Search

During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, variants of SARS-COV-2 are endemic and COVID-19 cases are common in many places. The symptoms of COVID-19 are highly diverse and robust, ranging from invisible to severe respiratory failure. Current detection methods for the disease are time-consuming and expensive with low accuracy and precision. To address such situations, we have designed a framework for COVID-19 and Pneumonia detection using multiple deep learning algorithms further accompanied by a deployment scheme. In this study, we have utilized four prominent deep learning models, which are VGG-19, ResNet-50, Inception V3 and Xception, on two separate datasets of CT scan and X-ray images (COVID/Non-COVID) to identify the best models for the detection of COVID-19. We achieved accuracies ranging from 86% to 99% depending on the model and dataset. To further validate our findings, we have applied the four distinct models on two more supplementary datasets of X-ray images of bacterial pneumonia and viral pneumonia. Additionally, we have implemented a flask app to visualize the outcome of our framework to show the identified COVID and Non-COVID images. The findings of this study will be helpful to develop an AI-driven automated tool for the cost effective and faster detection and better management of COVID-19 patients., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Islam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Recent advances in Vision Transformer (ViT)-based deep learning have significantly improved the accuracy of lung disease prediction from chest X-ray images. However, limited research exists on comparing the effectiveness of different optimizers for lung disease prediction within ViT models. This study aims to systematically evaluate and compare the performance of various optimization methods for ViT-based models in predicting lung diseases from chest X-ray images., Methods: This study utilized a chest X-ray image dataset comprising 19,003 images containing both normal cases and six lung diseases: COVID-19, Viral Pneumonia, Bacterial Pneumonia, Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), and Tuberculosis. Each ViT model (ViT, FastViT, and CrossViT) was individually trained with each optimization method (Adam, AdamW, NAdam, RAdam, SGDW, and Momentum) to assess their performance in lung disease prediction., Results: When tested with ViT on the dataset with balanced-sample sized classes, RAdam demonstrated superior accuracy compared to other optimizers, achieving 95.87%. In the dataset with imbalanced sample size, FastViT with NAdam achieved the best performance with an accuracy of 97.63%., Conclusions: We provide comprehensive optimization strategies for developing ViT-based model architectures, which can enhance the performance of these models for lung disease prediction from chest X-ray images., (© 2024. The Author(s).)

Objectives: Systemic steroid administration has been suggested for the treatment of coronavirus disease 2019 (COVID-19), but the occurrence of osteonecrosis of the femoral head (ONFH) was one of the concerns for this treatment. This study aimed to use magnetic resonance imaging (MRI) to assess the incidence of ONFH after the treatment for COVID-19., Methods: The study included 41 patients who were hospitalized and treated for pneumonia or other COVID-19-induced diseases. We conducted interviews with these patients regarding hip pain and performed MRI screenings for ONFH. The incidence and timing of ONFH after COVID-19 treatment were investigated., Results: Of the 41 patients, one died of pneumonia and the remaining patients did not complain of hip pain. MRI screening was performed for 26 patients, and asymptomatic ONFH was detected in one patient (3.8%) whose ONFH appeared 1 month after the COVID-19 infection., Conclusions: Our MRI screening of ONFH in post-COVID-19 patients revealed asymptomatic ONFH, which would not have been identified without active screening. Physicians should be aware that ONFH may occur in patients after treating COVID-19., (© Japan College of Rheumatology 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objectives: To identify clinical characteristics and risk factors for pulmonary involvements in asymptomatic and mildly symptomatic patients infected with SARS-CoV-2 Omicron variant by chest imaging analysis., Methods: Detailed data and chest computed tomography (CT) imaging features were retrospectively analyzed from asymptomatic and mildly symptomatic patients infected with Omicron between 24 April and 10 May 2022. We scored chest CT imaging features and categorized the patients into obvious pulmonary involvements (OPI) (score > 2) and not obvious pulmonary involvements (NOPI) (score ≤ 2) groups based on the median score. The risk factors for OPI were identified with analysis results visualized by nomogram., Results: In total, 339 patients were included (145 were male and 194 were female), and the most frequent clinical symptoms were cough (75.5%); chest CT imaging features were mostly linear opacities (42.8%). Pulmonary involvements were more likely to be found in the left lower lung lobe, with a significant difference in the lung total severity score of the individual lung lobes ( p < 0.001). Logistic regression analysis revealed age stratification [odds ratio (OR) = 1.92, 95% confidence interval (CI) (1.548-2.383); p  < 0.001], prolonged nucleic acid negative conversion time (NCT) (NCT > 8d) [OR = 1.842, 95% CI (1.104-3.073); p  = 0.019], and pulmonary diseases [OR = 4.698, 95% CI (1.159-19.048); p  = 0.03] as independent OPI risk factors., Conclusion: Asymptomatic and mildly symptomatic patients infected with Omicron had pulmonary involvements which were not uncommon. Potential risk factors for age stratification, prolonged NCT, and pulmonary diseases can help clinicians to identify OPI in asymptomatic and mildly symptomatic patients infected with Omicron., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Liu, Cao, Dai, Chen, Zhao, Xu, Xu, Cao, Zhan and Zuo.)

Objectives: Due to the government's liberalization of epidemic control, the current 2019 novel coronavirus disease (COVID-19) has started to spread widely within China. This study aimed to use the fetal heart quantification (fetal HQ) technique to assess the cardiac function and morphology of the fetuses of pregnant women diagnosed with COVID-19 in the early stages of pregnancy., Methods: Exactly 86 pregnant women diagnosed with COVID-19 infection in early pregnancy (COVID-19 group) and 90 healthy pregnant women (control group) who underwent fetal echocardiography were prospectively included in this study. The fetal HQ technique was applied to compare the differences in the global sphericity index (GSI), global strain values (GS), fractional area change (FAC), and 24-segment fractional shortening (FS) of the left and right ventricles, between the COVID-19 group and the control group., Results: Compared with the control group, the differences in GS and 24-segment FS of the left ventricle in the COVID-19 group were not statistically significant. However, the COVID-19 group showed lower GSI values compared with the control group (1.24 vs 1.28). FAC (48.12%) of the left ventricle and GS (-23.55%), FAC (41.74%) of the right ventricle in the COVID-19 group were reduced compared with FAC (50.50%) of the left ventricle and GS (-27.63%), FAC (46.01%) of the right ventricle in the control group. Segmental analysis revealed reduced FS in segments 20-24 in the COVID-19 group compared with the control group. Right ventricular GS was an independent predictor of adverse pregnancy outcome with an optimal cutoff value of -18.66%., Conclusions: The results suggest that COVID-19 infection in early pregnancy may have a negative impact on fetal cardiac morphology and function. Fetal HQ may offer a new assessment method for the early identification of fetal cardiac alterations in pregnant women infected with COVID-19., (© 2024 The Authors. Journal of Ultrasound in Medicine published by Wiley Periodicals LLC on behalf of American Institute of Ultrasound in Medicine.)

Background and Purpose: This prospective observational study aimed to investigate the occurrence of avascular necrosis (AVN) of the femoral head in COVID-19 patients through MRI scans. The study examined the patterns of AVN in 110 individuals who had undergone conventional COVID-19 therapy and reported hip discomfort. This study highlights the importance of considering AVN as a potential complication of COVID-19 therapy, particularly in younger patients who experience hip discomfort., Methods: Individuals who had corticosteroid treatment for COVID-19 and experienced hip discomfort during 6 months between January 2022 and August 2022 were included in this study, and an MRI scan was done to observe changes in the hip joint., Results: The results were classified using the Ficat and Arlet classification system. The analysis revealed that AVN was not present in 91.81% of cases. However, Stage I AVN was detected in 4.54% of cases, Stage II AVN in 2.72% of cases, and Stage III AVN in 1.1% of cases. No cases of Stage IV AVN were observed., Conclusions: The study concludes that AVN occurred in 6% of individuals who underwent conventional therapy for COVID-19 and experienced hip discomfort. In these settings (post COVID-19), normal MRI results were more typical, and mild AVN (Stage I) was a frequent finding in MRI scans that were positive., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objective: To evaluate longitudinal changes in peripapillary vessel density (VD) following coronavirus disease 2019 (COVID-19) using optical coherence tomography angiography., Methods: As part of a prospective longitudinal observational study, we studied healthy individuals with a history of mild COVID-19 confirmed via real-time polymerase chain reaction. After recovery, we used the Optovue RTVue XR Avanti machine to perform optic nerve head (ONH) imaging. We also assessed the VD of all vessels and of small vessels in the disc and the radial peripapillary capillary (RPC) network at 1 and 3 months post-recovery., Results: We included 17 patients (34 eyes; mean age: 36.9 ± 10.2 years, range: 24-62 years) who had recovered from COVID-19. No changes were observed in the ONH parameters. However, there was a noticeable trend of increased small vessel VD values in the RPC. These increases were significant for the peripapillary whole, superior hemifield, inferior-temporal, temporal-superior, and superior-temporal small vessels. Moreover, the evaluation of all vessel VD values in the RPC revealed a significant decrease in the inside disc and a significant increase in a grid-based inferior region., Conclusion: COVID-19 may affect VD of the RPC in the ONH, and should be considered in ONH evaluations., Competing Interests: Declaration of conflicting interestThe authors declare that there is no conflict of interest.

