Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR

Exhaled Volatile Organic Compounds Detection in Pneumonia Screening: A Comprehensive Meta-analysis

BACKGROUND

Pneumonia is a common lower respiratory tract infection, and early diagnosis is crucial for timely treatment and improved prognosis. Traditional diagnostic methods for pneumonia, such as chest imaging and microbiological examinations, have certain limitations. Exhaled volatile organic compounds (VOCs) detection, as an emerging non-invasive diagnostic technique, has shown potential application value in pneumonia screening.

METHOD

A systematic search was conducted on PubMed, Embase, Cochrane Library, and Web of Science, with the retrieval time up to March 2024. The inclusion criteria were diagnostic studies evaluating exhaled VOCs detection for the diagnosis of pneumonia, regardless of the trial design type. A meta-analysis was performed using a bivariate model for sensitivity and specificity.

RESULTS

A total of 14 diagnostic studies were included in this meta-analysis. The pooled results demonstrated that exhaled VOCs detection had a combined sensitivity of 0.94 (95% CI: 0.92-0.95) and a combined specificity of 0.83 (95% CI: 0.81-0.84) in pneumonia screening, with an area under the summary receiver operating characteristic (SROC) curve (AUC) of 0.96. The pooled diagnostic odds ratio (DOR) was 104.37 (95% CI: 27.93-390.03), and the pooled positive and negative likelihood ratios (LR) were 8.98 (95% CI: 3.88-20.80) and 0.11 (95% CI: 0.05-0.22), indicating a high diagnostic performance.

CONCLUSION

This study highlights the potential of exhaled VOCs detection as an effective, non-invasive screening method for pneumonia, which could facilitate future diagnosis in pneumonia. Further high-quality, large-scale studies are required to confirm the clinical utility of exhaled VOCs detection in pneumonia screening.

STUDY REGISTRATION

PROSPERO, Review no. CRD42024520498.

Nonrigid registration method for longitudinal chest CT images in COVID-19

Rationale and objectives

To analyze morphological changes in patients with COVID-19-associated pneumonia over time, a nonrigid registration technique is required that reduces differences in respiratory phase and imaging position and does not excessively deform the lesion region. A nonrigid registration method using deep learning was applied for lung field alignment, and its practicality was verified through quantitative evaluation, such as image similarity of whole lung region and image similarity of lesion region, as well as visual evaluation by a physician.

Materials and methods

First, the lung field positions and sizes of the first and second CT images were roughly matched using a classical registration method based on iterative calculations as a preprocessing step. Then, voxel-by-voxel transformation was performed using VoxelMorph, a nonrigid deep learning registration method. As an objective evaluation, the similarity of the images was calculated. To evaluate the invariance of image features in the lesion site, primary statistics and 3D shape features were calculated and statistically analyzed. Furthermore, as a subjective evaluation, the similarity of images and whether nonrigid transformation caused unnatural changes in the shape and size of the lesion region were visually evaluated by a pulmonologist.

Results

The proposed method was applied to 509 patient data points with high image similarity. The variances in histogram characteristics before and after image deformation were confirmed. Visual evaluation confirmed the agreement between the shape and internal structure of the lung field and the natural deformation of the lesion region.

Conclusion

The developed nonrigid registration method was shown to be effective for quantitative time series analysis of the lungs.

A Joint Classification Method for COVID-19 Lesions Based on Deep Learning and Radiomics

Pneumonia caused by novel coronavirus is an acute respiratory infectious disease. Its rapid spread in a short period of time has brought great challenges for global public health. The use of deep learning and radiomics methods can effectively distinguish the subtypes of lung diseases, provide better clinical prognosis accuracy, and assist clinicians, enabling them to adjust the clinical management level in time. The main goal of this study is to verify the performance of deep learning and radiomics methods in the classification of COVID-19 lesions and reveal the image characteristics of COVID-19 lung disease. An MFPN neural network model was proposed to extract the depth features of lesions, and six machine-learning methods were used to compare the classification performance of deep features, key radiomics features and combined features for COVID-19 lung lesions. The results show that in the COVID-19 image classification task, the classification method combining radiomics and deep features can achieve good classification results and has certain clinical application value.

Semi-supervised CT image segmentation via contrastive learning based on entropy constraints

Deep learning-based methods for fast target segmentation of computed tomography (CT) imaging have become increasingly popular. The success of current deep learning methods usually depends on a large amount of labeled data. Labeling medical data is a time-consuming and laborious task. Therefore, this paper aims to enhance the segmentation of CT images by using a semi-supervised learning method. In order to utilize the valid information in unlabeled data, we design a semi-supervised network model for contrastive learning based on entropy constraints. We use CNN and Transformer to capture the image's local and global feature information, respectively. In addition, the pseudo-labels generated by the teacher networks are unreliable and will lead to degradation of the model performance if they are directly added to the training. Therefore, unreliable samples with high entropy values are discarded to avoid the model extracting the wrong features. In the student network, we also introduce the residual squeeze and excitation module to learn the connection between different channels of each layer feature to obtain better segmentation performance. We demonstrate the effectiveness of the proposed method on the COVID-19 CT public dataset. We mainly considered three evaluation metrics: DSC, HD95, and JC. Compared with several existing state-of-the-art semi-supervised methods, our method improves DSC by 2.3%, JC by 2.5%, and reduces HD95 by 1.9 mm. In this paper, a semi-supervised medical image segmentation method is designed by fusing CNN and Transformer and utilizing entropy-constrained contrastive learning loss, which improves the utilization of unlabeled medical images.

Novel Deep CNNs Explore Regions, Boundaries, and Residual Learning for COVID-19 Infection Analysis in Lung CT

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

Imaging of pulmonary infections encountered in the emergency department in post-COVID 19 era- common, rare and exotic. Bacterial and viral

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

Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey

This paper addresses the significant problem of identifying the relevant background and contextual literature related to deep learning (DL) as an evolving technology in order to provide a comprehensive analysis of the application of DL to the specific problem of pneumonia detection via chest X-ray (CXR) imaging, which is the most common and cost-effective imaging technique available worldwide for pneumonia diagnosis. This paper in particular addresses the key period associated with COVID-19, 2020-2023, to explain, analyze, and systematically evaluate the limitations of approaches and determine their relative levels of effectiveness. The context in which DL is applied as both an aid to and an automated substitute for existing expert radiography professionals, who often have limited availability, is elaborated in detail. The rationale for the undertaken research is provided, along with a justification of the resources adopted and their relevance. This explanatory text and the subsequent analyses are intended to provide sufficient detail of the problem being addressed, existing solutions, and the limitations of these, ranging in detail from the specific to the more general. Indeed, our analysis and evaluation agree with the generally held view that the use of transformers, specifically, vision transformers (ViTs), is the most promising technique for obtaining further effective results in the area of pneumonia detection using CXR images. However, ViTs require extensive further research to address several limitations, specifically the following: biased CXR datasets, data and code availability, the ease with which a model can be explained, systematic methods of accurate model comparison, the notion of class imbalance in CXR datasets, and the possibility of adversarial attacks, the latter of which remains an area of fundamental research.

CMM: A CNN-MLP Model for COVID-19 Lesion Segmentation and Severity Grading

In this paper, a CNN-MLP model (CMM) is proposed for COVID-19 lesion segmentation and severity grading in CT images. The CMM starts by lung segmentation using UNet, and then segmenting the lesion from the lung region using a multi-scale deep supervised UNet (MDS-UNet), finally implementing the severity grading by a multi-layer preceptor (MLP). In MDS-UNet, shape prior information is fused with the input CT image to reduce the searching space of the potential segmentation outputs. The multi-scale input compensates for the loss of edge contour information in convolution operations. In order to enhance the learning of multiscale features, the multi-scale deep supervision extracts supervision signals from different upsampling points on the network. In addition, it is empirical that the lesion which has a whiter and denser appearance tends to be more severe in the COVID-19 CT image. So, the weighted mean gray-scale value (WMG) is proposed to depict this appearance, and together with the lung and lesion area to serve as input features for the severity grading in MLP. To improve the precision of lesion segmentation, a label refinement method based on the Frangi vessel filter is also proposed. Comparative experiments on COVID-19 public datasets show that our proposed CMM achieves high accuracy on COVID-19 lesion segmentation and severity grading.

