Determinants of Chest X-Ray Sensitivity for COVID- 19: A Multi-Institutional Study in the United States

Predicting oxygen needs in COVID-19 patients using chest radiography multi-region radiomics

The objective is to evaluate the performance of blood test results, radiomics, and a combination of the two data types on the prediction of the 24-h oxygenation support need for the Coronavirus disease 2019 (COVID-19) patients. In this retrospective cohort study, COVID-19 patients with confirmed real-time reverse transcription-polymerase chain reaction assay (RT-PCR) test results between February 2020 and August 2021 were investigated. Initial blood cell counts, chest radiograph, and the status of oxygenation support used within 24 h were collected (n = 290; mean age, 45 ± 19 years; 125 men). Radiomics features from six lung zones were extracted. Logistic regression and random forest models were developed using the clinical-only, radiomics-only, and combined data. Ten repeats of fivefold cross-validation with bootstrapping were used to identify the input features and models with the highest area under the receiver operating characteristic curve (AUC). Higher AUCs were achieved when using only radiomics features compared to using only clinical features (0.94 ± 0.03 vs. 0.88 ± 0.04). The best combined model using both radiomics and clinical features achieved highest in the cross-validation (0.95 ± 0.02) and test sets (0.96 ± 0.02). In comparison, the best clinical-only model yielded AUCs of 0.88 ± 0.04 in cross-validation and 0.89 ± 0.03 in test set. Both radiomics and clinical data can be used to predict 24-h oxygenation support need for COVID-19 patients with AUC > 0.88. Moreover, the combination of both data types further improved the performance.

Lung Ultrasound Score in COVID-19 Patients Correlates with PO2/FiO2, Intubation Rates, and Mortality

Introduction

The point-of-care lung ultrasound (LUS) score has been used in coronavirus 2019 (COVID-19) patients for diagnosis and risk stratification, due to excellent sensitivity and infection control concerns. We studied the ratio of partial pressure of oxygen in arterial blood to the fraction of inspiratory oxygen concentration (PO2/FiO2), intubation rates, and mortality correlation to the LUS score.

Methods

We conducted a systematic review using PRISMA guidelines. Included were articles published from December 1, 2019-November 30, 2021 using LUS in adult COVID-19 patients in the intensive care unit or the emergency department. Excluded were studies on animals and on pediatric and pregnant patients. We assessed bias using QUADAS-2. Outcomes were LUS score and correlation to PO2/FiO2, intubation, and mortality rates. Random effects model pooled the meta-analysis results.

Results

We reviewed 27 of 5,267 studies identified. Of the 27 studies, seven were included in the intubation outcome, six in the correlation to PO2/FiO2 outcome, and six in the mortality outcome. Heterogeneity was found in ultrasound protocols and outcomes. In the pooled results of 267 patients, LUS score was found to have a strong negative correlation to PO2/FiO2 with a correlation coefficient of -0.69 (95% confidence interval [CI] -0.75, -0.62). In pooled results, 273 intubated patients had a mean LUS score that was 6.95 points higher (95% CI 4.58-9.31) than that of 379 non-intubated patients. In the mortality outcome, 385 survivors had a mean LUS score that was 4.61 points lower (95% CI 3.64-5.58) than that of 181 non-survivors. There was significant heterogeneity between the studies as measured by the I2 and Cochran Q test.

Conclusion

A higher LUS score was strongly correlated with a decreasing PO2/FiO2 in COVID-19 pneumonia patients. The LUS score was significantly higher in intubated vs non-intubated patients with COVID-19. The LUS score was significantly lower in critically ill patients with COVID-19 pneumonia that survive.

Handheld Lung Ultrasound to Detect COVID-19 Pneumonia in Inpatients: A Prospective Cohort Study

Background: Chest imaging, including chest X-ray (CXR) and computed tomography (CT), can be a helpful adjunct to nucleic acid test (NAT) in the diagnosis and management of Coronavirus Disease 2019 (COVID-19). Lung point of care ultrasound (POCUS), particularly with handheld devices, is an imaging alternative that is rapid, highly portable, and more accessible in low-resource settings. A standardized POCUS scanning protocol has been proposed to assess the severity of COVID-19 pneumonia, but it has not been sufficiently validated to assess diagnostic accuracy for COVID-19 pneumonia. Purpose: To assess the diagnostic performance of a standardized lung POCUS protocol using a handheld POCUS device to detect patients with either a positive NAT or a COVID-19-typical pattern on CT scan. Methods: Adult inpatients with confirmed or suspected COVID-19 and a recent CT were recruited from April to July 2020. Twelve lung zones were scanned with a handheld POCUS machine. Images were reviewed independently by blinded experts and scored according to the proposed protocol. Patients were divided into low, intermediate, and high suspicion based on their POCUS score. Results: Of 79 subjects, 26.6% had a positive NAT and 31.6% had a typical CT pattern. The receiver operator curve for POCUS had an area under the curve (AUC) of 0.787 for positive NAT and 0.820 for a typical CT. Using a two-point cutoff system, POCUS had a sensitivity of 0.90 and 1.00 compared to NAT and typical CT pattern, respectively, at the lower cutoff; it had a specificity of 0.90 and 0.89 compared to NAT and typical CT pattern at the higher cutoff, respectively. Conclusions: The proposed lung POCUS protocol with a handheld device showed reasonable diagnostic performance to detect inpatients with a positive NAT or typical CT pattern for COVID-19. Particularly in low-resource settings, POCUS with handheld devices may serve as a helpful adjunct for persons under investigation for COVID-19 pneumonia.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Interpretation of chest radiography in patients with known or suspected SARS-CoV-2 infection: what we learnt from comparison with computed tomography

Differently from computed tomography (CT), well-defined terminology for chest radiography (CXR) findings and standardized reporting in the setting of known or suspected COVID-19 are still lacking. We propose a revision of CXR major imaging findings in SARS-CoV-2 pneumonia derived from the comparison of CXR and CT, suggesting a precise and standardized terminology for CXR reporting. This description will consider asymptomatic patients, symptomatic patients, and patients with SARS-CoV-2-related pulmonary complications. We suggest using terms such as ground-glass opacities, consolidation, and reticular pattern for the most common findings, and characteristic chest radiographic pattern in presence of one or more of the above-mentioned findings with peripheral and mid-to-lower lung zone distribution.