The COVID-19 pandemic caused by SARS-CoV-2 has triggered a consequential public health crisis of post-acute sequelae of COVID-19 (PASC), sometimes referred to as long COVID. The mechanisms of the heterogeneous persistent symptoms and signs that comprise PASC are under investigation, and several studies have pointed to the central nervous and vascular systems as being potential sites of dysfunction. In the current study, we recruited individuals with PASC with diverse symptoms, and examined the relationship between neuroinflammation and circulating markers of vascular dysfunction. We used [ 11 C]PBR28 PET neuroimaging, a marker of neuroinflammation, to compare 12 PASC individuals versus 43 normative healthy controls. We found significantly increased neuroinflammation in PASC versus controls across a wide swath of brain regions including midcingulate and anterior cingulate cortex, corpus callosum, thalamus, basal ganglia, and at the boundaries of ventricles. We also collected and analyzed peripheral blood plasma from the PASC individuals and found significant positive correlations between neuroinflammation and several circulating analytes related to vascular dysfunction. These results suggest that an interaction between neuroinflammation and vascular health may contribute to common symptoms of PASC., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Currently, Coronavirus Disease 2019 (COVID-19) is still endangering world health and safety and deep learning (DL) is expected to be the most powerful method for efficient detection of COVID-19. However, patients' privacy concerns prohibit data sharing between medical institutions, leading to unexpected performance of deep neural network (DNN) models. Fortunately, federated learning (FL), as a novel paradigm, allows participating clients to collaboratively train models without exposing source data outside original location. Nevertheless, the current FL-based COVID-19 detection methods prefer optimizing secondary objectives including delay, energy consumption and privacy, while few works focus on improving the model accuracy and stability. In this paper, we propose a federated learning framework with dynamic focus for COVID-19 detection on CXR images, named FedFocus. Specifically, to improve the training efficiency and accuracy, the training loss of each model is taken as the basis for parameter aggregation weights. As training layer deepens, a constantly updated dynamic factor is designed to stabilize the aggregation process. In addition, to highly restore the real dataset, the training sets in our experiments are divided based on the population and the infection of three real cities. Extensive experiments conducted on the real-world CXR images dataset demonstrate that FedFocus outperforms the baselines in model training efficiency, accuracy and stability.

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.

Objective: We aimed to compare the radiological and clinical characteristics of COVID-19-positive and -negative acute ischemic stroke (AIS) patients who underwent revascularization and to evaluate whether COVID-19 has an effect on revascularization and outcome in AIS patients with COVID-19 infection., Methods: Consecutive COVID-19 positive and negative AIS patients who underwent intravenous thrombolysis and/or endovascular therapy in our hospital between March 2020 and February 2022 were included in this study. Our study is retrospective and 23 COVID-19 positive patients and 108 COVID-19 negative patients were compared in terms of radiological and clinical characteristics., Results: Hypertension was lower in the COVID-19 positive ischemic stroke group (p=0.029). Admission NIHSS score was higher in COVID-19 positive patients (p=0.041). ASPECTS was found to be lower in COVID-19 positive ischemic stroke patients (p=0.019). The rate of hyperdense artery sign differed between groups (p=0.014) and was higher in the COVID-19 positive ischemic stroke group. The incidence of multi-vessel occlusion was found to be significantly higher in the COVID-19-positive ischemic stroke group (p=0.002). In terms of prognostic features, only the 3-month good outcome rate was statistically significantly lower in the COVID-19-positive ischemic stroke group (p=0.011)., Conclusion: This study found that radiologically, COVID-19 may be associated with lower ASPECTS in ischemic stroke patients receiving revascularization treatment and may predispose to multivessel occlusion and hyperdense artery sign. Clinically, COVID-19 may be associated with a more severe initial presentation and worse prognosis at 3 months in ischemic stroke patients undergoing revascularization, but may not affect long-term mortality., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The low vertebral bone computed tomography (CT) Hounsfield unit values measured on CT scans reflect low bone mineral density (BMD) and are known as diagnostic indicators for osteoporosis. The potential prognostic significance of low BMD defined by vertebral bone CT values for the coronavirus disease 2019 (COVID-19) remains unclear. This study aimed to assess the impact of BMD on the clinical outcome in Japanese patients with COVID-19 and evaluate the association between BMD and critical outcomes, such as high-flow nasal cannula, non-invasive and invasive positive pressure ventilation, extracorporeal membrane oxygenation, or death. We examined the effects of COVID-19 severity on the change of BMD over time. This multicenter retrospective cohort study enrolled 1132 inpatients with COVID-19 from the Japan COVID-19 Task Force database between February 2020 and September 2022. The bone CT values of the 4th, 7th, and 10th thoracic vertebrae were measured from chest CT images. The average of these values was defined as BMD. Furthermore, a comparative analysis was conducted between the BMD on admission and its value 3 months later. The low BMD group had a higher proportion of critical outcomes than did the high BMD group. In a subanalysis stratifying patients by epidemic wave according to onset time, critical outcomes were higher in the low BMD group in the 1st-4th waves. Multivariable logistic analysis of previously reported factors associated with COVID-19 severity revealed that low BMD, chronic kidney disease, and diabetes were independently associated with critical outcomes. At 3 months post-infection, patients with oxygen demand during hospitalization showed markedly decreased BMD than did those on admission. Low BMD in patients with COVID-19 may help predict severe disease after the disease onset. BMD may decrease over time in patients with severe COVID-19, and the impact on sequelae symptoms should be investigated in the future., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: No comprehensive analysis of the pulmonary sequelae of coronavirus disease 2019 (COVID-19) in Japan based on respiratory function tests and chest computed tomography (CT) has been reported. We evaluated post-COVID-19 conditions, especially focusing on pulmonary sequelae assessed by pulmonary function tests and chest CT., Methods: For this prospective cohort study, we enrolled 1069 patients who presented pneumonia at the time of admission in 55 hospitals from February 2020 to September 2021. Disease severity was classified as moderateⅠ, moderate II, and severe, defined primarily according to the degree of respiratory failure. The data on post-COVID-19 conditions over 12 months, pulmonary function, and chest CT findings at 3 months were evaluated in this study. Additionally, the impact of COVID-19 severity on pulmonary sequelae, such as impaired diffusion capacity, restrictive pattern, and CT abnormalities, was also evaluated., Results: The most frequently reported post-COVID-19 conditions at 3 months after COVID-19 were muscle weakness, dyspnea, and fatigue (48.4%, 29.0%, and 24.7%, respectively). The frequency of symptoms gradually decreased over subsequent months. In pulmonary function tests at 3 months, the incidence of impaired diffusion capacity and restrictive pattern increased depending on disease severity. There also were differences in the presence of chest CT abnormalities at the 3 months, which was markedly correlated with the severity., Conclusion: We reported a comprehensive analysis of post-COVID-19 condition, pulmonary function, and chest CT abnormalities in Japanese patients with COVID-19. The findings of this study will serve as valuable reference data for future post-COVID-19 condition research in Japan., Competing Interests: Declaration of Competing interest The authors have no conflicts of interest., (Copyright © 2024. Published by Elsevier B.V.)