Visual Knowledge Domain of Artificial Intelligence in Computed Tomography: A Review Based on Bibliometric Analysis

ABSTRACT

Artificial intelligence (AI)-assisted medical imaging technology is a new research area of great interest that has developed rapidly over the last decade. However, there has been no bibliometric analysis of published studies in this field. The present review focuses on AI-related studies on computed tomography imaging in the Web of Science database and uses CiteSpace and VOSviewer to generate a knowledge map and conduct the basic information analysis, co-word analysis, and co-citation analysis. A total of 7265 documents were included and the number of documents published had an overall upward trend. Scholars from the United States and China have made outstanding achievements, and there is a general lack of extensive cooperation in this field. In recent years, the research areas of great interest and difficulty have been the optimization and upgrading of algorithms, and the application of theoretical models to practical clinical applications. This review will help researchers understand the developments, research areas of great interest, and research frontiers in this field and provide reference and guidance for future studies.

COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography

Background and novelty

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

Methodology

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

Results

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

Conclusion

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

Effect of Model-Based Iterative Reconstruction on Image Quality of Chest Computed Tomography for COVID-19 Pneumonia

PURPOSE

This study aimed to compare the image quality of chest computed tomography (CT) scans for COVID-19 pneumonia using forward-projected model-based iterative reconstruction solution-LUNG (FIRST-LUNG) with filtered back projection (FBP) and hybrid iterative reconstruction (HIR).

METHOD

The CT images of 44 inpatients diagnosed with COVID-19 pneumonia between December 2022 and June 2023 were retrospectively analyzed. The CT images were reconstructed using FBP, HIR, and FIRST-LUNG-MILD/STANDARD/STRONG. The CT values and noise of the lumen of the main trachea and erector spine muscle were measured for each group. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Subjective evaluations included overall image quality, noise, streak artifact, visualization of normal lung structures, and abnormal CT features. One-way analysis of variance was used to compare the objective and subjective indicators among the five groups. The task-based transfer function was derived for three distinct contrasts representing anatomical structures, lower-contrast lesion, and higher-contrast lesion.

RESULTS

The results of the study demonstrated significant differences in image noise, SNR, and CNR among the five groups (P < 0.001). The FBP images exhibited the highest levels of noise and the lowest SNR and CNR among the five groups (P < 0.001). When compared to the FBP and HIR groups, the noise was lower in the FIRST-LUNG-MILD/STANDARD/STRONG group, while the SNR and CNR were higher (P < 0.001). The subjective overall image quality score of FIRST-LUNG-MILD/STANDARD was significantly better than FBP and FIRST-LUNG-STRONG (P < 0.001). FIRST-LUNG-MILD was superior to FBP, HIR, FIRST-LUNG-STANDARD, and FIRST-LUNG-STRONG in visualizing proximal and peripheral bronchovascular and subpleural vessels (P < 0.05). Additionally, FIRST-LUNG-MILD achieved the best scores in evaluating abnormal lung structure (P < 0.001). The overall interobserver agreement was substantial (intraclass correlation coefficient = 0.891). The task-based transfer function 50% values of FIRST reconstructions are consistently higher compared to FBP and HIR.

CONCLUSIONS

The FIRST-LUNG-MILD/STANDARD algorithm can enhance the image quality of chest CT in patients with COVID-19 pneumonia, while preserving important details of the lesions, better than the FBP and HIR algorithms. After evaluating various COVID-19 pneumonia lesions and considering the improvement in image quality, we recommend using the FIRST-LUNG-MILD reconstruction for diagnosing COVID-19 pneumonia.

Removing Adversarial Noise in X-ray Images via Total Variation Minimization and Patch-Based Regularization for Robust Deep Learning-based Diagnosis

Deep learning has significantly advanced the field of radiology-based disease diagnosis, offering enhanced accuracy and efficiency in detecting various medical conditions through the analysis of complex medical images such as X-rays. This technology's ability to discern subtle patterns and anomalies has proven invaluable for swift and accurate disease identification. The relevance of deep learning in radiology has been particularly highlighted during the COVID-19 pandemic, where rapid and accurate diagnosis is crucial for effective treatment and containment. However, recent research has uncovered vulnerabilities in deep learning models when exposed to adversarial attacks, leading to incorrect predictions. In response to this critical challenge, we introduce a novel approach that leverages total variation minimization to combat adversarial noise within X-ray images effectively. Our focus narrows to COVID-19 diagnosis as a case study, where we initially construct a classification model through transfer learning designed to accurately classify lung X-ray images encompassing no pneumonia, COVID-19 pneumonia, and non-COVID pneumonia cases. Subsequently, we extensively evaluated the model's susceptibility to targeted and un-targeted adversarial attacks by employing the fast gradient sign gradient (FGSM) method. Our findings reveal a substantial reduction in the model's performance, with the average accuracy plummeting from 95.56 to 19.83% under adversarial conditions. However, the experimental results demonstrate the exceptional efficacy of the proposed denoising approach in enhancing the performance of diagnosis models when applied to adversarial examples. Post-denoising, the model exhibits a remarkable accuracy improvement, surging from 19.83 to 88.23% on adversarial images. These promising outcomes underscore the potential of denoising techniques to fortify the resilience and reliability of AI-based COVID-19 diagnostic systems, laying the foundation for their successful deployment in clinical settings.

A Literature Review on the Relative Diagnostic Accuracy of Chest CT Scans versus RT-PCR Testing for COVID-19 Diagnosis

BACKGROUND

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

METHOD

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

FINDINGS

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

CONCLUSION

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

Association between volume of lung damage and endoplasmic reticulum stress expression among severe COVID-19 ICU patients

Introduction

Links have been established between SARS-CoV-2 and endoplasmic reticulum stress (ERS). However, the relationships between inflammation, ERS, and the volume of organ damage are not well known in humans. The aim of this study was to explore whether ERS explains lung damage volume (LDV) among COVID-19 patients admitted to the intensive care unit (ICU).

Materials and methods

We conducted a single-center retrospective study (ancillary analysis of a prospective cohort) including severe COVID-19 ICU patients who had a chest computed tomography (CT) scan 24 h before/after admission to assess LDV. We performed two multivariate linear regression models to identify factors associated with plasma levels of 78 kDa-Glucose-Regulated Protein (GRP78; ERS marker) and Interleukin-6 (IL-6; inflammation marker) at admission.

Results

Among 63 patients analyzed, GRP78 plasma level was associated with LDV in both multivariate models (β = 22.23 [4.08;40.38]; p = 0.0179, β = 20.47 [0.74;40.20]; p = 0.0423) but not with organ failure (Sequential Organ Failure Assessment (SOFA) score) at admission (r = 0.03 [-0.22;0.28]; p = 0.2559). GRP78 plasma level was lower among ICU survivors (1539.4 [1139.2;1941.1] vs. 1714.2 [1555.2;2579.1] pg./mL. respectively; p = 0.0297). IL-6 plasma level was associated with SOFA score at admission in both multivariate models (β = 136.60 [65.50;207.70]; p = 0.0003, β = 193.70 [116.60;270.90]; p < 0.0001) but not with LDV (r = 0.13 [-0.14;0.39]; p = 0.3219). IL-6 plasma level was not different between ICU survivors and non-survivors (12.2 [6.0;43.7] vs. 30.4 [12.9;69.7] pg./mL. respectively; p = 0.1857). There was no correlation between GRP78 and IL-6 plasma levels (r = 0.13 [-0.13;0.37]; p = 0.3106).

Conclusion

Among severe COVID-19 patients, ERS was associated with LDV but not with systemic inflammation, while systemic inflammation was associated with organ failure but not with LDV.

MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation

The rapid spread of the new pandemic, i.e., coronavirus disease 2019 (COVID-19), has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected area segmentation from computed tomography (CT) image, has attracted much attention by serving as an adjunct to increase the accuracy of COVID-19 screening and clinical diagnosis. Although lesion segmentation is a hot topic, traditional deep learning methods are usually data-hungry with millions of parameters, easy to overfit under limited available COVID-19 training data. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional methods are usually computationally intensive. To address the above two problems, we propose MiniSeg, a lightweight model for efficient COVID-19 segmentation from CT images. Our efforts start with the design of an attentive hierarchical spatial pyramid (AHSP) module for lightweight, efficient, effective multiscale learning that is essential for image segmentation. Then, we build a two-path (TP) encoder for deep feature extraction, where one path uses AHSP modules for learning multiscale contextual features and the other is a shallow convolutional path for capturing fine details. The two paths interact with each other for learning effective representations. Based on the extracted features, a simple decoder is added for COVID-19 segmentation. For comparing MiniSeg to previous methods, we build a comprehensive COVID-19 segmentation benchmark. Extensive experiments demonstrate that the proposed MiniSeg achieves better accuracy because its only 83k parameters make it less prone to overfitting. Its high efficiency also makes it easy to deploy and develop. The code has been released at https://github.com/yun-liu/MiniSeg.