Deep Neural Network Augments Performance of Junior Residents in Diagnosing COVID-19 Pneumonia on Chest Radiographs

Chest X-rays (CXRs) are essential in the preliminary radiographic assessment of patients affected by COVID-19. Junior residents, as the first point-of-contact in the diagnostic process, are expected to interpret these CXRs accurately. We aimed to assess the effectiveness of a deep neural network in distinguishing COVID-19 from other types of pneumonia, and to determine its potential contribution to improving the diagnostic precision of less experienced residents. A total of 5051 CXRs were utilized to develop and assess an artificial intelligence (AI) model capable of performing three-class classification, namely non-pneumonia, non-COVID-19 pneumonia, and COVID-19 pneumonia. Additionally, an external dataset comprising 500 distinct CXRs was examined by three junior residents with differing levels of training. The CXRs were evaluated both with and without AI assistance. The AI model demonstrated impressive performance, with an Area under the ROC Curve (AUC) of 0.9518 on the internal test set and 0.8594 on the external test set, which improves the AUC score of the current state-of-the-art algorithms by 1.25% and 4.26%, respectively. When assisted by the AI model, the performance of the junior residents improved in a manner that was inversely proportional to their level of training. Among the three junior residents, two showed significant improvement with the assistance of AI. This research highlights the novel development of an AI model for three-class CXR classification and its potential to augment junior residents' diagnostic accuracy, with validation on external data to demonstrate real-world applicability. In practical use, the AI model effectively supported junior residents in interpreting CXRs, boosting their confidence in diagnosis. While the AI model improved junior residents' performance, a decline in performance was observed on the external test compared to the internal test set. This suggests a domain shift between the patient dataset and the external dataset, highlighting the need for future research on test-time training domain adaptation to address this issue.

Assessing the implementation of COVID-19 structured reporting templates for chest radiography: a scoping review

Objective

One of the common modalities used in imaging COVID-19 positive patients is chest radiography (CXR), and serves as a valuable imaging method to diagnose and monitor a patients' condition. Structured reporting templates are regularly used for the assessment of COVID-19 CXRs and are supported by international radiological societies. This review has investigated the use of structured templates for reporting COVID-19 CXRs.

Methods

A scoping review was conducted on literature published between 2020 and 2022 using Medline, Embase, Scopus, Web of Science, and manual searches. An essential criterion for the inclusion of the articles was the use of reporting methods employing either a structured quantitative or qualitative reporting method. Thematic analyses of both reporting designs were then undertaken to evaluate utility and implementation.

Results

Fifty articles were found with the quantitative reporting method used in 47 articles whilst 3 articles were found employing a qualitative design. Two quantitative reporting tools (Brixia and RALE) were used in 33 studies, with other studies using variations of these methods. Brixia and RALE both use a posteroanterior or supine CXR divided into sections, Brixia with six and RALE with four sections. Each section is scaled numerically depending on the level of infection. The qualitative templates relied on selecting the best descriptor of the presence of COVID-19 radiological appearances. Grey literature from 10 international professional radiology societies were also included in this review. The majority of the radiology societies recommend a qualitative template for reporting COVID-19 CXRs.

Conclusion

Most studies employed quantitative reporting methods which contrasted with the structured qualitative reporting template advocated by most radiological societies. The reasons for this are not entirely clear. There is also a lack of research literature on both the implementation of the templates or comparing both template types, indicating that the use of structured radiology reporting types may be an underdeveloped clinical strategy and research methodology.

Advances in knowledge

This scoping review is unique in that it has undertaken an examination of the utility of the quantitative and qualitative structured reporting templates for COVID-19 CXRs. Moreover, through this review, the material examined has allowed a comparison of both instruments, clearly showing the favoured style of structured reporting by clinicians. At the time of the database interrogation, there were no studies found had undertaken such examinations of both reporting instruments. Moreover, due to the enduring influence of COVID-19 on global health, this scoping review is timely in examining the most innovative structured reporting tools that could be used in the reporting of COVID-19 CXRs. This report could assist clinicians in decision-making regarding templated COVID-19 reports.

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

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

COVID-19 Diagnosis on Chest Radiograph Using Artificial Intelligence

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has disrupted the world since 2019, causing significant morbidity and mortality in developed and developing countries alike. Although substantial resources have been diverted to developing diagnostic, preventative, and treatment measures, disparities in the availability and efficacy of these tools vary across countries. We seek to assess the ability of commercial artificial intelligence (AI) technology to diagnose COVID-19 by analyzing chest radiographs.

MATERIALS AND METHODS

Chest radiographs taken from symptomatic patients within two days of polymerase chain reaction (PCR) tests were assessed for COVID-19 infection by board-certified radiologists and commercially available AI software. Sixty patients with negative and 60 with positive COVID reverse transcription-polymerase chain reaction (RT-PCR) tests were chosen. Results were compared against results of the PCR test for accuracy and statistically analyzed by receiver operating characteristic (ROC) curves along with area under the curve (AUC) values.

RESULTS

A total of 120 chest radiographs (60 positive and 60 negative RT-PCR tests) radiographs were analyzed. The AI software performed significantly better than chance (p = 0.001) and did not differ significantly from the radiologist ROC curve (p = 0.78).

CONCLUSION

Commercially available AI software was not inferior compared with trained radiologists in accurately identifying COVID-19 cases by analyzing radiographs. While RT-PCR testing remains the standard, current advances in AI help correctly analyze chest radiographs to diagnose COVID-19 infection.

Radiographic Findings and Association With Clinical Severity and Outcomes in Critically Ill Patients With COVID-19

PURPOSE

To describe evolution and severity of radiographic findings and assess association with disease severity and outcomes in critically ill COVID-19 patients.