Objectives: Cytotoxic lesions of the corpus callosum (CLOCC) are a common magnetic resonance imaging (MRI) finding associated with various systemic diseases including COVID-19. Although an increasing number of such cases is reported in the literature, there is a lack of systematic evidence summarizing the etiology and neuroimaging findings of these lesions. Thus, the aim of this systematic review was to synthesize the applied nomenclature, neuroimaging and clinical features, and differential diagnoses as well as associated disease entities of CLOCC., Materials and Methods: A comprehensive literature search in three biomedical databases identified 441 references, out of which 324 were eligible for a narrative summary including a total of 1353 patients., Results: Our PRISMA-conform systematic review identifies a broad panel of disease entities which are associated with CLOCC, among them toxic/drug-treatment-associated, infectious (viral, bacterial), vascular, metabolic, traumatic, and neoplastic entities in both adult and pediatric individuals. On MRI, CLOCC show typical high T2 signal, low T1 signal, restricted diffusion, and lack of contrast enhancement. The majority of the lesions were reversible within the follow-up period (median follow-up 3 weeks). Interestingly, even though CLOCC were mostly associated with symptoms of the underlying disease, in exceptional cases, CLOCC were associated with callosal neurological symptoms. Of note, employed nomenclature for CLOCC was highly inconsistent., Conclusions: Our study provides high-level evidence for clinical and imaging features of CLOCC as well as associated disease entities., Clinical Relevance Statement: Our study provides high-level evidence on MRI features of CLOCC as well as a comprehensive list of disease entities potentially associated with CLOCC. Together, this will facilitate rigorous diagnostic workup of suspected CLOCC cases., Key Points: • Cytotoxic lesions of the corpus callosum (CLOCC) are a frequent MRI feature associated with various systemic diseases. • Cytotoxic lesions of the corpus callosum show a highly homogenous MRI presentation and temporal dynamics. • This comprehensive overview will benefit (neuro)radiologists during diagnostic workup., (© 2023. The Author(s).)

Purpose: To develop an iterative concomitant field and motion corrected (iCoMoCo) reconstruction for isotropic high-resolution UTE pulmonary imaging at 0.55 T., Methods: A free-breathing golden-angle stack-of-spirals UTE sequence was used to acquire data for 8 min with prototype and commercial 0.55 T MRI scanners. The data was binned into 12 respiratory phases based on superior-inferior navigator readouts. The previously published iterative motion corrected (iMoCo) reconstruction was extended to include concomitant field correction directly in the cost function. The reconstruction was implemented within the Gadgetron framework for inline reconstruction. Data were retrospectively reconstructed to simulate scan times of 2, 4, 6, and 8 min. Image quality was assessed using apparent SNR and image sharpness. The technique was evaluated in healthy volunteers and patients with known lung pathology including coronavirus disease 2019 infection, chronic granulomatous disease, lymphangioleiomyomatosis, and lung nodules., Results: The technique provided diagnostic-quality images, and image quality was maintained with a slight loss in SNR for simulated scan times down to 4 min. Parenchymal apparent SNR was 4.33 ± 0.57, 5.96 ± 0.65, 7.36 ± 0.64, and 7.87 ± 0.65 using iCoMoCo with scan times of 2, 4, 6, and 8 min, respectively. Image sharpness at the diaphragm was comparable between iCoMoCo and reference images. Concomitant field corrections visibly improved the sharpness of anatomical structures away from the isocenter. Inline image reconstruction and artifact correction were achieved in <5 min., Conclusion: The proposed iCoMoCo pulmonary imaging technique can generate diagnostic quality images with 1.75 mm isotropic resolution in less than 5 min using a 6-min acquisition, on a 0.55 T scanner., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Covid-19 is a global pandemic. Early and accurate detection of positive cases prevent the further spread of this epidemic and help to treat rapidly the infected patients. During the peak of this epidemic, there was an insufficiency of Covid-19 test kits. In addition, this technique takes a considerable time in the diagnosis. Hence the need to find fast, accurate and low-cost method to replace or supplement RT PCR-based methods. Covid-19 is a respiratory disease, chest X-ray images are often used to diagnose pneumonia. From this perspective, these images can play an important role in the Covid-19 detection. In this article, we propose ConvCoroNet, a deep convolutional neural network model optimized with new method based on iterative thresholding algorithm to detect coronavirus automatically from chest X-ray images. ConvCoroNet is trained on a dataset prepared by collecting chest X-ray images of Covid-19, pneumonia and normal cases from publically datasets. The experimental results of our proposed model show a high accuracy of 99.50%, sensitivity of 98.80% and specificity of 99.85% when detecting Covid-19 from chest X-ray images. ConvCoroNet achieves promising results in the automatic detection of Covid-19 from chest X-ray images. It may be able to help radiologists in the Covid-19 detection by reducing the examination time of X-ray images.Communicated by Ramaswamy H. Sarma.

Background: Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi-institutional cohort of patients with COVID-19 using a DL-based model., Purpose: This study aimed to evaluate the performance of deep privacy-preserving federated learning (DPFL) in predicting COVID-19 outcomes using chest CT images., Methods: After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold-out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold-out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences., Results: The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79-0.85) and (95% CI: 0.77-0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models (p-value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501., Conclusion: The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi-institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process., (© 2024 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Millions of individuals around the world have been impacted by the ongoing coronavirus outbreak, known as the COVID-19 pandemic. Blockchain, Artificial Intelligence (AI), and other cutting-edge digital and innovative technologies have all offered promising solutions in such situations. AI provides advanced and innovative techniques for classifying and detecting symptoms caused by the coronavirus. Additionally, Blockchain may be utilized in healthcare in a variety of ways thanks to its highly open, secure standards, which permit a significant drop in healthcare costs and opens up new ways for patients to access medical services. Likewise, these techniques and solutions facilitate medical experts in the early diagnosis of diseases and later in treatments and sustaining pharmaceutical manufacturing. Therefore, in this work, a smart blockchain and AI-enabled system is presented for the healthcare sector that helps to combat the coronavirus pandemic. To further incorporate Blockchain technology, a new deep learning-based architecture is designed to identify the virus in radiological images. As a result, the developed system may offer reliable data-gathering platforms and promising security solutions, guaranteeing the high quality of COVID-19 data analytics. We created a multi-layer sequential deep learning architecture using a benchmark data set. In order to make the suggested deep learning architecture for the analysis of radiological images more understandable and interpretable, we also implemented the Gradient-weighted Class Activation Mapping (Grad-CAM) based colour visualization approach to all of the tests. As a result, the architecture achieves a classification accuracy rate of 0.96, thus producing excellent results.

Severe COVID-19 can lead to extensive lung disease causing lung architectural distortion. In this study we employed machine learning and statistical atlas-based approaches to explore possible changes in lung shape among COVID-19 patients and evaluated whether the extent of these changes was associated with COVID-19 severity. On a large multi-institutional dataset (N = 3443), three different populations were defined; a) healthy (no COVID-19), b) mild COVID-19 (no ventilator required), c) severe COVID-19 (ventilator required), and the presence of lung shape differences between them were explored using baseline chest CT. Significant lung shape differences were observed along mediastinal surfaces of the lungs across all severity of COVID-19 disease. Additionally, differences were seen on basal surfaces of the lung when compared between healthy and severe COVID-19 patients. Finally, an AI model (a 3D residual convolutional network) characterizing these shape differences coupled with lung infiltrates (ground-glass opacities and consolidation regions) was found to be associated with COVID-19 severity., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Anant Madabhushi reports financial support was provided by National Cancer Institute. Anant Madabhushi reports financial support was provided by Aiforia Inc. Anant Madabhushi reports financial support was provided by National Institute of Biomedical Imaging and Bioengineering. Anant Madabhushi reports financial support was provided by National Center for Research Resources. Anant Madabhushi reports financial support was provided by VA Merit Review Award. Anant Madabhushi reports financial support was provided by Breast Cancer Research Program. Anant Madabhushi reports financial support was provided by Prostate Cancer Research Program. Anant Madabhushi reports financial support was provided by Lung Cancer Research Program. Anant Madabhushi reports financial support was provided by Peer Reviewed Cancer Research Program. Anant Madabhushi reports financial support was provided by Kidney Precision Medicine Project. Anant Madabhushi reports financial support was provided by Glue Grant. Anant Madabhushi reports financial support was provided by Bristol Myers-Squibb. Anant Madabhushi reports financial support was provided by Boehringer-Ingelheim. Anant Madabhushi reports financial support was provided by Eli-Lilly. Anant Madabhushi reports financial support was provided by AstraZeneca. Anant Madabhushi reports a relationship with Picture Health that includes: board membership., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: To evaluate the neurological alterations induced by Omicron infection, to compare brain changes in chronic insomnia with those in exacerbated chronic insomnia in Omicron patients, and to examine individuals without insomnia alongside those with new-onset insomnia., Methods: In this study, a total of 135 participants were recruited between January 11 and May 4, 2023, including 26 patients with chronic insomnia without exacerbation, 24 patients with chronic insomnia with exacerbation, 40 patients with no sleep disorder, and 30 patients with new-onset insomnia after infection with Omicron (a total of 120 participants with different sleep statuses after infection), as well as 15 healthy controls who were never infected with Omicron. Neuropsychiatric data, clinical symptoms, and multimodal magnetic resonance imaging data were collected. The gray matter thickness and T1, T2, proton density, and perivascular space values were analyzed. Associations between changes in multimodal magnetic resonance imaging findings and neuropsychiatric data were evaluated with correlation analyses., Results: Compared with healthy controls, gray matter thickness changes were similar in the patients who have and do not have a history of chronic insomnia groups after infection, including an increase in cortical thickness near the parietal lobe and a reduction in cortical thickness in the frontal, occipital, and medial brain regions. Analyses showed a reduced gray matter thickness in patients with chronic insomnia compared with those with an aggravation of chronic insomnia post-Omicron infection, and a reduction was found in the right medial orbitofrontal region (mean [SD], 2.38 [0.17] vs. 2.67 [0.29] mm; P < 0.001). In the subgroups of Omicron patients experiencing sleep deterioration, patients with a history of chronic insomnia whose insomnia symptoms worsened after infection displayed heightened medial orbitofrontal cortical thickness and increased proton density values in various brain regions. Conversely, patients with good sleep quality who experienced a new onset of insomnia after infection exhibited reduced cortical thickness in pericalcarine regions and decreased proton density values. In new-onset insomnia patients post-Omicron infection, the thickness in the right pericalcarine was negatively correlated with the Self-rating Anxiety Scale (r =  - 0.538, P = 0.002, P FDR  = 0.004) and Self-rating Depression Scale (r =  - 0.406, P = 0.026, P FDR  = 0.026) scores., Conclusions: These findings help us understand the pathophysiological mechanisms involved when Omicron invades the nervous system and induces various forms of insomnia after infection. In the future, we will continue to pay attention to the dynamic changes in the brain related to insomnia caused by Omicron infection., (© 2024. The Author(s).)