A Comparative Study of "Grafting to" and "Grafting from" Conjugation Methods for the Preparation of Antibody-Temperature-Responsive Polymer Conjugates

Early diagnosis of infectious diseases is still challenging particularly in a nonlaboratory environment or limited resources areas. Thus, sensitive, inexpensive, and easily handled diagnostic approaches are required. The lateral flow immunoassay (LFIA) is commonly used in the screening of infectious diseases despite its poor sensitivity, especially with low pathogenic loads (early stages of infection). This article introduces a novel polymeric material that might help in the enrichment and concentration of pathogens to overcome the LFIA misdiagnosis. To achieve this, we evaluated the efficiency of introducing poly(N-isopropylacrylamide) (PNIPAAm) into immunoglobulin G (IgG) as a model antibody using two different conjugation methods: grafting to (GT) and grafting from (GF). The IgG-PNIPAAm conjugates were characterized using SDS-PAGE, DLS, and temperature-responsive phase transition behavior. SDS-PAGE analysis revealed that the GF method was more efficient in introducing the polymer than the GT method, with calculated polymer introduction ratios of 61% and 34%, respectively. The GF method proved to be less susceptible to steric hindrance and more efficient in introducing high-molecular-weight polymers into proteins. These results are consistent with previous studies comparing the GT and GF methods in similar systems. This study represents an important step toward understanding how the choice of polymer incorporation method affects the properties of IgG-PNIPAAm conjugates. The synthesized polymer allowed binding and enrichment of mouse IgG that was used as a model antigen with a clear LFIA band. On the basis of our findings, this system might help in improving the sensitivity of simple diagnostics.

Reinvestigating the performance of artificial intelligence classification algorithms on COVID-19 X-Ray and CT images

There are concerns that artificial intelligence (AI) algorithms may create underdiagnosis bias by mislabeling patient individuals with certain attributes (e.g., female and young) as healthy. Addressing this bias is crucial given the urgent need for AI diagnostics facing rapidly spreading infectious diseases like COVID-19. We find the prevalent AI diagnostic models show an underdiagnosis rate among specific patient populations, and the underdiagnosis rate is higher in some intersectional specific patient populations (for example, females aged 20-40 years). Additionally, we find training AI models on heterogeneous datasets (positive and negative samples from different datasets) may lead to poor model generalization. The model's classification performance varies significantly across test sets, with the accuracy of the better performance being over 40% higher than that of the poor performance. In conclusion, we developed an AI bias analysis pipeline to help researchers recognize and address biases that impact medical equality and ethics.

The top 50 most-cited articles about COVID-19 and the complications of COVID-19: A bibliometric analysis

Background

This bibliometric analysis examines the top 50 most-cited articles on COVID-19 complications, offering insights into the multifaceted impact of the virus. Since its emergence in Wuhan in December 2019, COVID-19 has evolved into a global health crisis, with over 770 million confirmed cases and 6.9 million deaths as of September 2023. Initially recognized as a respiratory illness causing pneumonia and ARDS, its diverse complications extend to cardiovascular, gastrointestinal, renal, hematological, neurological, endocrinological, ophthalmological, hepatobiliary, and dermatological systems.

Methods

Identifying the top 50 articles from a pool of 5940 in Scopus, the analysis spans November 2019 to July 2021, employing terms related to COVID-19 and complications. Rigorous review criteria excluded non-relevant studies, basic science research, and animal models. The authors independently reviewed articles, considering factors like title, citations, publication year, journal, impact factor, authors, study details, and patient demographics.

Results

The focus is primarily on 2020 publications (96%), with all articles being open access. Leading journals include The Lancet, NEJM, and JAMA, with prominent contributions from Internal Medicine (46.9%) and Pulmonary Medicine (14.5%). China played a major role (34.9%), followed by France and Belgium. Clinical features were the primary study topic (68%), often utilizing retrospective designs (24%). Among 22,477 patients analyzed, 54.8% were male, with the most common age group being 26-65 years (63.2%). Complications of COVID-19 affected 13.9% of patients, with a recovery rate of 57.8%.

Conclusion

Analyzing these top-cited articles offers clinicians and researchers a comprehensive, timely understanding of influential COVID-19 literature. This approach uncovers attributes contributing to high citations and provides authors with valuable insights for crafting impactful research. As a strategic tool, this analysis facilitates staying updated and making meaningful contributions to the dynamic field of COVID-19 research.

Evaluation of the value of chest CT severity score in assessment of COVID-19 severity and short-term prognosis

Background

Evaluations have shown that the severity of pulmonary involvement is very important in the mortality rate of patients with coronavirus disease 2019 (COVID-19). The purpose of this study was to evaluate the value of chest CT severity score in assessment of COVID-19 severity and short-term prognosis.

Materials and Methods

This study was a cross-sectional study with a sample size of 197 patients, including all patients admitted to Rasoul Akram Hospital, with positive polymerase chain reaction, to investigate the relationship between computed tomography (CT) severity score and mortality. The demographic data and CT scan findings (including the pattern, side, and distribution of involvement), co-morbidities, and lab data were collected. Finally, gathered data were analyzed by SPSS-26.

Results

119 (60.4%) patients were male, and 78 (39.6%) were female. The mean age was 58.58 ± 17.3 years. Totally, 61 patients died; of those, 41 (67.2%) were admitted to the intensive care unit (ICU), so there was a significant relation between death and ICU admission (P value = 0.000). Diabetes was the most common co-morbidity, followed by hypertension and IHD. There was no significant relation between co-morbidities and death (P value = 0.13). The most common patterns of CTs were interlobular septal thickening and ground glass opacities, and a higher CT severity score was in the second week from the onset of symptoms, which was associated with more mortality (P value < 0.05).

Conclusion

Our study showed that a patient with a higher CT severity score of the second week had a higher risk of mortality. Also, association of the CT severity score, laboratory data, and symptoms could be applicable in predicting the patient's condition.

CGO-ensemble: Chaos game optimization algorithm-based fusion of deep neural networks for accurate Mpox detection

The rising global incidence of human Mpox cases necessitates prompt and accurate identification for effective disease control. Previous studies have predominantly delved into traditional ensemble methods for detection, we introduce a novel approach by leveraging a metaheuristic-based ensemble framework. In this research, we present an innovative CGO-Ensemble framework designed to elevate the accuracy of detecting Mpox infection in patients. Initially, we employ five transfer learning base models that integrate feature integration layers and residual blocks. These components play a crucial role in capturing significant features from the skin images, thereby enhancing the models' efficacy. In the next step, we employ a weighted averaging scheme to consolidate predictions generated by distinct models. To achieve the optimal allocation of weights for each base model in the ensemble process, we leverage the Chaos Game Optimization (CGO) algorithm. This strategic weight assignment enhances classification outcomes considerably, surpassing the performance of randomly assigned weights. Implementing this approach yields notably enhanced prediction accuracy compared to using individual models. We evaluate the effectiveness of our proposed approach through comprehensive experiments conducted on two widely recognized benchmark datasets: the Mpox Skin Lesion Dataset (MSLD) and the Mpox Skin Image Dataset (MSID). To gain insights into the decision-making process of the base models, we have performed Gradient Class Activation Mapping (Grad-CAM) analysis. The experimental results showcase the outstanding performance of the CGO-ensemble, achieving an impressive accuracy of 100% on MSLD and 94.16% on MSID. Our approach significantly outperforms other state-of-the-art optimization algorithms, traditional ensemble methods, and existing techniques in the context of Mpox detection on these datasets. These findings underscore the effectiveness and superiority of the CGO-Ensemble in accurately identifying Mpox cases, highlighting its potential in disease detection and classification.

CD-Net: Cascaded 3D Dilated convolutional neural network for pneumonia lesion segmentation

COVID-19 is a global pandemic that has caused significant global, social, and economic disruption. To effectively assist in screening and monitoring diagnosed cases, it is crucial to accurately segment lesions from Computer Tomography (CT) scans. Due to the lack of labeled data and the presence of redundant parameters in 3D CT, there are still significant challenges in diagnosing COVID-19 in related fields. To address the problem, we have developed a new model called the Cascaded 3D Dilated convolutional neural network (CD-Net) for directly processing CT volume data. To reduce memory consumption when cutting volume data into small patches, we initially design a cascade architecture in CD-Net to preserve global information. Then, we construct a Multi-scale Parallel Dilated Convolution (MPDC) block to aggregate features of different sizes and simultaneously reduce the parameters. Moreover, to alleviate the shortage of labeled data, we employ classical transfer learning, which requires only a small amount of data while achieving better performance. Experimental results conducted on the different public-available datasets verify that the proposed CD-Net has reduced the negative-positive ratio and outperformed other existing segmentation methods while requiring less data.

