications

Introduction: Diagnostic ct scanning of the chest is currently being investigated for its ability to distinguish between ground glass opacities (GGO) caused by coronavirus 2019 (COVID-19) and GGO produced by other causes. Place and Duration: From January 2022 until June 2022, I will work as a Radiologist at Qazi Hussain Ahmad Hospital in Nowshera. Method(s): This study was cross sectional study carried out at the Qazi Hussain Ahmad Hospital, Nowshera for a period of six months. The overall sample size in the current study was100 non-contrast chest CTs. Eexperienced radiologists analyzed the CT images of the chest after redacting any personal information. laboratory results and the patient's medical history were noted. Result(s): The participants comprised 46 people with COVID19 and 100 without COVID19 who also had ground glass opacities on chest CT. There was no statistically significant difference in age between the groups (p-value = 0.212). Out of the non-COVID-19 GGO cases, three patients have hypersensitivity pneumonia in 3, eosinophilic pneumonia in 3, interstitial pneumonia in 7, pulmonary pneumonia in 3, pulmonary fibrosis in 7, drug-induced lung damage in 7, pulmonary alveolar hemorrhage in 3, and pulmonary emphysema in 11. Practical implication: This study will provide physician with the data to compare the likelihood that COVID-19 causes ground glass opacities on a chest CT scan versus the likelihood that they are caused by other probable causes Conclusion(s): Moreover, the specificity of chest CT in differentiating COVID-19 from viral pneumonia is only intermediate, and the specificity of chest CT in distinguishing COVID-19 from other reasons of ground glass opacities is poor. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.

Irreversible Lung Transformation Resulting from Damage In COVID-19 — Discourses and Examples of CT Images

The problem of the formation of irreversible residual changes after suffering viral lung damage with COVID-19 (COronaVIrus Disease 2019) after two years of the pandemic remains important and discussed. This is due to a large number of patients who have had a coronavirus infection (including those with a large amount of lung damage) and a possible unfavorable prognosis with a decrease in the quality and life expectancy. Given the fact that antifibrotic therapy has recently been actively used for a number of interstitial lung diseases (with idiopathic pulmonary fibrosis and systemic diseases), the question of the possible use of these drugs in case of an unfavorable outcome of COVID-19 is being considered. However, it is still not known exactly how often fibrosis develops in the outcome of a new coronavirus infection, and groups of patients who may have a poor prognosis in the form of an outcome in fibrosis have not been clearly identified. The review considers the pathogenetic aspects of the possible development of irreversible changes in patients with COVID-19, predisposing factors, as well as diagnostic features with an emphasis on CT scan with the authors' own observations. © 2022 Russian Archives of Internal Medicine.All rights reserved.

Enterovirus-Human-Rhinovirus Infection Leading to Acute Respiratory Distress Syndrome: A Case Report.

Enterovirus-human-rhinovirus (EV-HRV) is best known to cause the "common cold" and asthma exacerbations. Simple bronchitis and community-acquired pneumonia related to EV-HRV are also well documented. Scattered reports of rhinovirus causing acute respiratory distress syndrome (ARDS) have been published, yet the causality between recent SARS-CoV-2 pneumonia and severe ARDS secondary to EV-HRV has not been well defined. This case presents a 67-year-old male who was unvaccinated against SARS-CoV-2 with a past medical history of chronic obstructive pulmonary disease, who recently experienced a mild-to-moderate case of SARS-CoV-2 pneumonia, which was treated with dexamethasone and remdesivir. He was discharged to an inpatient psychiatric facility on as-needed oxygen via nasal cannula. Three weeks later, he experienced an episode of presyncope and was readmitted to the hospital. He then began to require increasing levels of supplemental oxygen via a high-flow nasal cannula. A real-time polymerase chain reaction respiratory pathogen panel was positive for EV-HRV. Computed tomography of the chest revealed extensive ground-glass opacities. Further workup for bacterial and fungal pneumonia was negative. Repeat SARS-CoV-2 testing was also negative. He required several days of supplemental oxygen via a high-flow nasal cannula. He received a short course of broad-spectrum antibiotics and a 10-day course of high-dose dexamethasone. Ultimately, he fully recovered, did not require further supplemental oxygen, and was discharged on room air.

Severe COVID-19 Pneumonia in an Unvaccinated Female Treated With Remdesivir.