To define the characteristics of fundus manifestations in patients after SARS-CoV-2 infection with multimodal imaging techniques. This is a retrospective multicenter and multimodal imaging study including 90 patients. All patients with a visual complaint occurring immediately after SARS-CoV-2 infection were referred to six clinics between December 2022 and February 2023. Demographic information and the temporal relationship between SARS-CoV-2 infection and visual symptoms were documented. The characteristics of the fundus lesions were evaluated using multimodal imaging. Ninety patients from six hospitals were included in this study, including 24 males (26.67%) and 66 (73.33%) females. Seventy-eight patients (86.66%) (146 eyes) were diagnosed with Acute Macular Neuroretinopathy (AMN). The AMN patients were primarily young women (67.95%). Sixty-eight patients (87.18%) had AMN in both eyes. Thirty-eight eyes (24.36%) included Purtscher or Purtscher-like lesions. optical coherence tomography and infrared retinal photographs can show AMN lesions well. Eleven cases were diagnosed with simple Purtscher or Purtscher-like retinopathy (2 cases, 2.22%), Vogt‒Koyanagi‒Harada (VKH) syndrome or VKH-like uveitis (3 cases, 3.33%), multiple evanescent white-dot syndrome (MEWDS) (2 cases, 2.22%), and rhino-orbital-cerebral mucormycosis (ROCM) (5 cases, 5.56%). After SARS-CoV-2 infection, diversified fundus lesions were evident in patients with visual complaints. In this report, AMN was the dominant manifestation, followed by Purtscher or Purtscher-like retinopathy, MEWDS, VKH-like uveitis, and ROCM., (© 2024. The Author(s).)

Background: The aim of the study was to investigate the muscle differences in children with osteogenesis imperfecta (OI) using opportunistic low-dose chest CT and to compare different methods for the segmentation of muscle in children., Methods: This single center retrospective study enrolled children with OI and controls undergoing opportunistic low-dose chest CT obtained during the COVID pandemic. From the CT images, muscle size (cross-sectional area) and density (mean Hounsfield Units [HU]) of the trunk muscles were measured at the mid-T4 and the mid-T10 level using two methods, the fixed thresholds and the Gaussian mixture model. The Bland-Altman method was also used to compute the strength of agreement between two methods. Comparison of muscle results between OI and controls were analyzed with Student t tests., Results: 20 children with OI (mean age, 9.1 ± 3.3 years, 15 males) and 40 age- and sex-matched controls were enrolled. Mean differences between two methods were good. Children with OI had lower T4 and T10 muscle density than controls measured by the fixed thresholds (41.2 HU vs. 48.0 HU, p < 0.01; 37.3 HU vs. 45.9 HU, p < 0.01). However, children with OI had lower T4 muscle size, T4 muscle density, T10 muscle size and T10 muscle density than controls measured by the Gaussian mixture model (110.9 vs. 127.2 cm 2 , p = 0.03; 44.6 HU vs. 51.3 HU, p < 0.01; 72.6 vs. 88.0 cm 2 , p = 0.01; 41.6 HU vs. 50.3 HU, p < 0.01, respectively)., Conclusions: Children with OI had lower trunk muscle density indicating that OI might also impair muscle quality. Moreover, the fixed thresholds may not be suitable for segmentation of muscle in children., (© 2024. The Author(s).)

The Coronavirus Disease 2019(COVID-19) has caused widespread and significant harm globally. In order to address the urgent demand for a rapid and reliable diagnostic approach to mitigate transmission, the application of deep learning stands as a viable solution. The impracticality of many existing models is attributed to excessively large parameters, significantly limiting their utility. Additionally, the classification accuracy of the model with few parameters falls short of desirable levels. Motivated by this observation, the present study employs the lightweight network MobileNetV3 as the underlying architecture. This paper incorporates the dense block to capture intricate spatial information in images, as well as the transition layer designed to reduce the size and channel number of the feature map. Furthermore, this paper employs label smoothing loss to address the inter-class similarity effects and uses class weighting to tackle the problem of data imbalance. Additionally, this study applies the pruning technique to eliminate unnecessary structures and further reduce the number of parameters. As a result, this improved model achieves an impressive 98.71% accuracy on an openly accessible database, while utilizing only 5.94 million parameters. Compared to the previous method, this maximum improvement reaches 5.41%. Moreover, this research successfully reduces the parameter count by up to 24 times, showcasing the efficacy of our approach. This demonstrates the significant benefits in regions with limited availability of medical resources., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background and Purpose: The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of two weeks and 1 month after admission by integrating radiological and clinical features., Methods: A retrospective analysis, approved by the Institutional Review Board, encompassed patients diagnosed with COVID-19 pneumonia between December 2022 and February 2023. The cohort was divided into training and validation groups in a 7:3 ratio. A trained multi-task U-Net network was deployed to segment COVID-19 pneumonia and lung regions in CT images, from which quantitative features were extracted. The eXtreme Gradient Boosting (XGBoost) algorithm was employed to construct a radiological model. A clinical model was constructed by LASSO method and stepwise regression analysis, followed by the subsequent construction of the combined model. Model performance was assessed using ROC and decision curve analysis (DCA), while Shapley's Additive interpretation (SHAP) illustrated the importance of CT features., Results: A total of 214 patients were recruited in our study. Four clinical characteristics and four CT features were identified as pivotal components for constructing the clinical and radiological models. The final four clinical characteristics were incorporated as well as the RS&#95;radiological model to construct the combined prediction model. SHAP analysis revealed that CT score difference exerted the most significant influence on the predictive performance of the radiological model. The training group's radiological, clinical, and combined models exhibited AUC values of 0.89, 0.72, and 0.92, respectively. Correspondingly, in the validation group, these values were observed to be 0.75, 0.72, and 0.81. The DCA curve showed that the combined model exhibited greater clinical utility than the clinical or radiological models., Conclusion: Our novel combined model, fusing quantitative CT features with clinical characteristics, demonstrated effective prediction of COVID-19 pneumonia progression from 2 weeks to 1 month after admission. This comprehensive model can potentially serve as a valuable tool for clinicians to develop personalized treatment strategies and improve patient outcomes., (© 2024. The Author(s).)