Chest CT Scan Findings in Children with COVID-19: A Systematic Review

Objectives

To gather, summarize, and appraise the available evidence on: 1) the accuracy of chest CT scan in diagnosing COVID-19 among children, and 2) the characteristic chest CT scan findings associated with COVID-19 pneumonia in children.

Methods

We comprehensively searched databases (MEDLINE, COCHRANE), clinical trial registries, bibliographic lists of selected studies, and unpublished data for relevant studies. Guide questions from the Painless Evidence Based Medicine and the National Institutes of Health Quality Assessment Tools were used to assess study quality.

Results

A poor quality study showed 86.0% (95% CI 73.8, 93.0) sensitivity and 75.9% (95% CI 67.1, 83.0) specificity of chest CT scan in diagnosing COVID-19 in children. Thirty-nine observational studies describing chest CT scan in children with COVID-19 showed abnormal findings in 717 of 1028 study subjects. Common chest CT scan findings in this population include: 1) ground glass opacities, patchy shadows, and consolidation, 2) lower lobe involvement, and 3) unilateral lung lesions.

Conclusion

Studies which investigate the accuracy of chest CT scan in the diagnosis of COVID-19 in children are limited by heterogeneous populations and small sample sizes. While chest CT scan findings such as patchy shadows, ground glass opacities, and consolidation are common in children with COVID-19, these may be similar to the imaging findings of other respiratory viral illnesses.

Interest of Chest CT to Assess the Prognosis of SARS-CoV-2 Pneumonia: An In-Hospital-Based Experience in Sub-Saharan Africa

Methods

We included all patients with respiratory symptoms (dyspnea, fever, and cough) and/or respiratory failure admitted to the SOS Médecins de nuit SARL hospital, DR Congo, during the 2nd and 3rd waves of the COVID-19 pandemic. The diagnosis of COVID-19 was established based on RT-PCR anti-SARS-CoV-2 tests (G1 (RT-PCR positive) vs. G2 (RT-PCR negative)), and all patients had a chest CT on the day of admission. We retrieved the digital files of patients, precisely the clinical, biological, and chest CT parameters of the day of admission as well as the vital outcome (survival or death). Chest CT were read by a very high-definition console using Advantage Windows software and exported to the hospital network using the RadiAnt DICOM viewer. To determine the threshold for the percentage of lung lesions associated with all-cause mortality, we used ROC curves. Factors associated with death, including chest CT parameters, were investigated using logistic regression analysis.

Results

The study included 200 patients (average age 56.2 ± 15.2 years; 19% diabetics and 4.5% obese), and COVID-19 was confirmed among 56% of them (G1). Chest CT showed that ground glass (72.3 vs. 39.8%), crazy paving (69.6 vs. 17.0%), and consolidation (83.9 vs. 22.7%), with bilateral and peripheral locations (68.8 vs. 30.7%), were more frequent in G1 vs. G2 (p < 0.001). No case of pulmonary embolism and fibrosis had been documented. The lung lesions affecting 30% of the parenchyma were informative in predicting death (area under the ROC curve at 0.705, p = 0.017). In multivariate analysis, a percentage of lesions affecting 50% of the lung parenchyma increased the risk of dying by 7.194 (1.656-31.250).

Conclusion

The chest CT demonstrated certain characteristic lesions more frequently in patients in whom the diagnosis of COVID-19 was confirmed. The extent of lesions affecting at least half of the lung parenchyma from the first day of admission to hospital increases the risk of death by a factor of 7.

Use of serum KL-6 and chest radiographic severity grade to predict 28-day mortality in COVID-19 patients with pneumonia: a retrospective cohort study

BACKGROUND

Coronavirus disease 2019 (COVID-19) has had a global social and economic impact. An easy assessment procedure to handily identify the mortality risk of inpatients is urgently needed in clinical practice. Therefore, the aim of this study was to develop a simple nomogram model to categorize patients who might have a poor short-term outcome.

METHODS

A retrospective cohort study of 189 COVID-19 patients was performed at Shanghai Ren Ji Hospital from December 12, 2022 to February 28, 2023. Chest radiography and biomarkers, including KL-6 were assessed. Risk factors of 28-day mortality were selected by a Cox regression model. A nomogram was developed based on selected variables by SMOTE strategy. The predictive performance of the derived nomogram was evaluated by calibration curve.

RESULTS

In total, 173 patients were enrolled in this study. The 28-day mortality event occurred in 41 inpatients (23.7%). Serum KL-6 and radiological severity grade (RSG) were selected as the final risk factors. A nomogram model was developed based on KL-6 and RSG. The calibration curve suggested that the nomogram model might have potential clinical value. The AUCs for serum KL-6, RSG, and the combined score in the development group and validation group were 0.885 (95% CI: 0.804-0.952), 0.818 (95% CI: 0.711-0.899), 0.868 (95% CI: 0.776-0.942) and 0.932 (95% CI: 0.862-0.997), respectively.

CONCLUSIONS

Our results suggested that the nomogram based on KL-6 and RSG might be a potential method for evaluating 28-day mortality in COVID-19 patients. A high combined score might indicate a poor outcome in COVID-19 patients with pneumonia.

Transmission electron microscopy reveals the presence of SARS-CoV-2 in human spermatozoa associated with an ETosis-like response

BACKGROUND

Severe acute syndrome coronavirus 2 can invade a variety of tissues, including the testis. Even though this virus is scarcely found in human semen polymerase chain reaction tests, autopsy studies confirm the viral presence in all testicular cell types, including spermatozoa and spermatids.

OBJECTIVE

To investigate whether the severe acute syndrome coronavirus 2 is present inside the spermatozoa of negative polymerase chain reaction-infected men up to 3 months after hospital discharge.

MATERIALS AND METHODS

This cross-sectional study included 13 confirmed moderate-to-severe COVID-19 patients enrolled 30-90 days after the diagnosis. Semen samples were obtained and examined with real-time polymerase chain reaction for RNA detection and by transmission electron microscopy.

RESULTS

In moderate-to-severe clinical scenarios, we identified the severe acute syndrome coronavirus 2 inside spermatozoa in nine of 13 patients up to 90 days after discharge from the hospital. Moreover, some DNA-based extracellular traps were reported in all studied specimens.

DISCUSSION AND CONCLUSION

Although severe acute syndrome coronavirus 2 was not present in the infected men's semen, it was intracellularly present in the spermatozoa till 3 months after hospital discharge. The Electron microscopy (EM) findings also suggest that spermatozoa produce nuclear DNA-based extracellular traps, probably in a cell-free DNA-dependent manner, similar to those previously described in the systemic inflammatory response to COVID-19. In moderate-to-severe cases, the blood-testes barrier grants little defence against different pathogenic viruses, including the severe acute syndrome coronavirus 2. The virus could also use the epididymis as a post-testicular route to bind and fuse to the mature spermatozoon and possibly accomplish the reverse transcription of the single-stranded viral RNA into proviral DNA. These mechanisms can elicit extracellular cell-free DNA formation. The potential implications of our findings for assisted conception must be addressed, and the evolutionary history of DNA-based extracellular traps as preserved ammunition in animals' innate defence might improve our understanding of the severe acute syndrome coronavirus 2 pathophysiology in the testis and spermatozoa.

Comparison of radiologic findings between SARS-CoV-2 and other respiratory tract viruses in critically ill children during the COVID-19 pandemic

Background/aim

This study was planned because the radiological distinction of COVID-19 and respiratory viral panel (RVP)-positive cases is necessary to prioritize intensive care needs and ensure non-COVID-19 cases are not overlooked. With that purpose, the objective of this study was to compare radiologic findings between SARS-CoV-2 and other respiratory airway viruses in critically ill children with suspected COVID-19 disease.

Materials and methods

This study was conducted as a multicenter, retrospective, observational, and cohort study in 24 pediatric intensive care units between March 1 and May 31, 2020. SARS-CoV-2- or RVP polymerase chain reaction (PCR)-positive patients' chest X-ray and thoracic computed tomography (CT) findings were evaluated blindly by pediatric radiologists.

Results

We enrolled 225 patients in the study, 81 of whom tested positive for Coronovirus disease-19 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The median age of all patients was 24 (7-96) months, while it was 96 (17-156) months for COVID-19-positive patients and 17 (6-48) months for positive for other RVP factor (p < 0.001). Chest X-rays were more frequently evaluated as normal in patients with SARS-CoV-2 positive results (p = 0.020). Unilateral segmental or lobar consolidation was observed more frequently on chest X-rays in rhinovirus cases than in other groups (p = 0.038). CT imaging findings of bilateral peribronchial thickening and/or peribronchial opacity were more frequently observed in RVP-positive patients (p = 0.046).

Conclusion

Chest X-ray and CT findings in COVID-19 patients are not specific and can be seen in other respiratory virus infections.