MATERIALS AND METHODS

This retrospective study included 62 COVID-19 patients admitted to the intensive care unit (ICU). Clinical data was obtained from electronic medical records. A total of 270 chest radiographs were reviewed and qualitatively scored (CXR score) using a severity scale of 0-30. Radiographic findings were correlated with clinical severity and outcome.

RESULTS

The CXR score increases from a median initial score of 10 at hospital presentation to the median peak CXR score of 18 within a median time of 4 days after hospitalization, and then slowly decreases to a median last CXR score of 15 in a median time of 12 days after hospitalization. The initial and peak CXR score was independently associated with invasive MV after adjusting for age, gender, body mass index, smoking, and comorbidities (Initial, odds ratio [OR]: 2.11 per 5-point increase, confidence interval [CI] 1.35-3.32, P= 0.001; Peak, OR: 2.50 per 5-point increase, CI 1.48-4.22, P= 0.001). Peak CXR scores were also independently associated with vasopressor usage (OR: 2.28 per 5-point increase, CI 1.30-3.98, P= 0.004). Peak CXR scores strongly correlated with the duration of invasive MV (Rho = 0.62, P< 0.001), while the initial CXR score (Rho = 0.26) and the peak CXR score (Rho = 0.27) correlated weakly with the sequential organ failure assessment score. No statistically significant associations were found between radiographic findings and mortality.

CONCLUSIONS

Evolution of radiographic features indicates rapid disease progression and correlate with requirement for invasive MV or vasopressors but not mortality, which suggests potential nonpulmonary pathways to death in COVID-19.

AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions

Background and objective

In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field.

Methods

The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection.

Results

The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well.

Conclusion

This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.

A study of the chest imaging findings of adult patients with COVID-19 on admission to a tertiary hospital in Johannesburg, South Africa

Background

South Africa has experienced multiple waves of the coronavirus disease 2019 (COVID-19) with little research documenting chest imaging features in an human immunodeficiency virus (HIV) and tuberculosis (TB) endemic region.

Objectives

Describe the chest imaging features, demographics and clinical characteristics of COVID-19 in an urban population.

Method

Retrospective, cross-sectional, review of chest radiographs and computed tomographies (CTs) of adults admitted to a tertiary hospital with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, between 01 May 2020 and 30 June 2020. Imaging was reviewed by three radiologists. Clinical parameters and laboratory data were analysed.

Results

A total of 113 adult patients with a mean age of 46 years and 10 months were included. A total of 113 chest radiographs and six CTs were read. Nineteen patients were HIV-positive (16.8%), 40 were hypertensive and diabetic (35.4%), respectively, and one had TB (0.9%). Common symptoms included cough (n = 69; 61.6%), dyspnoea (n = 60; 53.1%) and fever (n = 46; 40.7%). Lower zone predominant ground glass opacities (58.4%) and consolidation (29.2%) were most frequent on chest radiographs. The right lower lobe was most involved (46.9% ground glass opacities and 17.7% consolidation), with relative sparing of the left upper lobe. Bilateral ground glass opacities (66.7%) were most common on CT. Among the HIV-positive, ground glass opacities and consolidation were less common than in HIV-negative or unknown patients (p = 0.037 and p = 0.05, respectively).

Conclusion

COVID-19 in South Africa has similar chest imaging findings to those documented globally, with some differences between HIV-positive and HIV-negative or unknown patients. The authors corroborate relative sparing of the left upper lobe; however, further research is required to validate this currently unique local finding.

Clinical comparison and agreement of PCR, antigen, and viral culture for the diagnosis of COVID-19

The aim of this study is to compare the COVID-19 nasopharyngeal PCR (NP PCR) to antigen, nasal PCR, and viral culture. One-hundred-and-fourteen risk-stratified patients were tested by culture, nasal PCR, NP PCR, and Ag testing. Twenty (48%) of the high risk and 23 (32%) of the low risk were NP PCR positive. Compared with NP PCR, the sensitivity of nasal PCR, Sofia Ag, BinaxNOW Ag, and culture were 44%, 31%, 37%, and 15%. In the high risk group, the sensitivity of these tests improved to 71%, 37%, 50%, and 22%. Agreement between tests was highest between nasal PCR and both antigen tests. Patients who were NP PCR positive but antigen negative were more likely to have remote prior COVID-19 infection (p<0.01). Nasal PCR and antigen positive patients were more likely to have symptoms (p = 0.01).

Impact of X-radiation in the management of COVID-19 disease

Coronaviruses are a diverse group of viruses that infect both animals and humans. Even though the existence of coronavirus and its infection to humans is not new, the 2019-novel coronavirus (nCoV) caused a major burden to individuals and society i.e., anxiety, fear of infection, extreme competition for hospitalization, and more importantly financial liability. The nCoV infection/disease diagnosis was based on non-specific signs and symptoms, biochemical parameters, detection of the virus using reverse-transcription polymerase chain reaction (RT-PCR), and X-ray-based imaging. This review focuses on the consolidation of potentials of X-ray-based imaging modality [chest-X radiography (CXR) and chest computed tomography (CT)] and low-dose radiation therapy (LDRT) for screening, severity, and management of COVID-19 disease. Reported studies suggest that CXR contributed significantly toward initial rapid screening/diagnosis and CT- imaging to monitor the disease severity. The chest CT has high sensitivity up to 98% and low specificity for diagnosis and severity of COVID-19 disease compared to RT-PCR. Similarly, LDRT compliments drug therapy in the early recovery/Less hospital stays by maintaining the physiological parameters better than the drug therapy alone. All the results undoubtedly demonstrated the evidence that X-ray-based technology continues to evolve and play a significant role in human health care even during the pandemic.

Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning

Background

Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED.

Methods

This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S. institution. A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression.

Results

Overall ICC was 0.820 (95% CI 0.790–0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861–0.920) for the neural network and 0.936 (95% CI 0.918–0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906).

Conclusion

The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07617-7.