Objective: This study aimed to determine the risk factors associated with fluctuations in nucleic acid CT values in patients infected with the Omicron variant during an outbreak at a hospital in Changchun city., Methods: A retrospective analysis was conducted on general information, medical history, vaccination history, and laboratory test data of COVID-19 patients infected with the Omicron variant and admitted to the hospital in Changchun from March 2022 to April 2022. The study aimed to explore the factors influencing nucleic acid CT value fluctuations in COVID-19 patients infected with the Omicron variant in Changchun city., Results: Fluctuations in nucleic acid CT values were significantly correlated with occupation composition ( p = 0.030), hospital stay duration ( p = 0.000), heart rate ( p = 0.026), creatinine ( p = 0.011), platelet count ( p = 0.000), glutamic-pyruvic transaminase ( p = 0.045), and glutamic oxaloacetic transaminase ( p = 0.017). Binary logistic regression analysis revealed significant correlations between hospital stay duration ( p = 0.000), platelet count ( p = 0.019), heart rate ( p = 0.036), and nucleic acid CT value fluctuations ( p < 0.05), indicating that they were independent risk factors. Red blood cell count was identified as a factor influencing nucleic acid CT value fluctuations in Group A patients. Occupation composition, direct bilirubin, and platelet count were identified as factors influencing nucleic acid CT value fluctuations in Group B patients. Further binary logistic regression analysis indicated that occupational composition and direct bilirubin are significant independent factors for nucleic acid CT value fluctuations in Group B patients, positively correlated with occupational risk and negatively correlated with direct bilirubin., Conclusion: Therefore, enhancing patients' immunity, increasing physical exercise to improve myocardial oxygen consumption, reducing the length of hospital stays, and closely monitoring liver function at the onset of hospitalization to prevent liver function abnormalities are effective measures to control fluctuations in nucleic acid CT values., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Cao, Sun, Ding, Tian, Cui, Yang, Hu and Shi.)

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.

Purpose: Chest computed tomography (CT) is used to determine the severity of COVID-19 pneumonia, and pneumonia is associated with hyponatremia. This study aims to explore the predictive value of the semi-quantitative CT visual score for hyponatremia in patients with COVID-19 to provide a reference for clinical practice., Methods: In this cross-sectional study, 343 patients with RT-PCR confirmed COVID-19, all patients underwent CT, and the severity of lung lesions was scored by radiologists using the semi-quantitative CT visual score. The risk factors of hyponatremia in COVID-19 patients were analyzed and combined with laboratory tests. The thyroid function changes caused by SARS-CoV-2 infection and their interaction with hyponatremia were also analyzed., Results: In patients with SARS-CoV-2 infection, the total severity score (TSS) of hyponatremia was higher [M(range), 3.5(2.5-5.5) vs 3.0(2.0-4.5) scores, P =0.001], implying that patients with hyponatremia had more severe lung lesions. The risk factors of hyponatremia in the multivariate regression model included age, vomiting, neutrophils, platelet, and total severity score. SARS-CoV-2 infection impacted thyroid function, and patients with hyponatremia showed a lower free triiodothyronine (3.1 ± 0.9 vs 3.7 ± 0.9, P =0.001) and thyroid stimulating hormone level [1.4(0.8-2.4) vs 2.2(1.2-3.4), P =0.038]., Conclusion: Semi-quantitative CT score can be used as a risk factor for hyponatremia in patients with COVID-19. There is a weak positive correlation between serum sodium and free triiodothyronine in patients with SARS-CoV-2 infection., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wu, Li, Hao, Qi, Liu, Wei, Li, Zhang and Liu.)

Conventional and electron microscopy visualize structures in the micrometer to nanometer range, and such visualizations contribute decisively to our understanding of biological processes. Due to different factors in recording processes, microscopy images are subject to noise. Especially at their respective resolution limits, a high degree of noise can negatively effect both image interpretation by experts and further automated processing. However, the deteriorating effects of strong noise can be alleviated to a large extend by image enhancement algorithms. Because of the inherent high noise, a requirement for such algorithms is their applicability directly to noisy images or, in the extreme case, to just a single noisy image without a priori noise level information (referred to as blind zero-shot setting). This work investigates blind zero-shot algorithms for microscopy image denoising. The strategies for denoising applied by the investigated approaches include: filtering methods, recent feed-forward neural networks which were amended to be trainable on noisy images, and recent probabilistic generative models. As datasets we consider transmission electron microscopy images including images of SARS-CoV-2 viruses and fluorescence microscopy images. A natural goal of denoising algorithms is to simultaneously reduce noise while preserving the original image features, e.g., the sharpness of structures. However, in practice, a tradeoff between both aspects often has to be found. Our performance evaluations, therefore, focus not only on noise removal but set noise removal in relation to a metric which is instructive about sharpness. For all considered approaches, we numerically investigate their performance, report their denoising/sharpness tradeoff on different images, and discuss future developments. We observe that, depending on the data, the different algorithms can provide significant advantages or disadvantages in terms of their noise removal vs. sharpness preservation capabilities, which may be very relevant for different virological applications, e.g., virological analysis or image segmentation., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Salwig et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Implementing a structured COVID-19 lung ultrasound system, using COVID-RADS standardization. This case series exams revealed correlations between ultrasonographic and tomographic findings. Ventilatory assessments showed that higher categories required second-line oxygen. This replicable tool will aid in screening and predicting disease severity beyond the pandemic., Objective: We aimed to share our experience in implementing a structured system for COVID-19 lung findings, elucidating key aspects of the lung ultrasound score to facilitate its standardized clinical use beyond the pandemic scenario., Methods: Using a scoring system to classify the extent of lung involvement, we retrospectively analyzed the ultrasound reports performed in our institution according to COVID-RADS standardization., Results: The study included 69 thoracic ultrasound exams, with 27 following the protocol. The majority of patients were female (52%), with ages ranging from 1 to 96 years and an average of 56 years. Classification according to COVID-RADS was as follows: 11.1% in category 0, 37% in category 1, 44.4% in category 2, and 7.4% in category 3. Ground-glass opacities on tomography correlated with higher COVID-RADS scores (categories 2 and 3) in 82% of cases. Ventilatory assessment revealed that 50% of cases in higher COVID-RADS categories (2 and 3) required second-line oxygen supplementation, while none of the cases in lower categories (0 and 1) utilized this support., Conclusion: Lung ultrasound has been widely utilized as a diagnostic tool owing to its availability and simplicity of application. In the context of the pandemic emergency, a pressing need for a focused and easily applicable assessment arose. The structured reporting system, incorporating ultrasound findings for stratification, demonstrated ease of replicability. This system stands as a crucial tool for screening, predicting severity, and aiding in medical decisions, even in a non-pandemic context. Lung ultrasound enables precise diagnosis and ongoing monitoring of the disease. Ultrasound is an effective tool for assessing pulmonary findings in COVID-19. Structured reports enhance communication and are easily reproducible.

Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency and accuracy. The whale optimization algorithm (WOA) has shown promise in addressing this challenge, but issues persist, including poor stability, low efficiency, and accuracy in COVID-19 threshold image segmentation. To tackle these issues, we introduce a Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA). It incorporates a COS sampling initialization strategy (COSI), an adaptive global search approach (GS), and an all-dimensional neighborhood mechanism (ADN). COSI leverages probability density functions created from Latin hypercube sampling, ensuring even solution space coverage to improve the stability of the segmentation model. GS widens the exploration scope to combat stagnation during iterations and improve segmentation efficiency. ADN refines convergence accuracy around optimal individuals to improve segmentation accuracy. CAGWOA's performance is validated through experiments on various benchmark function test sets. Furthermore, we apply CAGWOA alongside similar methods in a multi-threshold image segmentation model for comparative experiments on lung X-ray images of infected patients. The results demonstrate CAGWOA's superiority, including better image detail preservation, clear segmentation boundaries, and adaptability across different threshold levels., (© 2024. The Author(s).)

We aimed to characterize the cognitive profile of post-acute COVID-19 syndrome (PACS) patients with cognitive complaints, exploring the influence of biological and psychological factors. Participants with confirmed SARS-CoV-2 infection and cognitive complaints ≥ 8 weeks post-acute phase were included. A comprehensive neuropsychological battery (NPS) and health questionnaires were administered at inclusion and at 1, 3 and 6 months. Blood samples were collected at each visit, MRI scan at baseline and at 6 months, and, optionally, cerebrospinal fluid. Cognitive features were analyzed in relation to clinical, neuroimaging, and biochemical markers at inclusion and follow-up. Forty-nine participants, with a mean time from symptom onset of 10.4 months, showed attention-executive function (69%) and verbal memory (39%) impairment. Apathy (64%), moderate-severe anxiety (57%), and severe fatigue (35%) were prevalent. Visual memory (8%) correlated with total gray matter (GM) and subcortical GM volume. Neuronal damage and inflammation markers were within normal limits. Over time, cognitive test scores, depression, apathy, anxiety scores, MRI indexes, and fluid biomarkers remained stable, although fewer participants (50% vs. 75.5%; p = 0.012) exhibited abnormal cognitive evaluations at follow-up. Altered attention/executive and verbal memory, common in PACS, persisted in most subjects without association with structural abnormalities, elevated cytokines, or neuronal damage markers., (© 2024. The Author(s).)