Lung ultrasound score predicts outcomes in patients with acute respiratory failure secondary to COVID-19 treated with non-invasive respiratory support: a prospective cohort study

BACKGROUND

Lung ultrasound has demonstrated its usefulness in several respiratory diseases management. One derived score, the Lung Ultrasound (LUS) score, is considered a good outcome predictor in patients with Acute Respiratory Failure (ARF). Nevertheless, it has not been tested in patients undergoing non-invasive respiratory support (NIRS). Taking this into account, the aim of this study is to evaluate LUS score as a predictor of 90-day mortality, ETI (Endotracheal intubation) and HFNC (High Flow Nasal Cannula) failure in patients with ARF due to COVID-19 admitted to a Respiratory Intermediate Care Unit (RICU) for NIRS management.

RESULTS

One hundred one patients were admitted to the RICU during the study period. Among these 76% were males and the median age was 55 (45-64) years. Initial ARF management started with HFNC, the next step was the use of Continuous Positive Airway Pressure (CPAP) devices and the last intervention was ETI and Intensive Care Unit (ICU) admission. Of the total study population, CPAP was required in 40%, ETI in 26%, while 15% died. By means of a ROC analysis, a LUS ≥ 25 points was identified as the cut-off point for mortality(AUC 0.81, OR 1.40, 95% CI 1.14 to 1.71; p < 0.001), ETI (AUC 0.83, OR 1.43, 95% CI 1.20 to 1.70; p < 0.001) and HFNC failure (AUC 0.75, OR 1.25, 95% CI 1.12 to 1.41; p < 0.001). Kaplan-Meier survival curves also identified LUS ≥ 25 as a predictor of 90-days mortality (HR 4.16, 95% CI 1.27-13.6) and 30 days ETI as well.

CONCLUSION

In our study, a ≥ 25 point cut-off of the Lung Ultrasound Score was identified as a good outcome prediction factor for 90-days mortality, ETI and HFNC failure in a COVID-19 ARF patients cohort treated in a RICU. Considering that LUS score is easy to calculate, a multicenter study to confirm our findings should be performed.

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

BACKGROUND

The artificial intelligence (AI) analysis of chest x-rays can increase the precision of binary COVID-19 diagnosis. However, it is unknown if AI-based chest x-rays can predict who will develop severe COVID-19, especially in low- and middle-income countries.

OBJECTIVE

The study aims to compare the performance of human radiologist Brixia scores versus 2 AI scoring systems in predicting the severity of COVID-19 pneumonia.

METHODS

We performed a cross-sectional study of 300 patients suspected with and with confirmed COVID-19 infection in Jakarta, Indonesia. A total of 2 AI scores were generated using CAD4COVID x-ray software.

RESULTS

The AI probability score had slightly lower discrimination (area under the curve [AUC] 0.787, 95% CI 0.722-0.852). The AI score for the affected lung area (AUC 0.857, 95% CI 0.809-0.905) was almost as good as the human Brixia score (AUC 0.863, 95% CI 0.818-0.908).

CONCLUSIONS

The AI score for the affected lung area and the human radiologist Brixia score had similar and good discrimination performance in predicting COVID-19 severity. Our study demonstrated that using AI-based diagnostic tools is possible, even in low-resource settings. However, before it is widely adopted in daily practice, more studies with a larger scale and that are prospective in nature are needed to confirm our findings.

COVID-19 and cancer risk arising from ionizing radiation exposure through CT scans: a cross-sectional study

BACKGROUND

The surge in the utilization of CT scans for COVID-19 diagnosis and monitoring during the pandemic is undeniable. This increase has brought to the forefront concerns about the potential long-term health consequences, especially radiation-induced cancer risk. This study aimed to quantify the potential cancer risk associated with CT scans performed for COVID-19 detection.

METHODS

In this cross-sectional study data from a total of 561 patients, who were referred to the radiology center at Imam Hossein Hospital in Shahroud, was collected. CT scan reports were categorized into three groups based on the radiologist's interpretation. The BEIR VII model was employed to estimate the risk of radiation-induced cancer.

RESULTS

Among the 561 patients, 299 (53.3%) were males and the average age of the patients was 49.61 ± 18.73 years. Of the CT scans, 408 (72.7%) were reported as normal. The average age of patients with normal, abnormal, and potentially abnormal CT scans was 47.57 ± 19.06, 54.80 ± 16.70, and 58.14 ± 16.60 years, respectively (p-value < 0.001). The average effective dose was 1.89 ± 0.21 mSv, with 1.76 ± 0.11 mSv for males and 2.05 ± 0.29 mSv for females (p-value < 0.001). The average risk of lung cancer was 3.84 ± 1.19 and 9.73 ± 3.27 cases per 100,000 patients for males and females, respectively. The average LAR for all cancer types was 10.30 ± 6.03 cases per 100,000 patients.

CONCLUSIONS

This study highlights the critical issue of increased CT scan usage for COVID-19 diagnosis and the potential long-term consequences, especially the risk of cancer incidence. Healthcare policies should be prepared to address this potential rise in cancer incidence and the utilization of CT scans should be restricted to cases where laboratory tests are not readily available or when clinical symptoms are severe.

Machine learning-based computer-aided simple triage (CAST) for COVID-19 pneumonia as compared with triage by board-certified chest radiologists

PURPOSE

Several reporting systems have been proposed for providing standardized language and diagnostic categories aiming for expressing the likelihood that lung abnormalities on CT images represent COVID-19. We developed a machine learning (ML)-based CT texture analysis software for simple triage based on the RSNA Expert Consensus Statement system. The purpose of this study was to conduct a multi-center and multi-reader study to determine the capability of ML-based computer-aided simple triage (CAST) software based on RSNA expert consensus statements for diagnosis of COVID-19 pneumonia.

METHODS

For this multi-center study, 174 cases who had undergone CT and polymerase chain reaction (PCR) tests for COVID-19 were retrospectively included. Their CT data were then assessed by CAST and consensus from three board-certified chest radiologists, after which all cases were classified as either positive or negative. Diagnostic performance was then compared by McNemar's test. To determine radiological finding evaluation capability of CAST, three other board-certified chest radiologists assessed CAST results for radiological findings into five criteria. Finally, accuracies of all radiological evaluations were compared by McNemar's test.

RESULTS

A comparison of diagnosis for COVID-19 pneumonia based on RT-PCR results for cases with COVID-19 pneumonia findings on CT showed no significant difference of diagnostic performance between ML-based CAST software and consensus evaluation (p > 0.05). Comparison of agreement on accuracy for all radiological finding evaluations showed that emphysema evaluation accuracy for investigator A (AC = 91.7%) was significantly lower than that for investigators B (100%, p = 0.0009) and C (100%, p = 0.0009).

CONCLUSION

This multi-center study shows COVID-19 pneumonia triage by CAST can be considered at least as valid as that by chest expert radiologists and may be capable for playing as useful a complementary role for management of suspected COVID-19 pneumonia patients as well as the RT-PCR test in routine clinical practice.

COVID-19 Chest Manifestation on CT Scan and Associated Risk Factors for Developing Pulmonary Fibrosis

PURPOSE

This retrospective study describes the imaging findings on chest computed tomography (CT) scans of coronavirus disease 2019 (COVID-19) patients as well as the prevalence of pulmonary fibrosis and the potential risk factors for the disease.

METHODS

One of the major COVID-19 centers in the western province of Saudi Arabia, the King Abdullah Medical Complex in Jeddah, was the site of this study. All adult COVID-19 patients who got a CT chest scan between January 2020 and April 2022 were included in the trial. The imaging findings and pulmonary severity scores (PSS) were obtained from the patients' CT chest. Patients were divided into two groups according to the evidence of fibrotic-like lung changes; clinical and radiological data between the two groups were subsequently compared. Data from the patients' electronic records was collected.

RESULTS

The average patient age was 56.4 years, and most (73.5%) patients were men. Two-thirds of the patients had comorbidities (69.1%). CT scans revealed that diffuse lung infiltration is reported in 61% of cases, followed by lower lobes in 19.9%. Ground glass opacity (94.1%), consolidation (76.5%), septal thickening, and/or reticulation (24.4%) were the main chest findings during the initial CT scan. Fibrotic-like lung changes were developed in 9.6% of patients. Patients known to have a positive history of hypertension (p-value = 0.031) and coronary artery disease (CAD) (p-value = 0.011) were found to be significantly more likely to develop lung fibrosis. The patients' pneumonia severity score was significantly higher among the lung fibrotic patients (p-value = 0.026). Also, patients who were diagnosed with pulmonary fibrosis stayed longer in the hospital (p-value 0.001). Sex and age did not correlate significantly with risk of lung fibrosis.