Time course of lung ultrasound findings in patients with COVID-19 pneumonia and cardiac dysfunction

Background

Lung ultrasound (LUS) is a valuable tool to predict and monitor the COVID-19 pneumonia course. However, the influence of cardiac dysfunction (CD) on LUS findings remains to be studied. Our objective was to determine the effect of CD on LUS in hospitalized patients with COVID-19 pneumonia.

Material and methods

Fifty-one patients with COVID-19 pneumonia participated in the study. Focused echocardiography (FoCUS) was carried out on day 1 to separate patients into two groups depending on whether they had FoCUS signs of CD (CD+ vs CD−). LUS scores, based on the thickness of the pleural line, the B-line characteristics, and the presence or not of consolidations, were obtained three times along the patient’s admission (D1, D5, D10) and compared between CD+ and CD− patients. A correlation analysis was carried out between LUS scores and the ratio of the arterial partial pressure of oxygen to the fraction of the inspired oxygen (P/F ratio).

Results

Twenty-two patients were CD+ and 29 patients were CD−. Among the CD+ patients, 19 were admitted to the intensive care unit (ICU), seven received invasive mechanical ventilation (IMV), and one did not survive. Among the CD− patients, 11 were admitted to the ICU, one received IMV and seven did not survive. CD+ patients showed a significantly lower P/F ratio than CD− patients. However, LUS scores showed no between-group differences, except for fewer subpleural consolidations in the upper quadrants of CD+ than on CD− patients.

Conclusion

In patients with COVID-19, CD contributed to a worse clinical course, but it did not induce significant changes in LUS. Our findings suggest that pathophysiological factors other than those reflected by LUS may be responsible for the differences in clinical condition between CD+ and CD− patients.

[Usefulness of chest X-rays for detecting COVID 19 pneumonia during the SARS-CoV-2 pandemic]

Objective

To review the prognostic usefulness of chest X-rays in selecting patients with suspected SARS-CoV-2 infection.

Material and methods

This cross-sectional descriptive observational study analyzed 978 patients with suspected SARS-CoV-2 infections who underwent chest X-ray examinations in the emergency department of a tertiary hospital in March 2020. We separately analyzed demographic, clinical, and prognostic variables in two groups of patients: those in whom reverse-transcriptase polymerase chain reaction (RT-PCR) was done (n = 535) and those in whom RT-PCR was not done because of low clinical suspicion (n = 443).

Results

In the group of patients with RT-PCR, the prevalence of SARS-CoV-2 was 70.4%, and the sensitivity of chest X-rays was 62.8%. In the group of patients without RT-PCR, chest X-rays were negative in 97.5%, corroborating the low clinical suspicion; these patients were discharged, and 5.6% of them reconsulted with mild forms of the disease. In the group of patients with RT-PCR, we observed no statistically significant differences in the percentage of pathologic chest X-rays between patients hospitalized in the ICU (72.9%) and in those hospitalized in other wards (68.3%) (p = 0.22).

Conclusion

In the context of the pandemic, patients with low clinical suspicion and negative chest X-rays can be discharged with a low probability of reconsultation or of developing severe COVID19. In patients with RT-PCR positive for SARS-CoV-2, chest X-rays have no prognostic usefulness.

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review

Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, this review article focuses on cascading numerous deep learning-based COVID-19 computerized tomography (CT) imaging diagnosis research, providing a baseline for future research. Compared to previous review articles on the topic, this study pigeon-holes the collected literature very differently (i.e., its multi-level arrangement). For this purpose, 71 relevant studies were found using a variety of trustworthy databases and search engines, including Google Scholar, IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus. We classify the selected literature in multi-level machine learning groups, such as supervised and weakly supervised learning. Our review article reveals that weak supervision has been adopted extensively for COVID-19 CT diagnosis compared to supervised learning. Weakly supervised (conventional transfer learning) techniques can be utilized effectively for real-time clinical practices by reusing the sophisticated features rather than over-parameterizing the standard models. Few-shot and self-supervised learning are the recent trends to address data scarcity and model efficacy. The deep learning (artificial intelligence) based models are mainly utilized for disease management and control. Therefore, it is more appropriate for readers to comprehend the related perceptive of deep learning approaches for the in-progress COVID-19 CT diagnosis research.

Assessing clinical applicability of COVID-19 detection in chest radiography with deep learning

The coronavirus disease 2019 (COVID-19) pandemic has impacted healthcare systems across the world. Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. Recent studies suggest that deep learning can be used to assess CXRs, providing an important second opinion for radiologists and technicians in the decision process, and super-human performance in detection of COVID-19 has been reported in multiple studies. In this study, the clinical applicability of deep learning systems for COVID-19 screening was assessed by testing the performance of deep learning systems for the detection of COVID-19. Specifically, four datasets were used: (1) a collection of multiple public datasets (284.793 CXRs); (2) BIMCV dataset (16.631 CXRs); (3) COVIDGR (852 CXRs) and 4) a private dataset (6.361 CXRs). All datasets were collected retrospectively and consist of only frontal CXR views. A ResNet-18 was trained on each of the datasets for the detection of COVID-19. It is shown that a high dataset bias was present, leading to high performance in intradataset train-test scenarios (area under the curve 0.55–0.84 on the collection of public datasets). Significantly lower performances were obtained in interdataset train-test scenarios however (area under the curve > 0.98). A subset of the data was then assessed by radiologists for comparison to the automatic systems. Finetuning with radiologist annotations significantly increased performance across datasets (area under the curve 0.61–0.88) and improved the attention on clinical findings in positive COVID-19 CXRs. Nevertheless, tests on CXRs from different hospital services indicate that the screening performance of CXR and automatic systems is limited (area under the curve < 0.6 on emergency service CXRs). However, COVID-19 manifestations can be accurately detected when present, motivating the use of these tools for evaluating disease progression on mild to severe COVID-19 patients.