The onset of the COVID-19 pandemic allowed physicians to gain experience in lung ultrasound (LUS) during the acute phase of the disease. However, limited data are available on LUS findings during the recovery phase. The aim of this study was to evaluate the utility of LUS to assess lung involvement in patients with post-COVID-19 syndrome. This study prospectively enrolled 72 patients who underwent paired LUS and chest CT scans (112 pairs including follow-up). The most frequent CT findings were ground glass opacities (83.3%), subpleural lines (72.2%), traction bronchiectasis (37.5%), and consolidations (31.9%). LUS revealed irregular pleural lines as a common abnormality initially (56.9%), along with subpleural consolidation >2.5 mm ≤10 mm (26.5%) and B-lines (26.5%). A strong correlation was found between LUS score, calculated by artificial intelligence percentage involvement in ground glass opacities described in CT (r = 0.702, p < 0.05). LUS score was significantly higher in the group with fibrotic changes compared to the non-fibrotic group with a mean value of 19.4 ± 5.7 to 11 ± 6.6, respectively ( p < 0.0001). LUS might be considered valuable for examining patients with persistent symptoms after recovering from COVID-19 pneumonia. Abnormalities identified through LUS align with CT scan findings; thus, LUS might potentially reduce the need for frequent chest CT examinations.

Background: ARDS is a heterogeneous syndrome with distinct clinical phenotypes. Here we investigate whether the presence or absence of large pulmonary ultrasonographic consolidations can categorize COVID-19 ARDS patients requiring mechanical ventilation into distinct clinical phenotypes., Methods: This is a retrospective study performed in a tertiary-level intensive care unit in Israel between April and September 2020. Data collected included lung ultrasound (LUS) findings, respiratory parameters, and treatment interventions. The primary outcome was a composite of three ARDS interventions: prone positioning, high PEEP, or a high dose of inhaled nitric oxide., Results: A total of 128 LUS scans were conducted among 23 patients. The mean age was 65 and about two-thirds were males. 81 scans identified large consolidation and were classified as "C-type", and 47 scans showed multiple B-lines with no or small consolidation and were classified as "B-type". The presence of a "C-type" study had 2.5 times increased chance of receiving the composite primary outcome of advanced ARDS interventions despite similar SOFA scores, Pao2/FiO2 ratio, and markers of disease severity (OR = 2.49, %95CI 1.40-4.44)., Conclusion: The presence of a "C-type" profile with LUS consolidation potentially represents a distinct COVID-19 ARDS subphenotype that is more likely to require aggressive ARDS interventions. Further studies are required to validate this phenotype in a larger cohort and determine causality, diagnostic, and treatment responses., Competing Interests: The above consulting relationship had no bearing or impact on the research conducted and the preparation of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials. All other authors have no conflicts of interest to declare., (Copyright: © 2024 Dayan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

With advancements in science and technology, the depth of human research on COVID-19 is increasing, making the investigation of medical images a focal point. Image segmentation, a crucial step preceding image processing, holds significance in the realm of medical image analysis. Traditional threshold image segmentation proves to be less efficient, posing challenges in selecting an appropriate threshold value. In response to these issues, this paper introduces Inner-based multi-strategy particle swarm optimization (IPSOsono) for conducting numerical experiments and enhancing threshold image segmentation in COVID-19 medical images. A novel dynamic oscillatory weight, derived from the PSO variant for single-objective numerical optimization (PSOsono) is incorporated. Simultaneously, the historical optimal positions of individuals in the particle swarm undergo random updates, diminishing the likelihood of algorithm stagnation and local optima. Moreover, an inner selection learning mechanism is proposed in the update of optimal positions, dynamically refining the global optimal solution. In the CEC 2013 benchmark test, PSOsono demonstrates a certain advantage in optimization capability compared to algorithms proposed in recent years, proving the effectiveness and feasibility of PSOsono. In the Minimum Cross Entropy threshold segmentation experiments for COVID-19, PSOsono exhibits a more prominent segmentation capability compared to other algorithms, showing good generalization across 6 CT images and further validating the practicality of the algorithm., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Cytokines are small secreted proteins that have specific effects on cellular interactions and are crucial for functioning of the immune system. Cytokines are involved in almost all diseases, but as microscopic chemical compounds they cannot be visualized at imaging for obvious reasons. Several imaging manifestations have been well recognized owing to the development of cytokine therapies such as those with bevacizumab (antibody against vascular endothelial growth factor) and chimeric antigen receptor (CAR) T cells and the establishment of new disease concepts such as interferonopathy and cytokine release syndrome. For example, immune effector cell-associated neurotoxicity is the second most common form of toxicity after CAR T-cell therapy toxicity, and imaging is recommended to evaluate the severity. The emergence of COVID-19, which causes a cytokine storm, has profoundly impacted neuroimaging. The central nervous system is one of the systems that is most susceptible to cytokine storms, which are induced by the positive feedback of inflammatory cytokines. Cytokine storms cause several neurologic complications, including acute infarction, acute leukoencephalopathy, and catastrophic hemorrhage, leading to devastating neurologic outcomes. Imaging can be used to detect these abnormalities and describe their severity, and it may help distinguish mimics such as metabolic encephalopathy and cerebrovascular disease. Familiarity with the neuroimaging abnormalities caused by cytokine storms is beneficial for diagnosing such diseases and subsequently planning and initiating early treatment strategies. The authors outline the neuroimaging features of cytokine-related diseases, focusing on cytokine storms, neuroinflammatory and neurodegenerative diseases, cytokine-related tumors, and cytokine-related therapies, and describe an approach to diagnosing cytokine-related disease processes and their differentials. © RSNA, 2024 Supplemental material is available for this article.

Several data sets have been collected and various artificial intelligence models have been developed for COVID-19 classification and detection from both chest radiography (CXR) and thorax computed tomography (CTX) images. However, the pitfalls and shortcomings of these systems significantly limit their clinical use. In this respect, improving the weaknesses of advanced models can be very effective besides developing new ones. The inability to diagnose ground-glass opacities by conventional CXR has limited the use of this modality in the diagnostic work-up of COVID-19. In our study, we investigated whether we could increase the diagnostic efficiency by collecting a novel CXR data set, which contains pneumonic regions that are not visible to the experts and can only be annotated under CTX guidance. We develop an ensemble methodology of well-established deep CXR models for this new data set and develop a machine learning-based non-maximum suppression strategy to boost the performance for challenging CXR images. CTX and CXR images of 379 patients who applied to our hospital with suspected COVID-19 were evaluated with consensus by seven radiologists. Among these, CXR images of 161 patients who also have had a CTX examination on the same day or until the day before or after and whose CTX findings are compatible with COVID-19 pneumonia, are selected for annotating. CTX images are arranged in the main section passing through the anterior, middle, and posterior according to the sagittal plane with the reformed maximum intensity projection (MIP) method in the coronal plane. Based on the analysis of coronal MIP reconstructed CTX images, the regions corresponding to the pneumonia foci are annotated manually in CXR images. Radiologically classified posterior to anterior (PA) CXR of 218 patients with negative thorax CTX imaging were classified as COVID-19 pneumonia negative group. Accordingly, we have collected a new data set using anonymized CXR (JPEG) and CT (DICOM) images, where the PA CXRs contain pneumonic regions that are hidden or not easily recognized and annotated under CTX guidance. The reference finding was the presence of pneumonic infiltration consistent with COVID-19 on chest CTX examination. COVID-Net, a specially designed convolutional neural network, was used to detect cases of COVID-19 among CXRs. Diagnostic performances were evaluated by ROC analysis by applying six COVID-Net variants (COVIDNet-CXR3-A, -B, -C/COVIDNet-CXR4-A, -B, -C) to the defined data set and combining these models in various ways via ensemble strategies. Finally, a convex optimization strategy is carried out to find the outperforming weighted ensemble of individual models. The mean age of 161 patients with pneumonia was 49.31 ± 15.12, and the median age was 48 years. The mean age of 218 patients without signs of pneumonia in thorax CTX examination was 40.04 ± 14.46, and the median was 38. When working with different combinations of COVID-Net's six variants, the area under the curve (AUC) using the ensemble COVID-Net CXR 4A-4B-3C was .78, sensitivity 67%, specificity 95%; COVID-Net CXR 4a-3b-3c was .79, sensitivity 69% and specificity 94%. When diverse and complementary COVID-Net models are used together through an ensemble, it has been determined that the AUC values are close to other studies, and the specificity is significantly higher than other studies in the literature., (© 2024 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.)