CONCLUSION

Pulmonary fibrosis was observed in 9.6% of COVID-19 patients. A close follow-up of patients with severe pneumonia, prolonged hospitalization, and pre-existing CAD and hypertension was necessary, as pulmonary fibrosis was more likely to occur as a result of these factors. There is a need for a thorough, long-term investigation with a large sample size.

Deep learning network selection and optimized information fusion for enhanced COVID‐19 detection

This study proposes a wrapper‐based technique to improve the classification performance of chest infection (including COVID‐19) detection using X‐rays. Deep features were extracted using pretrained deep learning models. Ten optimization techniques, including poor and rich optimization, path finder algorithm, Henry gas solubility optimization, Harris hawks optimization, atom search optimization, manta‐ray foraging optimization, equilibrium optimizer, slime mold algorithm, generalized normal distribution optimization, and marine predator algorithm, were used to determine the optimal features using a support vector machine. Moreover, a network selection technique was used to select the deep learning models. An online chest infection detection X‐ray scan dataset was used to validate the proposed approach. The results suggest that the proposed wrapper‐based automatic deep learning network selection and feature optimization framework has a high classification rate of 97.7%. The comparative analysis further validates the credibility of the framework in COVID‐19 and other chest infection classifications, suggesting that the proposed approach can help doctors in clinical practice.

Artificial intelligence (AI)-assisted chest computer tomography (CT) insights: a study on intensive care unit (ICU) admittance trends in 78 coronavirus disease 2019 (COVID-19) patients

Background

The global coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges for healthcare systems, notably the increased demand for chest computed tomography (CT) scans, which lack automated analysis. Our study addresses this by utilizing artificial intelligence-supported automated computer analysis to investigate lung involvement distribution and extent in COVID-19 patients. Additionally, we explore the association between lung involvement and intensive care unit (ICU) admission, while also comparing computer analysis performance with expert radiologists' assessments.

Methods

A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using CT scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analysed. Additionally, the computer analysis of COVID-19 involvement was compared against a human rating provided by radiological experts.

Results

The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (P<0.05). No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvement compared to the right lower lobes (P<0.05). When examining the regions, significantly more COVID-19 involvement was found when comparing the posterior vs. the anterior halves and the lower vs. the upper half of the lungs. Patients, who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and the rating by radiological experts.

Conclusions

The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.

Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread, and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing of infected patients. Medical imaging, such as X-ray and computed tomography (CT), combined with the potential of artificial intelligence (AI), plays an essential role in supporting medical personnel in the diagnosis process. Thus, in this article, five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their ensemble, using majority voting, have been used to classify COVID-19, pneumoniæ and healthy subjects using chest X-ray images. Multilabel classification was performed to predict multiple pathologies for each patient, if present. Firstly, the interpretability of each of the networks was thoroughly studied using local interpretability methods-occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT-and using a global technique-neuron activation profiles. The mean micro F1 score of the models for COVID-19 classifications ranged from 0.66 to 0.875, and was 0.89 for the ensemble of the network models. The qualitative results showed that the ResNets were the most interpretable models. This research demonstrates the importance of using interpretability methods to compare different models before making a decision regarding the best performing model.

Utility of bronchoalveolar lavage for COVID-19: a perspective from the Dragon consortium

Diagnosing COVID-19 and treating its complications remains a challenge. This review reflects the perspective of some of the Dragon (IMI 2-call 21, #101005122) research consortium collaborators on the utility of bronchoalveolar lavage (BAL) in COVID-19. BAL has been proposed as a potentially useful diagnostic tool to increase COVID-19 diagnosis sensitivity. In both critically ill and non-critically ill COVID-19 patients, BAL has a relevant role in detecting other infections or supporting alternative diagnoses and can change management decisions in up to two-thirds of patients. BAL is used to guide steroid and immunosuppressive treatment and to narrow or discontinue antibiotic treatment, reducing the use of unnecessary broad antibiotics. Moreover, cellular analysis and novel multi-omics techniques on BAL are of critical importance for understanding the microenvironment and interaction between epithelial cells and immunity, revealing novel potential prognostic and therapeutic targets. The BAL technique has been described as safe for both patients and healthcare workers in more than a thousand procedures reported to date in the literature. Based on these preliminary studies, we recognize that BAL is a feasible procedure in COVID-19 known or suspected cases, useful to properly guide patient management, and has great potential for research.

Point-of-care ultrasound use in COVID-19: a narrative review

Background and Objective

The coronavirus disease 2019 (COVID-19) pandemic that began in early 2020 resulted in significant mortality from respiratory tract infections. Existing imaging modalities such as chest X-ray (CXR) lacks sensitivity in its diagnosis while computed tomography (CT) scan carries risks of radiation and contamination. Point-of-care ultrasound (POCUS) has the advantage of bedside testing with higher diagnostic accuracy. We aim to describe the various applications of POCUS for patients with suspected severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in the emergency department (ED) and intensive care unit (ICU).

Methods

We performed literature search on the use of POCUS in the diagnosis and management of COVID-19 in MEDLINE, Embase and Scopus databases using the following search terms: "ultrasonography", "ultrasound", "COVID-19", "SARS-CoV-2", "SARS-CoV-2 variants", "emergency services", "emergency department" and "intensive care units". Search was performed independently by two reviewers with any discrepancy adjudicated by a third member.

Key Content and Findings

Lung POCUS in patients with COVID-19 shows different ultrasonographic features from pulmonary oedema, bacterial pneumonia, and other viral pneumonia, thus useful in differentiating between these conditions. It is more sensitive than CXR, and more accessible and widely available than CT scan. POCUS can be used to diagnose COVID-19 pneumonia, screen for COVID-19-related pulmonary and extrapulmonary complications, and guide management of ICU patients, such as timing of ventilator weaning based on lung POCUS findings.

Conclusions

POCUS is a useful and rapid point-of-care modality that can be used to aid in diagnosis, management, and risk stratification of COVID-19 patients in different healthcare settings.

Robust Medical Diagnosis: A Novel Two-Phase Deep Learning Framework for Adversarial Proof Disease Detection in Radiology Images

In the realm of medical diagnostics, the utilization of deep learning techniques, notably in the context of radiology images, has emerged as a transformative force. The significance of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), lies in their capacity to rapidly and accurately diagnose diseases from radiology images. This capability has been particularly vital during the COVID-19 pandemic, where rapid and precise diagnosis played a pivotal role in managing the spread of the virus. DL models, trained on vast datasets of radiology images, have showcased remarkable proficiency in distinguishing between normal and COVID-19-affected cases, offering a ray of hope amidst the crisis. However, as with any technological advancement, vulnerabilities emerge. Deep learning-based diagnostic models, although proficient, are not immune to adversarial attacks. These attacks, characterized by carefully crafted perturbations to input data, can potentially disrupt the models' decision-making processes. In the medical context, such vulnerabilities could have dire consequences, leading to misdiagnoses and compromised patient care. To address this, we propose a two-phase defense framework that combines advanced adversarial learning and adversarial image filtering techniques. We use a modified adversarial learning algorithm to enhance the model's resilience against adversarial examples during the training phase. During the inference phase, we apply JPEG compression to mitigate perturbations that cause misclassification. We evaluate our approach on three models based on ResNet-50, VGG-16, and Inception-V3. These models perform exceptionally in classifying radiology images (X-ray and CT) of lung regions into normal, pneumonia, and COVID-19 pneumonia categories. We then assess the vulnerability of these models to three targeted adversarial attacks: fast gradient sign method (FGSM), projected gradient descent (PGD), and basic iterative method (BIM). The results show a significant drop in model performance after the attacks. However, our defense framework greatly improves the models' resistance to adversarial attacks, maintaining high accuracy on adversarial examples. Importantly, our framework ensures the reliability of the models in diagnosing COVID-19 from clean images.

Smartphone-based detection of COVID-19 and associated pneumonia using thermal imaging and a transfer learning algorithm

COVID-19-related pneumonia is typically diagnosed using chest x-ray or computed tomography images. However, these techniques can only be used in hospitals. In contrast, thermal cameras are portable, inexpensive devices that can be connected to smartphones. Thus, they can be used to detect and monitor medical conditions outside hospitals. Herein, a smartphone-based application using thermal images of a human back was developed for COVID-19 detection. Image analysis using a deep learning algorithm revealed a sensitivity and specificity of 88.7% and 92.3%, respectively. The findings support the future use of noninvasive thermal imaging in primary screening for COVID-19 and associated pneumonia.

Performance of chest X-ray scoring in predicting disease severity and outcomes of patients hospitalised with COVID-19 in Bangladesh

Introduction

Evaluation of potential outcomes of COVID-19-affected pneumonia patients using computed tomography scans may not be conceivable in low-resource settings. Thus, we aimed to evaluate the performance of chest X-ray scoring in predicting the disease severity and outcomes of adults hospitalised with COVID-19.