Convolutional Neural Network-Based Automatic Analysis of Chest Radiographs for the Detection of COVID-19 Pneumonia: A Prioritizing Tool in the Emergency Department, Phase I Study and Preliminary "Real Life" Results

The aim of our study is the development of an automatic tool for the prioritization of COVID-19 diagnostic workflow in the emergency department by analyzing chest X-rays (CXRs). The Convolutional Neural Network (CNN)-based method we propose has been tested retrospectively on a single-center set of 542 CXRs evaluated by experienced radiologists. The SARS-CoV-2 positive dataset (n = 234) consists of CXRs collected between March and April 2020, with the COVID-19 infection being confirmed by an RT-PCR test within 24 h. The SARS-CoV-2 negative dataset (n = 308) includes CXRs from 2019, therefore prior to the pandemic. For each image, the CNN computes COVID-19 risk indicators, identifying COVID-19 cases and prioritizing the urgent ones. After installing the software into the hospital RIS, a preliminary comparison between local daily COVID-19 cases and predicted risk indicators for 2918 CXRs in the same period was performed. Significant improvements were obtained for both prioritization and identification using the proposed method. Mean Average Precision (MAP) increased (p < 1.21 × 10−21 from 43.79% with random sorting to 71.75% with our method. CNN sensitivity was 78.23%, higher than radiologists’ 61.1%; specificity was 64.20%. In the real-life setting, this method had a correlation of 0.873. The proposed CNN-based system effectively prioritizes CXRs according to COVID-19 risk in an experimental setting; preliminary real-life results revealed high concordance with local pandemic incidence.

COVID-19 Detection in CT/X-ray Imagery Using Vision Transformers

The steady spread of the 2019 Coronavirus disease has brought about human and economic losses, imposing a new lifestyle across the world. On this point, medical imaging tests such as computed tomography (CT) and X-ray have demonstrated a sound screening potential. Deep learning methodologies have evidenced superior image analysis capabilities with respect to prior handcrafted counterparts. In this paper, we propose a novel deep learning framework for Coronavirus detection using CT and X-ray images. In particular, a Vision Transformer architecture is adopted as a backbone in the proposed network, in which a Siamese encoder is utilized. The latter is composed of two branches: one for processing the original image and another for processing an augmented view of the original image. The input images are divided into patches and fed through the encoder. The proposed framework is evaluated on public CT and X-ray datasets. The proposed system confirms its superiority over state-of-the-art methods on CT and X-ray data in terms of accuracy, precision, recall, specificity, and F1 score. Furthermore, the proposed system also exhibits good robustness when a small portion of training data is allocated.

Acute management of COVID-19 in the emergency department: An evidence-based review

Coronavirus disease (COVID-19) has been relentlessly battering the world wave after wave in different countries at different rates and times. Emergency departments (EDs) around the globe have had to constantly adapt to this ever-changing influx of information and recommendations by various national and international health agencies. This review compiles the available evidence on the guidelines for triaging, evaluation, and management of critically ill patients with COVID-19 presenting to the ED and in need of emergency resuscitation. The quintessential components of resuscitation focus on airway, breathing, and circulation with good supportive care as the cornerstone of acute management of critically ill COVID-19 patients. Irrational investigations and therapeutics must be avoided during these times of medical uncertainty and antibiotic stewardship should be diligently followed.

Explainable COVID-19 Detection on Chest X-rays Using an End-to-End Deep Convolutional Neural Network Architecture

The coronavirus pandemic is spreading around the world. Medical imaging modalities such as radiography play an important role in the fight against COVID-19. Deep learning (DL) techniques have been able to improve medical imaging tools and help radiologists to make clinical decisions for the diagnosis, monitoring and prognosis of different diseases. Computer-Aided Diagnostic (CAD) systems can improve work efficiency by precisely delineating infections in chest X-ray (CXR) images, thus facilitating subsequent quantification. CAD can also help automate the scanning process and reshape the workflow with minimal patient contact, providing the best protection for imaging technicians. The objective of this study is to develop a deep learning algorithm to detect COVID-19, pneumonia and normal cases on CXR images. We propose two classifications problems, (i) a binary classification to classify COVID-19 and normal cases and (ii) a multiclass classification for COVID-19, pneumonia and normal. Nine datasets and more than 3200 COVID-19 CXR images are used to assess the efficiency of the proposed technique. The model is trained on a subset of the National Institute of Health (NIH) dataset using swish activation, thus improving the training accuracy to detect COVID-19 and other pneumonia. The models are tested on eight merged datasets and on individual test sets in order to confirm the degree of generalization of the proposed algorithms. An explainability algorithm is also developed to visually show the location of the lung-infected areas detected by the model. Moreover, we provide a detailed analysis of the misclassified images. The obtained results achieve high performances with an Area Under Curve (AUC) of 0.97 for multi-class classification (COVID-19 vs. other pneumonia vs. normal) and 0.98 for the binary model (COVID-19 vs. normal). The average sensitivity and specificity are 0.97 and 0.98, respectively. The sensitivity of the COVID-19 class achieves 0.99. The results outperformed the comparable state-of-the-art models for the detection of COVID-19 on CXR images. The explainability model shows that our model is able to efficiently identify the signs of COVID-19.

Myocarditis and Subclinical-Like Infection Associated With SARS-CoV-2 in Two Cats Living in the Same Household in France: A Case Report With Literature Review

This report provides the first clinical, radiographic, echocardiographic, and biological description of SARS-CoV-2-associated myocarditis with a 6-month follow-up in a 5-year-old obese male domestic shorthair cat (Cat-1) presented for refractory congestive heart failure, with high cardiac troponin-I level (5.24 ng/ml), and a large lingual ulcer. The animal was SARS-CoV-2 positive on serology. The other cat living in the same household (Cat-2) never showed any clinical sign but was also confirmed SARS-CoV-2 positive on serology. Both cats were SARS-CoV-2 PCR negative. Cat-1 had closer contact than Cat-2 with their owner, who had been in close contact with a coworker tested PCR positive for COVID-19 (Alpha (B.1.1.7) variant) 4 weeks before Cat-1's first episode of congestive heart failure. A focused point-of-care echocardiography at presentation revealed for Cat-1 numerous B-lines, pleural effusion, severe left atrial dilation and dysfunction, and hypertrophic cardiomyopathy phenotype associated with focal pulmonary consolidations. Both myocarditis and pneumonia were suspected, leading to the prescription of cardiac medications and antibiotics. One month later, Cat-1 recovered, with normalization of left atrial size and function, and radiographic and echocardiography disappearance of heart failure signs and pulmonary lesions. An extensive literature review of SARS-CoV-2-related cardiac injury in pets in comparison with human pathology is discussed.