The chest X-ray (CXR) Brixia scoring system was developed exclusively for COVID-19 severity assessment. However, no association between the score and respiratory mechanics during mechanical ventilation has been examined. Our aim was to evaluate the association between the CXR Brixia score and respiratory mechanics on the first day of mechanical ventilation in critically ill COVID-19 patients. A total of 77 COVID-19 patients who underwent mechanical ventilation and CXR in the ICU setting were retrospectively included. The CXR Brixia scoring system was applied, and respiratory mechanics data were recorded on the first day of invasive mechanical ventilation. Median Simplified Acute Physiologic Score II (SAPSII) and Sequential Organ Failure Assessment (SOFA) scores were 40 (31-54) and 6 (4-8), respectively. The median Brixia score was 14 (11-16). The correlation between the Brixia score and static compliance or driving pressure was significant, at r = -0.38, p < 0.001 and r = 0.33, p = 0.003, respectively. Using multivariable linear regression, the model with the B zone was significantly better associated with static compliance (F = 11.5, R 2  = 0.14, p = 0.001) and driving pressure (F = 11.3, R 2  = 0.13, p = 0.001). In logistic regression analysis, the Brixia score (OR 1.24; 95% CI 1.07, 1.45; p = 0.005), B zone (OR 2.60; 95% CI 1.30, 5.20; p = 0.007), C zone (OR 2.50; 95% CI 1.23, 5.11; p = 0.012), A zone (OR 2.01; 95% CI 1.16, 3.44; p = 0.012), and D zone (OR 1.84; 95% CI 1.07, 3.17; p = 0.027) significantly predicted a driving pressure > 14 cmH 2 O. There is a relationship between changes in Brixia-scored chest X-ray images and compliance and driving pressure on the first day of invasive mechanical ventilation. We identified some CXR areas using the Brixia score, and evaluation of the Brixia score may provide additional information for predicting respiratory mechanics., (© 2023. The Author(s), under exclusive licence to Società Italiana di Medicina Interna (SIMI).)

A preserved sense of smell and taste allows us to understand many environmental "messages" and results in meaningfully improvements to quality of life. With the COVID-19 pandemic, it became clear how important these senses are for social and nutritional status and catapulted this niche chemosensory research area towards widespread interest. In the current exploratory work, we assessed two groups of post-COVID-19 patients who reported having had (Group 1) or not (Group 2) a smell/taste impairment at the disease onset. The aim was to compare them using validated smell and taste tests as well as with brain magnetic resonance imaging volumetric analysis. Normative data were used for smell scores comparison and a pool of healthy subjects, recruited before the pandemic, served as controls for taste scores. The majority of patients in both groups showed an olfactory impairment, which was more severe in Group 1 (median UPSIT scores: 24.5 Group 1 vs 31.0 Group 2, p = 0.008), particularly among women (p = 0.014). No significant differences emerged comparing taste scores between Group 1 and Group 2, but dysgeusia was only present in Group 1 patients. However, for taste scores, a significant difference was found between Group 1 and controls (p = 0.005). No MRI anatomical abnormalities emerged in any patients while brain volumetric analysis suggested a significant difference among groups for the right caudate nucleus (p = 0.028), although this was not retained following Benjamini-Hochberg correction. This exploratory study could add new information in COVID-19 chemosensory long-lasting impairment and address future investigations on the post-COVID-19 patients' research., (© 2024. The Author(s).)

Coronavirus disease 2019 (COVID-19) pneumonia has had catastrophic effects worldwide. Radiology, in particular computed tomography (CT) imaging, has proven to be valuable in the diagnosis, prognostication, and longitudinal assessment of those diagnosed with COVID-19 pneumonia. This article will review acute and chronic pulmonary radiologic manifestations of COVID-19 pneumonia with an emphasis on CT and also highlighting histopathology, relevant clinical details, and some notable challenges when interpreting the literature., Competing Interests: Disclosure Nothing to disclose., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Purpose: To evaluate the trends in utilization and results of computed tomography pulmonary angiography (CTPA study) for detection of acute pulmonary embolism (PE) in the Emergency Department (ED) during different phases of COVID-19 public health emergency., Methods: We conducted a retrospective review of CTPA studies ordered through our ED in the months of March through May during five consecutive years from 2019 to 2023, designated as pre-pandemic, early, ongoing, recovery, and post-pandemic periods respectively. Collected characteristics included patient age, patient sex, and result of the study., Results: The utilization of CTPA studies for ED patients increased during the early, ongoing, and recovery periods. CTPA study utilization in the post-pandemic period was not significantly different from the pre-pandemic period (p = 0.08). No significant difference in CTPA study utilization was noted in the other periods when stratified by age group or sex, compared to the pre-pandemic period. The positivity rate of acute PE in ED patients was not significantly different in other periods compared to the pre-pandemic period., Conclusion: At our institution, the utilization and positivity rates of CTPA studies for the ED patients were not significantly different in the post-pandemic period compared to the pre-pandemic period. While studies spanning a larger timeframe and involving multiple institutions are needed to test the applicability of this observation to a wider patient population beyond our defined post-pandemic period, we conclude that our study provides some confidence to the ordering provider and the radiologist in embracing the end of COVID-19 public health emergency by the WHO and the United States HHS with respect to CTPA studies., (© 2024. The Author(s), under exclusive licence to American Society of Emergency Radiology (ASER).)

To generate synthetic medical data incorporating image-tabular hybrid data by merging an image encoding/decoding model with a table-compatible generative model and assess their utility. We used 1342 cases from the Stony Brook University Covid-19-positive cases, comprising chest X-ray radiographs (CXRs) and tabular clinical data as a private dataset (pDS). We generated a synthetic dataset (sDS) through the following steps: (I) dimensionally reducing CXRs in the pDS using a pretrained encoder of the auto-encoding generative adversarial networks (αGAN) and integrating them with the correspondent tabular clinical data; (II) training the conditional tabular GAN (CTGAN) on this combined data to generate synthetic records, encompassing encoded image features and clinical data; and (III) reconstructing synthetic images from these encoded image features in the sDS using a pretrained decoder of the αGAN. The utility of sDS was assessed by the performance of the prediction models for patient outcomes (deceased or discharged). For the pDS test set, the area under the receiver operating characteristic (AUC) curve was calculated to compare the performance of prediction models trained separately with pDS, sDS, or a combination of both. We created an sDS comprising CXRs with a resolution of 256 × 256 pixels and tabular data containing 13 variables. The AUC for the outcome was 0.83 when the model was trained with the pDS, 0.74 with the sDS, and 0.87 when combining pDS and sDS for training. Our method is effective for generating synthetic records consisting of both images and tabular clinical data., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Introduction: Zambia is experiencing a critical shortage of radiologists responsible for interpreting X-ray images. Nine radiologists serve the entire population of over 18 million people. Consequently, referring physicians can receive reports late and often receive X-ray images without radiological reports attached, which may lead to delayed diagnoses and treatment of critically injured patients. This challenge could be alleviated if radiographers could assist with interpreting X-ray images. This study was undertaken to subject a cohort of Zambian radiographers to a training intervention, however, the COVID-19 pandemic necessitated using a novel approach to the intervention by delivering the training mainly through social media but also through face-to-face lectures., Methods: A cohort of 27 radiographers employed at eight public hospitals in the Copperbelt Province of Zambia undertook a training intervention using face-to-face training and image discussions on the social media WhatsApp® platform. The participants underwent a pre-and post-test in which they were asked to interpret 20 adult trauma CXR images. For the training intervention, the radiographers attended a face-to-face image interpretation lecture, after which they received training images with a radiologist report weekly for eight weeks via the WhatsApp® platform. Participants were encouraged to discuss and pose questions via the platform., Results: The cohort of radiographers (n = 27) showed an improvement in their interpretation skills for trauma CXR images. The interpretation median scores ranged from approximately 82% to 93% in the pre-test and 85% to 97% in the post-test. The Wilcoxon signed-rank tests revealed significant differences in the interpretation ability skills for 12 of the 20 CXR images after the 8-week training, demonstrating the successful implementation of the program. When comparing three categories of radiographers' years of experience (1-5; >5-10; and >10 years), the Kruskal Wallis test could not identify significant differences in the CXR image interpretation skills among the different categories of experience (P = 0.1616). When comparing the interpretation skills of radiographers working at the three different hospital levels (Level 3 with a full-time radiologist and more than ten radiographers; Level 1 and 2 without a full-time radiologist; Level 2 with six to ten radiographers; and Level 1 with five or less radiographers), the Kruskal Wallis test revealed that the level of the hospital where the radiographers were employed significantly influenced their skills to interpret the CXR images (P = 0.0323)., Conclusion: This type of novel training intervention is urgently required in the Copperbelt Province of Zambia. The results show that the training process was implemented successfully to improve radiographers' image interpretation skills of adult trauma CXR images., Implications for Practice: Promoting radiographers' involvement in image interpretation will likely improve imaging services in Zambia, considering the critical shortage of radiologists., (Copyright © 2024. Published by Elsevier Inc.)