Methods

This was a retrospective chart analysis consuming data from COVID-19-positive adults who had chest X-ray availability and were admitted to a temporary COVID unit, in Bangladesh from 23rd April 2020 to 15th November 2021. At least one clinical intensivist and one radiologist combinedly reviewed each admission chest X-ray for the different lung findings. Chest X-ray scoring varied from 0 to 8, depending on the area of lung involvement with 0 indicating no involvement and 8 indicating ⩾75% involvement of both lungs. The receiver operating characteristic curve was used to determine the optimum chest X-ray cut-off score for predicting the fatal outcomes.

Result

A total of 218 (82.9%) out of 263 COVID-19-affected adults were included in the study. The receiver operating characteristic curve demonstrated the optimum cut-off as ⩾3 and ⩾5 for disease severity and death, respectively. In multivariate logistic regression analysis, a chest X-ray score of ⩾3 was found to be independently associated with disease severity (aOR: 8.70; 95% CI: 3.82, 19.58, p < 0.001) and a score of ⩾5 with death (aOR: 16.53; 95% CI: 4.74, 57.60, p < 0.001) after adjusting age, sex, antibiotic usage before admission, history of fever, cough, diabetes mellitus, hypertension, total leukocytes count and C-reactive protein.

Conclusion

Using chest X-ray scoring derived cut-off at admission might help to identify the COVID-19-affected adults who are at risk of severe disease and mortality. This may help to initiate early and aggressive management of such patients, thereby reducing their fatal outcomes.

HRCTCov19-a high-resolution chest CT scan image dataset for COVID-19 diagnosis and differentiation

INTRODUCTION

Computed tomography (CT) was a widely used diagnostic technique for COVID-19 during the pandemic. High-Resolution Computed Tomography (HRCT), is a type of computed tomography that enhances image resolution through the utilization of advanced methods. Due to privacy concerns, publicly available COVID-19 CT image datasets are incredibly tough to come by, leading to it being challenging to research and create AI-powered COVID-19 diagnostic algorithms based on CT images.

DATA DESCRIPTION

To address this issue, we created HRCTCov19, a new COVID-19 high-resolution chest CT scan image collection that includes not only COVID-19 cases of Ground Glass Opacity (GGO), Crazy Paving, and Air Space Consolidation but also CT images of cases with negative COVID-19. The HRCTCov19 dataset, which includes slice-level and patient-level labeling, has the potential to assist in COVID-19 research, in particular for diagnosis and a distinction using AI algorithms, machine learning, and deep learning methods. This dataset, which can be accessed through the web at http://databiox.com , includes 181,106 chest HRCT images from 395 patients labeled as GGO, Crazy Paving, Air Space Consolidation, and Negative.

"KAIZEN" method realizing implementation of deep-learning models for COVID-19 CT diagnosis in real world hospitals

Numerous COVID-19 diagnostic imaging Artificial Intelligence (AI) studies exist. However, none of their models were of potential clinical use, primarily owing to methodological defects and the lack of implementation considerations for inference. In this study, all development processes of the deep-learning models are performed based on strict criteria of the "KAIZEN checklist", which is proposed based on previous AI development guidelines to overcome the deficiencies mentioned above. We develop and evaluate two binary-classification deep-learning models to triage COVID-19: a slice model examining a Computed Tomography (CT) slice to find COVID-19 lesions; a series model examining a series of CT images to find an infected patient. We collected 2,400,200 CT slices from twelve emergency centers in Japan. Area Under Curve (AUC) and accuracy were calculated for classification performance. The inference time of the system that includes these two models were measured. For validation data, the slice and series models recognized COVID-19 with AUCs and accuracies of 0.989 and 0.982, 95.9% and 93.0% respectively. For test data, the models' AUCs and accuracies were 0.958 and 0.953, 90.0% and 91.4% respectively. The average inference time per case was 2.83 s. Our deep-learning system realizes accuracy and inference speed high enough for practical use. The systems have already been implemented in four hospitals and eight are under progression. We released an application software and implementation code for free in a highly usable state to allow its use in Japan and globally.

Detection of COVID-19 using edge devices by a light-weight convolutional neural network from chest X-ray images

Deep learning is a highly significant technology in clinical treatment and diagnostics nowadays. Convolutional Neural Network (CNN) is a new idea in deep learning that is being used in the area of computer vision. The COVID-19 detection is the subject of our medical study. Researchers attempted to increase the detection accuracy but at the cost of high model complexity. In this paper, we desire to achieve better accuracy with little training space and time so that this model easily deployed in edge devices. In this paper, a new CNN design is proposed that has three stages: pre-processing, which removes the black padding on the side initially; convolution, which employs filter banks; and feature extraction, which makes use of deep convolutional layers with skip connections. In order to train the model, chest X-ray images are partitioned into three sets: learning(0.7), validation(0.1), and testing(0.2). The models are then evaluated using the test and training data. The LMNet, CoroNet, CVDNet, and Deep GRU-CNN models are the other four models used in the same experiment. The propose model achieved 99.47% & 98.91% accuracy on training and testing respectively. Additionally, it achieved 97.54%, 98.19%, 99.49%, and 97.86% scores for precision, recall, specificity, and f1-score respectively. The proposed model obtained nearly equivalent accuracy and other similar metrics when compared with other models but greatly reduced the model complexity. Moreover, it is found that proposed model is less prone to over fitting as compared to other models.

Potential diagnostic application of a novel deep learning- based approach for COVID-19

COVID-19 is a highly communicable respiratory illness caused by the novel coronavirus SARS-CoV-2, which has had a significant impact on global public health and the economy. Detecting COVID-19 patients during a pandemic with limited medical facilities can be challenging, resulting in errors and further complications. Therefore, this study aims to develop deep learning models to facilitate automated diagnosis of COVID-19 from CT scan records of patients. The study also introduced COVID-MAH-CT, a new dataset that contains 4442 CT scan images from 133 COVID-19 patients, as well as 133 CT scan 3D volumes. We proposed and evaluated six different transfer learning models for slide-level analysis that are responsible for detecting COVID-19 in multi-slice spiral CT. Additionally, multi-head attention squeeze and excitation residual (MASERes) neural network, a novel 3D deep model was developed for patient-level analysis, which analyzes all the CT slides of a given patient as a whole and can accurately diagnose COVID-19. The codes and dataset developed in this study are available at https://github.com/alrzsdgh/COVID . The proposed transfer learning models for slide-level analysis were able to detect COVID-19 CT slides with an accuracy of more than 99%, while MASERes was able to detect COVID-19 patients from 3D CT volumes with an accuracy of 100%. These achievements demonstrate that the proposed models in this study can be useful for automatically detecting COVID-19 in both slide-level and patient-level from patients' CT scan records, and can be applied for real-world utilization, particularly in diagnosing COVID-19 cases in areas with limited medical facilities.

Image quality assessment in low-dose COVID-19 chest CT examinations

BACKGROUND

Low-dose thoracic protocols were developed massively during the COVID-19 outbreak.

PURPOSE

To study the impact on image quality (IQ) and the diagnosis reliability of COVID-19 low-dose chest computed tomography (CT) protocols.

MATERIAL AND METHODS

COVID-19 low-dose protocols were implemented on third- and second-generation CT scanners considering two body mass index (BMI) subgroups (<25 kg/m2 and >25 kg/m2). Contrast-to-noise ratios (CNR) were compared with a Catphan phantom. Next, two radiologists retrospectively assessed IQ for 243 CT patients using a 5-point Linkert scale for general IQ and diagnostic criteria. Kappa score and Wilcoxon rank sum tests were used to compare IQ score and CTDIvol between radiologists, protocols, and scanner models.

RESULTS

In vitro analysis of Catphan inserts showed in majority significantly decreased CNR for the low dose versus standard acquisition protocols on both CT scanners. However, in vivo, there was no impact on the diagnosis: sensitivity and specificity were ≥0.8 for all protocols and CT scanners. The third-generation scanner involved a significantly lower dose compared to the second-generation scanner (CTDIvol of 1.8 vs. 2.6 mGy for BMI <25 kg/m2 and 3.3 vs. 4.6 mGy for BMI >25 kg/m2). Still, the third-generation scanner showed a significantly higher IQ with the low-dose protocol compared to the second-generation scanner (30.9 vs. 28.1 for BMI <25 kg/m2 and 29.9 vs. 27.8 for BMI >25 kg/m2). Finally, the two radiologists had good global inter-reader agreement (kappa ≥0.6) for general IQ.

CONCLUSION

Low-dose protocols provided sufficient IQ independently of BMI subgroups and CT models without any impact on diagnosis reliability.