One year of COVID-19 pandemic: what we Radiologists have learned about imaging

BACKGROUND

 Since its outbreak in December 2019, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has infected more than 151 million people worldwide. More than 3.1 million have died from Coronavirus Disease 2019 (COVID-19), the illness caused by SARS-CoV-2. The virus affects mainly the upper respiratory tract and the lungs causing pneumonias of varying severity. Moreover, via direct and indirect pathogenetic mechanisms, SARS-CoV-2 may lead to a variety of extrapulmonary as well as vascular manifestations.

METHODS

 Based on a systematic literature search via PubMed, original research articles, meta-analyses, reviews, and case reports representing the current scientific knowledge regarding diagnostic imaging of COVID-19 were selected. Focusing on the imaging appearance of pulmonary and extrapulmonary manifestations as well as indications for imaging, these data were summarized in the present review article and correlated with basic pathophysiologic mechanisms.

RESULTS AND CONCLUSION

 Typical signs of COVID-19 pneumonia are multifocal, mostly bilateral, rounded, polycyclic or geographic ground-glass opacities and/or consolidations with mainly peripheral distribution. In severe cases, peribronchovascular lung zones are affected as well. Other typical signs are the "crazy paving" pattern and the halo and reversed halo (the latter two being less common). Venous thromboembolism (and pulmonary embolism in particular) is the most frequent vascular complication of COVID-19. However, arterial thromboembolic events like ischemic strokes, myocardial infarctions, and systemic arterial emboli also occur at higher rates. The most frequent extrapulmonary organ manifestations of COVID-19 affect the central nervous system, the heart, the hepatobiliary system, and the gastrointestinal tract. Usually, they can be visualized in imaging studies as well. The most important imaging modality for COVID-19 is chest CT. Its main purpose is not to make the primary diagnosis, but to differentiate COVID-19 from other (pulmonary) pathologies, to estimate disease severity, and to detect concomitant diseases and complications.

KEY POINTS

  · Typical signs of COVID-19 pneumonia are multifocal, mostly peripheral ground-glass opacities/consolidations.. · Imaging facilitates differential diagnosis, estimation of disease severity, and detection of complications.. · Venous thromboembolism (especially pulmonary embolism) is the predominant vascular complication of COVID-19.. · Arterial thromboembolism (e. g., ischemic strokes, myocardial infarctions) occurs more frequently as well.. · The most common extrapulmonary manifestations affect the brain, heart, hepatobiliary system, and gastrointestinal system..

CITATION FORMAT

· Gross A, Albrecht T. One year of COVID-19 pandemic: what we Radiologists have learned about imaging. Fortschr Röntgenstr 2021; DOI: 10.1055/a-1522-3155.

Development and prospective validation of COVID-19 chest X-ray screening model for patients attending emergency departments

Chest X-rays (CXRs) are the first-line investigation in patients presenting to emergency departments (EDs) with dyspnoea and are a valuable adjunct to clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to facilitate rapid triage of CXRs for further patient testing and/or isolation. In this work we develop an AI algorithm, CovIx, to differentiate normal, abnormal, non-COVID-19 pneumonia, and COVID-19 CXRs using a multicentre cohort of 293,143 CXRs. The algorithm is prospectively validated in 3289 CXRs acquired from patients presenting to ED with symptoms of COVID-19 across four sites in NHS Greater Glasgow and Clyde. CovIx achieves area under receiver operating characteristic curve for COVID-19 of 0.86, with sensitivity and F1-score up to 0.83 and 0.71 respectively, and performs on-par with four board-certified radiologists. AI-based algorithms can identify CXRs with COVID-19 associated pneumonia, as well as distinguish non-COVID pneumonias in symptomatic patients presenting to ED. Pre-trained models and inference scripts are freely available at https://github.com/beringresearch/bravecx-covid.

Detection of COVID-19 findings by the local interpretable model-agnostic explanations method of types-based activations extracted from CNNs

Covid-19 is a disease that affects the upper and lower respiratory tract and has fatal consequences in individuals. Early diagnosis of COVID-19 disease is important. Datasets used in this study were collected from hospitals in Istanbul. The first dataset consists of COVID-19, viral pneumonia, and bacterial pneumonia types. The second dataset consists of the following findings of COVID-19: ground glass opacity, ground glass opacity, and nodule, crazy paving pattern, consolidation, consolidation, and ground glass. The approach suggested in this paper is based on artificial intelligence. The proposed approach consists of three steps. As a first step, preprocessing was applied and, in this step, the Fourier Transform and Gradient-weighted Class Activation Mapping methods were applied to the input images together. In the second step, type-based activation sets were created with three different ResNet models before the Softmax method. In the third step, the best type-based activations were selected among the CNN models using the local interpretable model-agnostic explanations method and re-classified with the Softmax method. An overall accuracy success of 99.15% was achieved with the proposed approach in the dataset containing three types of image sets. In the dataset consisting of COVID-19 findings, an overall accuracy success of 99.62% was achieved with the recommended approach.

Neurointervention in the Era of COVID-19: Korean Nationwide Survey, Literature Review, and Recommendations

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a tremendous impact on healthcare systems worldwide. Although the most common presentation of COVID-19 is respiratory illness, neurologic manifestations are increasing and the pandemic may have consequential effects on urgent conditions such as acute ischemic stroke. In this document, we describe the current status of neurointervention in Korea affected by COVID-19 based on a nationwide survey and review relevant literature from other countries and professional societies.