Though recent developments in the management of the coronavirus disease 2019 (COVID-19) pandemic have resulted in significant progress, its continued persistence demands continued consideration both of larger scale public health factors as well as individual patient management. We present a case that provides a broad perspective across several issues within both categories, of a morbidly obese 34-year-old unvaccinated female presenting with respiratory distress secondary to COVID-19 pneumonia, managed through remdesivir therapy. Though this case presents an example of successful management, it nonetheless emphasizes the demand for a renewed focus on vaccine hesitancy and obesity as public health issues, particularly within the context of the pandemic.

Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database.

Background and Objective: The spread of coronavirus has been challenging for the healthcare system's proper management and diagnosis during the rapid spread and control of the infection. Real-time reverse transcription-polymerase chain reaction (RT-PCR), though considered the standard testing measure, has low sensitivity and is time-consuming, which restricts the fast screening of individuals. Therefore, computer tomography (CT) is used to complement the traditional approaches and provide fast and effective screening over other diagnostic methods. This work aims to appraise the importance of chest CT findings of COVID-19 and post-COVID in the diagnosis and prognosis of infected patients and to explore the ways and means to integrate CT findings for the development of advanced Artificial Intelligence (AI) tool-based predictive diagnostic techniques. Methods: The retrospective study includes a 188 patient database with COVID-19 infection confirmed by RT-PCR testing, including post-COVID patients. Patients underwent chest high-resolution computer tomography (HRCT), where the images were evaluated for common COVID-19 findings and involvement of the lung and its lobes based on the coverage region. The radiological modalities analyzed in this study may help the researchers in generating a predictive model based on AI tools for further classification with a high degree of reliability. Results: Mild to moderate ground glass opacities (GGO) with or without consolidation, crazy paving patterns, and halo signs were common COVID-19 related findings. A CT score is assigned to every patient based on the severity of lung lobe involvement. Conclusion: Typical multifocal, bilateral, and peripheral distributions of GGO are the main characteristics related to COVID-19 pneumonia. Chest HRCT can be considered a standard method for timely and efficient assessment of disease progression and management severity. With its fusion with AI tools, chest HRCT can be used as a one-stop platform for radiological investigation and automated diagnosis system.

Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.

Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

COVID-19 Detection Using Raw Chest X-Ray Images

COVID-19 has had a lasting effect on the human population around the globe. originating from Wuhan, China, in December 2019, the virus managed to spread worldwide in a short time. Huge waiting time between the detection of symptoms and clinical confirmation of the virus being present in the body has made the virus more fatal;thus, rapid screening of large numbers of suspected patients is essential. Due to inefficiency in pathological testing, alternate ways must be devised to combat these issues. Due to advancements in CAD, integrating radiological images with Artificial Intelligence (AI) can detect the disease accurately. This study proposes a deep learning model for automatic COVID-19 detection using raw Chest X-ray (CXR) images. With 17 convolutional layers, the proposed model is trained to diagnose COVID-19 with an 96.67% accuracy. The model can be used to help the world in numerous ways. © 2022 IEEE.

Computed tomography manifestations and prognosis of common COVID-19 cases

Common COVID-19 cases are those with fever, respiratory symptoms, and positive imaging findings. This chapter illustrates 16 common cases with serial computed tomography examinations showing findings from the early phase to the advanced phase and then to the absorption phase. In the early phase, single or multiple ground-glass opacities (GGOs) with crazy-paving sign, mainly in the subpleural area of unilateral or bilateral lungs, are noticed, and then in the advanced phase, some of these GGOs turn into consolidations with air bronchogram inside;and finally, in the absorption phase, the GGOs and consolidations totally disappear or some parenchymal bands remain in the lung area, especially in the subpleural area.

An Investigation of COVID-19 Diagnosis and Severity Detection Using Convolutional Neural Networks