Self-supervised learning with self-distillation on COVID-19 medical image classification

BACKGROUND AND OBJECTIVE

Currently, COVID-19 is a highly infectious disease that can be clinically diagnosed based on diagnostic radiology. Deep learning is capable of mining the rich information implied in inpatient imaging data and accomplishing the classification of different stages of the disease process. However, a large amount of training data is essential to train an excellent deep-learning model. Unfortunately, due to factors such as privacy and labeling difficulties, annotated data for COVID-19 is extremely scarce, which encourages us to propose a more effective deep learning model that can effectively assist specialist physicians in COVID-19 diagnosis.

METHODS

In this study,we introduce Masked Autoencoder (MAE) for pre-training and fine-tuning directly on small-scale target datasets. Based on this, we propose Self-Supervised Learning with Self-Distillation on COVID-19 medical image classification (SSSD-COVID). In addition to the reconstruction loss computation on the masked image patches, SSSD-COVID further performs self-distillation loss calculations on the latent representation of the encoder and decoder outputs. The additional loss calculation can transfer the knowledge from the global attention of the decoder to the encoder which acquires only local attention.

RESULTS

Our model achieves 97.78 % recognition accuracy on the SARS-COV-CT dataset containing 2481 images and is further validated on the COVID-CT dataset containing 746 images, which achieves 81.76 % recognition accuracy. Further introduction of external knowledge resulted in experimental accuracies of 99.6% and 95.27 % on these two datasets, respectively.

CONCLUSIONS

SSSD-COVID can obtain good results on the target dataset alone, and when external information is introduced, the performance of the model can be further improved to significantly outperform other models.Overall, the experimental results show that our method can effectively mine COVID-19 features from rare data and can assist professional physicians in decision-making to improve the efficiency of COVID-19 disease detection.

Hydrogen Sulfide: Physiological Roles and Therapeutic Implications against COVID-19

The COVID-19 pandemic due to severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) poses a major menace to economic and public health worldwide. Angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2) are two host proteins that play an essential function in the entry of SARS-- COV-2 into host cells. Hydrogen sulfide (H2S), a new gasotransmitter, has been shown to protect the lungs from potential damage through its anti-inflammatory, antioxidant, antiviral, and anti-aging effects. It is well known that H2S is crucial in controlling the inflammatory reaction and the pro-inflammatory cytokine storm. Therefore, it has been suggested that some H2S donors may help treat acute lung inflammation. Furthermore, recent research illuminates a number of mechanisms of action that may explain the antiviral properties of H2S. Some early clinical findings indicate a negative correlation between endogenous H2S concentrations and COVID-19 intensity. Therefore, reusing H2S-releasing drugs could represent a curative option for COVID-19 therapy.

CT features of acute COVID-19 and long-term follow-up

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

A Biobanking System for Diagnostic Images: Architecture Development, COVID-19-Related Use Cases, and Performance Evaluation

BACKGROUND

Systems capable of automating and enhancing the management of research and clinical data represent a significant contribution of information and communication technologies to health care. A recent advancement is the development of imaging biobanks, which are now enabling the collection and storage of diagnostic images, clinical reports, and demographic data to allow researchers identify associations between lifestyle and genetic factors and imaging-derived phenotypes.

OBJECTIVE

The aim of this study was to design and evaluate the system performance of a network for an operating biobank of diagnostic images, the Bio Check Up Srl (BCU) Imaging Biobank, based on the Extensible Neuroimaging Archive Toolkit open-source platform.

METHODS

Three usage cases were designed focusing on evaluation of the memory and computing consumption during imaging collections upload and during interactions between two kinds of users (researchers and radiologists) who inspect chest computed tomography scans of a COVID-19 cohort. The experiments considered three network setups: (1) a local area network, (2) virtual private network, and (3) wide area network. The experimental setup recorded the activity of a human user interacting with the biobank system, which was continuously replayed multiple times. Several metrics were extracted from network traffic traces and server logs captured during the activity replay.

RESULTS

Regarding the diagnostic data transfer, two types of containers were considered: the Web and the Database containers. The Web appeared to be the more memory-hungry container with a higher computational load (average 2.7 GB of RAM) compared to that of the database. With respect to user access, both users demonstrated the same network performance level, although higher resource consumption was registered for two different actions: DOWNLOAD & LOGOUT (100%) for the researcher and OPEN VIEWER (20%-50%) for the radiologist.

CONCLUSIONS

This analysis shows that the current setup of BCU Imaging Biobank is well provisioned for satisfying the planned number of concurrent users. More importantly, this study further highlights and quantifies the resource demands of specific user actions, providing a guideline for planning, setting up, and using an image biobanking system.

Effect of vaccination status on CORADS and computed tomography severity score in hospitalized COVID-19 patients: A retrospective study

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic is continuing. The disease most commonly affects the lungs. Since the beginning of the pandemic thorax computed tomography (CT) has been an indispensable imaging method for diagnosis and follow-up. The disease is tried to be controlled with vaccines. Vaccination reduces the possibility of a severe course of the disease.

AIM

The aim of this study is to investigate whether the vaccination status of patients hospitalized due to COVID-19 has an effect on the CT severity score (CT-SS) and CORADS score obtained during hospitalization.

METHODS

The files of patients hospitalized between April 1, 2021 and April 1, 2022 due to COVID-19 were retrospectively reviewed. A total of 224 patients who were older than 18 years of age, whose vaccination status was accessible, whose severe acute respiratory syndrome coronavirus 2 polymerase chain reaction result was positive, and who had a Thorax CT scan during hospitalization were included in the study.

RESULTS

Among the patients included in the study, 52.2% were female and the mean age was 61.85 years. The patients applied to the hospital on the average 7th day of their complaints. While 63 patients were unvaccinated (Group 1), 20 were vaccinated with a single dose of CoronaVac (Group 2), 24 with a single dose of BioNTech (Group 3), 38 with 2 doses of CoronaVac (Group 4), 40 with 2 doses of BioNTech (Group 5), and 39 with 3 doses of vaccine (2 doses of CoronaVac followed by a single dose of BioNTech, Group 6). CT-SS ranged from 5 to 23, with a mean of 12.17.CT-SS mean of the groups were determined as 14.17, 13.35, 11.58, 10.87, 11.28, 10.85, respectively. Accordingly, as a result of the comparisons between the groups, the CT-SS levels of the unvaccinated patients found to be significantly higher than the other groups. As the vaccination rates increased, the rate of typical COVID-19 findings on CT was found to be significantly lower.

CONCLUSION

Increased vaccination rates in COVID-19 patients reduce the probability of typical COVID-19 symptoms in the lungs. It also reduces the risk of severe disease and decreases CT Severity Scores. This may lead to a loss of importance of Thorax CT in the diagnosis of COVID-19 pneumonia as the end of the pandemic approaches.

Rapid and sensitive detection of SARS-CoV-2 virus in human saliva samples using glycan based nanozyme: a clinical study

A highly sensitive colorimetric method (glycan-based nano(e)zyme) was developed for sensitive and rapid detection of the SARS-CoV-2 virus based on N-acetyl neuraminic acid (sialic acid)-functionalized gold nanoparticles (SA-Au NZs). A number of techniques were used to characterize the prepared nanomaterials including XRD, FT-IR, UV-vis, DLS, and TEM. DLS analysis indicates an average hydrodynamic size of 34 nm, whereas TEM analysis indicates an average particle size of 15.78 nm. This observation confirms that water interacts with nanoparticle surfaces, resulting in a large hydrodynamic diameter. The peroxidase-like activity of SA-Au NZs was examined with SARS-CoV-2 and influenza viruses (influenza A (H1N1), influenza A (H3N2), and influenza B). UV-visible spectroscopy was used to monitor and record the results, as well as naked eye detection (photographs). SA-Au NZs exhibit a change in color from light red to purple when SARS-CoV-2 is present, and they exhibit a redshift in their spectrum. N-acetyl neuraminic acid interacts with SARS-CoV-2 spike glycoprotein, confirming its ability to bind glycans. As a result, SA-Au NZs can detect COVID-19 with sensitivity and specificity of over 95% and 98%, respectively. This method was approved by testing saliva samples from 533 suspected individuals at Ghaem Hospital of Mashhad, Mashhad, Iran. Sensitivity and specificity were calculated by comparing the results with the definitive results. The positive results were accompanied by a color change from bright red to purple within five minutes. Statistical analysis was performed based on variables such as age, gender, smoking, diabetes, hypertension, and lung involvement. In clinical trials, it was demonstrated that this method can be used to diagnose SARS-CoV-2 in a variety of places, such as medical centers, hospitals, airports, universities, and schools.