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

Purpose

To explore whether chest X-ray severity scoring (CX-SS) could be reliable to assess the severity of pulmonary parenchymal disease in COVID-19 patients.

Material and methods

The study consisted of 325 patients whose COVID-19 was confirmed by RT-PCR test and who underwent chest X-ray and computed tomography (CT) studies to assess parenchymal disease severity. Only 195 cases included in the final analysis after exclusion of cases with previous chest disease and cases having more than 24 hours interval between their X-ray and CT chest studies. Both chest X-ray and CT severity scores (CT-SS) were recorded by 2 experienced radiologists and were compared to the clinical severity. Interobserver agreement was assessed for CX-SS and CT-SS.

Results

In relation to the clinical severity, the sensitivity of the CX-SS for diagnosis of moderate to severe parenchymal disease was high (90.4% and 100%) and low for mild cases (66.2%), while the specificity was high for mild to moderate parenchymal disease (100%) compared to severe cases (86.7%). The sensitivity, specificity, and diagnostic accuracy of the CT-SS were higher than CX-SS. Pearson correlation coefficient demonstrated a strong positive correlation between CX-SS and CT-SS (rs = 0.88, p < 0.001). The inter-observer agreement for CX-SS was good (k = 0.79, p = 0.001), and it was excellent for CT-SS (k = 0.85, p = 0.001).

Conclusions

CX-SS is reliable to assess the severity of COVID-19 pulmonary parenchymal disease, especially in moderate and severe cases, with the tendency of overestimation of severe cases.

Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning

BACKGROUND

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, in late 2019 and created a global pandemic that overwhelmed healthcare systems. COVID-19, as of July 3, 2021, yielded 182 million confirmed cases and 3.9 million deaths globally according to the World Health Organization. Several patients who were initially diagnosed with mild or moderate COVID-19 later deteriorated and were reclassified to severe disease type.

OBJECTIVE

The aim is to create a predictive model for COVID-19 ventilatory support and mortality early on from baseline (at the time of diagnosis) and routinely collected data of each patient (CXR, CBC, demographics, and patient history).

METHODS

Four common machine learning algorithms, three data balancing techniques, and feature selection are used to build and validate predictive models for COVID-19 mechanical requirement and mortality. Baseline CXR, CBC, demographic, and clinical data were retrospectively collected from April 2, 2020, till June 18, 2020, for 5739 patients with confirmed PCR COVID-19 at King Abdulaziz Medical City in Riyadh. However, of those patients, only 1508 and 1513 have met the inclusion criteria for ventilatory support and mortalilty endpoints, respectively.

RESULTS

In an independent test set, ventilation requirement predictive model with top 20 features selected with reliefF algorithm from baseline radiological, laboratory, and clinical data using support vector machines and random undersampling technique attained an AUC of 0.87 and a balanced accuracy of 0.81. For mortality endpoint, the top model yielded an AUC of 0.83 and a balanced accuracy of 0.80 using all features with balanced random forest. This indicates that with only routinely collected data our models can predict the outcome with good performance. The predictive ability of combined data consistently outperformed each data set individually for intubation and mortality. For the ventilator support, chest X-ray severity annotations alone performed better than comorbidity, complete blood count, age, or gender with an AUC of 0.85 and balanced accuracy of 0.79. For mortality, comorbidity alone achieved an AUC of 0.80 and a balanced accuracy of 0.72, which is higher than models that use either chest radiograph, laboratory, or demographic features only.

CONCLUSION

The experimental results demonstrate the practicality of the proposed COVID-19 predictive tool for hospital resource planning and patients' prioritization in the current COVID-19 pandemic crisis.

Lung Ultrasound Examination in Patients with SARS-CoV-2 Infection: Multicenter Study

BACKGROUND

The COVID-19 pandemic has, by necessity, contributed to rapid advancements in medicine. Owing to the necessity of following strict anti-epidemic sanitary measures when taking care of infected patients, the accessibility of standard diagnostic methods may be limited. Consequently, the significance and potential of bedside diagnostic modalities increase, including lung ultrasound (LUS).

METHOD

Multicenter registry study involving adult patients with confirmed COVID-19, for whom LUS was performed.

RESULTS

A total of 228 patients (61% males) qualified for the study. The average age was 60 years (±14), 40% were older than 65 years of age. In 130 from 173 hospitalized patients, HRCT (high-resolution computed tomography) was performed. In 80% of patients, LUS findings indicated interstitial pneumonia. In hospitalized patients multifocally located single B-lines, symmetrical B-lines, and areas of white lung were significantly more frequent as compared to ambulatory patients. LUS findings, both those indicating interstitial syndrome and consolidations, were positively correlated with HRCT images. As compared to HRCT, the sensitivity and specificity of LUS in detecting interstitial pneumonia were 97% and 100%, respectively.

CONCLUSIONS

As compared to HRCT, LUS is characterized by a very high sensitivity and specificity in detecting interstitial pneumonia in COVID-19 patients. Potentially, LUS can be a particularly useful diagnostic modality for COVID-19 patients pneumonia.

Initial findings in chest X-rays as predictors of worsening lung infection in patients with COVID-19: correlation in 265 patients

Background and aims

We aimed to analyze the relationship between the initial chest X-ray findings in patients with severe acute respiratory syndrome due to infection with SARS-CoV-2 and eventual clinical worsening and to compare three systems of quantifying these findings.

Material and methods

This retrospective study reviewed the clinical and radiological evolution of 265 adult patients with COVID-19 attended at our center between March 2020 and April 2020. We recorded data related to patients’ comorbidities, hospital stay, and clinical worsening (admission to the ICU, intubation, and death). We used three scoring systems taking into consideration 6 or 8 lung fields (designated 6A, 6B, and 8) to quantify lung involvement in each patient’s initial pathological chest X-ray and to classify its severity as mild, moderate, or severe, and we compared these three systems. We also recorded the presence of alveolar opacities and linear opacities (fundamentally linear atelectasis) in the first chest X-ray with pathologic findings.