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). It is a contagious disease that has infected more than millions of people around the globe. COVID-19 can be diagnosed based on the amount of infection in the lungs. Apart from the gold standard reverse transcription-polymerase chain reaction test, X-rays and CT scans can be used to diagnose and detect COVID-19 severity. Due to technological advancements, deep learning models play a crucial role in COVID-19 analysis because of their efficiency and accuracy. Hence, the present work investigates various research works to detect COVID-19 using a convolutional neural network. It compares architectures such as Inception, MobileNet, DenseNet, and new architectures like CovidNet and CovidSDNet were developed. Some of the investigations in COVID-19 detection were conducted by performing data augmentation and ensembling techniques with and without transfer learning. Most research works used accuracy, precision, recall, and F1-score as the performance metrics for evaluation. The numerical comparison analysis shows that the earlier works achieved an accuracy of about 89 to 98 percent. However, in most investigated research, multiclass classification is performed to classify the given CT scan or X-ray image into COVID-19, normal or pneumonia. In the current situation, there is a possibility that some people with COVID-19 might have other respiratory diseases as well. Hence, the investigations suggest that multi-label classification with convolutional neural networks can be suitable to determine the combination of respiratory problems present along with COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Comparison of COVID-19 pneumonia during the SARS-CoV-2 Omicron wave and the previous non-Omicron wave in a single facility.

BACKGROUND: The characteristics of coronavirus disease 2019 (COVID-19) pneumonia caused by the severe acute respiratory syndrome coronavirus 2 Omicron variant have not been fully described. Unlike other variants, the Omicron variant replicates rapidly in the bronchus. Therefore, we hypothesized that it would have different computed tomography (CT) findings from non-Omicron variants. METHODS: We enrolled patients with COVID-19 who visited our hospital and underwent chest CT during the first month of the Omicron wave (January 2022; N = 231) and the previous non-Omicron wave (July 2021; N = 87). We retrospectively evaluated the differences in the prevalence rate and CT characteristics of COVID-19 pneumonia between the two waves. RESULTS: The prevalence of pneumonia was significantly lower in the Omicron wave group (79/231, 34.2%) compared to the previous wave group (67/87, 77.0%) (P < 0.001). For the predominant distribution pattern of pneumonia, the Omicron wave group revealed a significantly lower rate of the peripheral pattern and a higher rate of the random pattern than the previous wave group. In addition, the Omicron wave group had a significantly lower rate of consolidation than the previous wave group. The ground-glass opacities (GGOs) rate was similar between the two wave groups. For GGOs patterns, cluster-like GGOs along the bronchi on chest CT were more frequently observed during the Omicron wave than during the previous wave. CONCLUSION: The Omicron wave group had a lower COVID-19 pneumonia prevalence than the previous wave group. Cluster-like GGOs should be noted as a characteristic CT finding of pneumonia during the Omicron wave.

COVLIAS 1.0Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans.

BACKGROUND: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. METHODOLOGY: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models-namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet-were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. RESULTS: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests-namely, the Mann-Whitney test, paired t-test, and Wilcoxon test-demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. CONCLUSIONS: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.

Chest CT Findings and Their Temporal Evolution in COVID-19 Pneumonia.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic originated in China in November 2019 and is caused by the SARS-CoV-2 virus. The virus binds to nasal and pharyngeal epithelial cells and migrates to the lower respiratory tract. The confirmatory test for COVID-19 infection is the reverse transcription-polymerase chain reaction (RT-PCR). Chest CT plays an important role in the diagnosis, triage, and treatment of affected individuals. We describe the findings on chest CT and their temporal evolution in COVID-19 pneumonia. METHODS: We conducted a retrospective, cross-sectional study on COVID-19-positive patients who underwent chest CT. CT images of the patients were reviewed for ground-glass opacities, consolidation, crazy-paving appearance, vascular dilatation, traction bronchiectasis, architectural distortion, and subpleural and parenchymal bands. Distribution of opacities on axial sections, ancillary findings, and co-existent lung diseases were recorded. To assess the temporal evolution of CT findings, the time in days between the onset of the first symptom and the date of the CT scan of each patient was recorded. Statistical analysis was performed. RESULTS: Ground-glass opacities, consolidation, and a combination of both were the most important features in COVID-19 pneumonia. Patients in the early stage showed simple ground-glass opacities; in the progressive stage showed consolidation and ground-glass opacities with crazy-paving appearance, subpleural and parenchymal bands, and architectural distortion; in the peak stage showed progression of these findings; and in the late stage showed interval resolution of these findings. Axial distribution of these opacities was asymmetric, with peripheral subpleural predominance involving posterior, lateral, and both these locations, associated with apicobasal gradient. CONCLUSION: Chest CT permits rapid diagnosis of COVID-19 pneumonia, enabling appropriate treatment to be instituted at the earliest. Thus, it is life-saving in resource-constrained environments.