Results

In the χ2 analysis, moderate or severe involvement in the three classification systems correlated with hospital admission (P = .009 in 6A, P = .001 in 6B, and P = .001 in 8) and with death (P = .02 in 6A, P = .01 in 6B, and P = .006 in 8). In the regression analysis, the most significant associations were 6B with alveolar involvement (OR 2.3; 95%CI 1.1.–4.7; P = .025;) and 8 with alveolar involvement (OR 2.07; 95% CI 1.01.–4.25; P = .046). No differences were observed in the ability of the three systems to predict clinical worsening by classifications of involvement in chest X-rays as moderate or severe.

Conclusion

Moderate/severe extension in the three chest X-ray scoring systems evaluating the extent of involvement over 6 or 8 lung fields and the finding of alveolar opacities in the first pathologic X-ray correlated with mortality and the rate of hospitalization in the patients studied. No significant difference was found in the predictive ability of the three classification systems proposed.

A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia

INTRODUCTION

Coronavirus disease 2019 (COVID-19) led to a global pandemic. Although reverse transcription polymerase chain reaction (RT-PCR) of viral nucleic acid is the gold standard for COVID-19 diagnosis, its sensitivity was found to not be high enough in many reports. As radiomics-based diagnosis research has recently emerged, we aimed to use computerized tomography (CT)-based radiomics models to differentiate COVID-19 pneumonia from other viral pneumonia infections.

MATERIALS AND METHODS

This study was performed according to the preferred reporting items for systematic review and meta-analysis diagnostic test accuracy studies (PRISMA-DTA) guidelines. The Pubmed, Cochrane, and Embase databases were searched. The pooled sensitivity and pooled specificity were calculated. A summary receiver operating characteristic (sROC) curve was constructed. The study quality was evaluated based on the radiomics quality score.

RESULTS

A total of 10,300 patients were involved in this meta-analysis. The radiomics quality score ranged from 13 to 16 (maximum score: 36). The pooled sensitivity was 0.885 (95% CI: 0.818-0.929), and the pooled specificity was 0.811 (95% CI: 0.667-0.902). The pooled AUC was 906. Conclusion: Our meta-analysis showed that CT-based radiomics feature models can successfully differentiate COVID-19 from other viral pneumonias.

Highlighting COVID-19: What the imaging exams show about the disease

Coronavirus disease 2019 (COVID-19), a global emergency, is caused by severe acute respiratory syndrome coronavirus 2. The gold standard for its diagnosis is the reverse transcription polymerase chain reaction, but considering the high number of infected people, the low availability of this diagnostic tool in some contexts, and the limitations of the test, other tools that aid in the identification of the disease are necessary. In this scenario, imaging exams such as chest X-ray (CXR) and computed tomography (CT) have played important roles. CXR is useful for assessing disease progression because it allows the detection of extensive consolidations, besides being a fast and cheap method. On the other hand, CT is more sensitive for detecting lung changes in the early stages of the disease and is also useful for assessing disease progression. Of note, ground-glass opacities are the main COVID-19-related CT findings. Positron emission tomography combined with CT can be used to evaluate chronic and substantial damage to the lungs and other organs; however, it is an expensive test. Lung ultrasound (LUS) has been shown to be a promising technique in that context as well, being useful in the screening and monitoring of patients, disease classification, and management related to mechanical ventilation. Moreover, LUS is an inexpensive alternative available at the bedside. Finally, magnetic resonance imaging, although not usually requested, allows the detection of pulmonary, cardiovascular, and neurological abnormalities associated with COVID-19. Furthermore, it is important to consider the challenges faced in the radiology field in the adoption of control measures to prevent infection and in the follow-up of post-COVID-19 patients.

Mediastinal lymphadenopathy in COVID-19: A review of literature

A novel coronavirus disease 2019 (COVID-19) is a progressive viral disease that affected people around the world with widespread morbidity and mortality. Patients with COVID-19 infection typically had pulmonary manifestation but can also present with gastrointestinal, cardiac, or neurological system dysfunction. Chest imaging in patients with COVID-19 commonly show bilateral lung involvement with bilateral ground-glass opacity and consolidation. Mediastinal lymphadenopathy can be found due to infectious or non-infectious etiologies. It is commonly found to be associated with malignant diseases, sarcoidosis, and heart failure. Mediastinal lymph node enlargement is not a typical computer tomography of the chest finding of patients with COVID-19 infection. We summarized the literature which suggested or investigated the mediastinal lymph node enlargement in patients with COVID-19 infection. Further studies are needed to better characterize the importance of mediastinal lymphadenopathy in patients with COVID-19 infection.

Radiological and clinical spectrum of COVID-19: A major concern for public health

The pandemic of novel coronavirus disease 2019 (COVID-19) is an infectious disease caused by +ve strand RNA virus (SARS-CoV-2, severe acute respiratory syndrome coronavirus 2) that belongs to the corona viridae family. In March, the World Health Organization declared the outbreak of novel coronavirus for the public health emergency. Although SARS-CoV-2 infection presents with respiratory symptoms, it affects other organs such as the kidneys, liver, heart and brain. Early-stage laboratory disease testing shows many false positive or negative outcomes such as less white blood cell count and a low number of lymphocyte count. However, radiological examination and diagnosis are among the main components of the diagnosis and treatment of COVID-19. In particular, for COVID-19, chest computed tomography developed vigorous initial diagnosis and disease progression assessment. However, the accuracy is limited. Although real-time reverse transcription-polymerase chain reaction is the gold standard method for the diagnosis of COVID-19, sometimes it may give false-negative results. Due to the consequences of the missing diagnosis. This resulted in a discrepancy between the two means of examination. Conversely, based on currently available evidence, we summarized the possible understanding of the various patho-physiology, radio diagnostic methods in severe COVID-19 patients. As the information on COVID-19 evolves rapidly, this review will provide vital information for scientists and clinicians to consider novel perceptions for the comprehensive knowledge of the diagnostic approaches based on current experience.