Background: Classical pulmonary thromboembolism (TE) and local pulmonary thrombosis (PT) have been suggested as mechanisms of thrombosis in COVID-19. However, robust evidence is still lacking because this was mainly based on retrospective studies, in which patients were included when TE was suspected., Methods: All patients with COVID-19 pneumonia underwent computed tomography and pulmonary angiography in a prospective study. The main objective was to determine the number and percentage of thrombi surrounded by lung opacification (TSO) in each patient, as well as their relationship with percentage of lung involvement (TLI), to distinguish classical TE (with a random location of thrombi that should correspond to a percentage of TSO equivalent to the TLI) from PT. We determined TLI by artificial intelligence. Analyses at patient level (TLI and percentage of TSO) and at thrombi level (TLI and TSO) were performed., Results: We diagnosed TE in 70 out of 184 patients. Three (2-8) thrombi/patient were detected. The percentage of TSO was 100% (75-100) per patient, and TLI was 19.9% (4.6-35.2). Sixty-five patients (92.9%) were above the random scenario with higher percentage of TSO than TLI. Most thrombi were TSO (n = 299, 75.1%). When evaluating by TLI (<10%, 10%-20%, 20%-30% and >30%), percentage of TSO was higher in most groups. Thrombi were mainly in subsegmental/segmental arteries, and percentage of TSO was higher in all locations., Conclusions: Thrombi in COVID-19 were found within lung opacities in a higher percentage than lung involvement, regardless of TLI and clot location, supporting the hypothesis of local PT rather than "classic TE"., (© 2024 The Authors. European Journal of Clinical Investigation published by John Wiley & Sons Ltd on behalf of Stichting European Society for Clinical Investigation Journal Foundation.)

Coronavirus disease-2019 (COVID-19) can cause acute ischemic stroke (AIS) due to large vessel occlusion (LVO). Some cases of COVID-19-related LVO are known to be resistant to mechanical thrombectomy and have different characteristics from non-COVID-19-related LVO. Inflammation of the occluded arterial wall is suspected as one of the causes of such differences, but the exact mechanism is not fully understood. A 52-year-old man suffered from AIS due to left vertebral artery (VA) occlusion during the recovery period after mild COVID-19. Successful recanalization of the left VA was achieved with antithrombotic therapy, but a late and reversible edematous lesion appeared in part of the brainstem adjacent to the left VA, with abnormal enhancement in both the left VA wall and medulla oblongata on postcontrast magnetic resonance imaging. We suggest that the left VA wall inflammation, induced by COVID-19, caused the ischemic stroke and extended to the brainstem, and an incidental thrombosed unruptured aneurysm of the left VA accelerated these changes. This case provides the first evidence of LVO after COVID-19 in which the pathological conditions in the brainstem adjacent to the affected artery could be observed with neuroimaging and inflammation of the arterial wall was indirectly confirmed. Physicians should be aware that unconventional ischemic stroke may develop in some patients during the recovery period after COVID-19., Competing Interests: Declaration of conflicting interestsThe author(s) declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Introduction: Radiologists have extensively employed the interpretation of chest X-rays (CXR) to identify visual markers indicative of COVID-19 infection, offering an alternative approach for the screening of infected individuals. This research article presents CovMediScanX, a deep learning-based framework designed for a rapid and automated diagnosis of COVID-19 from CXR scan images., Methods: The proposed approach encompasses gathering and preprocessing CXR image datasets, training deep learning-based custom-made Convolutional Neural Network (CNN), pre-trained and hybrid transfer learning models, identifying the highest-performing model based on key evaluation metrics, and embedding this model into a web interface called CovMediScanX, designed for radiologists to detect the COVID-19 status in new CXR images., Results: The custom-made CNN model obtained a remarkable testing accuracy of 94.32% outperforming other models. CovMediScanX, employing the custom-made CNN underwent evaluation with an independent dataset also. The images in the independent dataset are sourced from a scanning machine that is entirely different from those used for the training dataset, highlighting a clear distinction of datasets in their origins. The evaluation outcome highlighted the framework's capability to accurately detect COVID-19 cases, showcasing encouraging results with a precision of 73% and a recall of 84% for positive cases. However, the model requires further enhancement, particularly in improving its detection of normal cases, as evidenced by lower precision and recall rates., Conclusion: The research proposes CovMediScanX framework that demonstrates promising potential in automatically identifying COVID-19 cases from CXR images. While the model's overall performance on independent data needs improvement, it is evident that addressing bias through the inclusion of diverse data sources during training could further enhance accuracy and reliability., (Copyright © 2024. Published by Elsevier Inc.)

Over the past two decades, machine analysis of medical imaging has advanced rapidly, opening up significant potential for several important medical applications. As complicated diseases increase and the number of cases rises, the role of machine-based imaging analysis has become indispensable. It serves as both a tool and an assistant to medical experts, providing valuable insights and guidance. A particularly challenging task in this area is lesion segmentation, a task that is challenging even for experienced radiologists. The complexity of this task highlights the urgent need for robust machine learning approaches to support medical staff. In response, we present our novel solution: the D-TrAttUnet architecture. This framework is based on the observation that different diseases often target specific organs. Our architecture includes an encoder-decoder structure with a composite Transformer-CNN encoder and dual decoders. The encoder includes two paths: the Transformer path and the Encoders Fusion Module path. The Dual-Decoder configuration uses two identical decoders, each with attention gates. This allows the model to simultaneously segment lesions and organs and integrate their segmentation losses. To validate our approach, we performed evaluations on the Covid-19 and Bone Metastasis segmentation tasks. We also investigated the adaptability of the model by testing it without the second decoder in the segmentation of glands and nuclei. The results confirmed the superiority of our approach, especially in Covid-19 infections and the segmentation of bone metastases. In addition, the hybrid encoder showed exceptional performance in the segmentation of glands and nuclei, solidifying its role in modern medical image analysis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objectives: To automatically label chest radiographs and chest CTs regarding the detection of pulmonary infection in the report text, to calculate the number needed to image (NNI) and to investigate if these labels correlate with regional epidemiological infection data., Materials and Methods: All chest imaging reports performed in the emergency room between 01/2012 and 06/2022 were included (64,046 radiographs; 27,705 CTs). Using a regular expression-based text search algorithm, reports were labeled positive/negative for pulmonary infection if described. Data for regional weekly influenza-like illness (ILI) consultations (10/2013-3/2022), COVID-19 cases, and hospitalization (2/2020-6/2022) were matched with report labels based on calendar date. Positive rate for pulmonary infection detection, NNI, and the correlation with influenza/COVID-19 data were calculated., Results: Between 1/2012 and 2/2020, a 10.8-16.8% per year positive rate for detecting pulmonary infections on chest radiographs was found (NNI 6.0-9.3). A clear and significant seasonal change in mean monthly detection counts (102.3 winter; 61.5 summer; p < .001) correlated moderately with regional ILI consultations (weekly data r = 0.45; p < .001). For 2020-2021, monthly pulmonary infection counts detected by chest CT increased to 64-234 (23.0-26.7% per year positive rate, NNI 3.7-4.3) compared with 14-94 (22.4-26.7% positive rate, NNI 3.7-4.4) for 2012-2019. Regional COVID-19 epidemic waves correlated moderately with the positive pulmonary infection CT curve for 2020-2022 (weekly new cases: r = 0.53; hospitalizations: r = 0.65; p < .001)., Conclusion: Text mining of radiology reports allows to automatically extract diagnoses. It provides a metric to calculate the number needed to image and to track the trend of diagnoses in real time, i.e., seasonality and epidemic course of pulmonary infections., Clinical Relevance: Digitally labeling radiology reports represent previously neglected data and may assist in automated disease tracking, in the assessment of physicians' clinical reasoning for ordering radiology examinations and serve as actionable data for hospital workflow optimization., Key Points: • Radiology reports, commonly not machine readable, can be automatically labeled with the contained diagnoses using a regular-expression based text search algorithm. • Chest radiograph reports positive for pulmonary infection moderately correlated with regional influenza-like illness consultations (weekly data; r = 0.45; p < .001) and chest CT reports with the course of the regional COVID-19 pandemic (new cases: r = 0.53; hospitalizations: r = 0.65; p < 0.001). • Rendering radiology reports into data labels provides a metric for automated disease tracking, the assessment of ordering physicians clinical reasoning and can serve as actionable data for workflow optimization., (© 2023. The Author(s).)

Abstract: Both the central and peripheral nervous systems can be affected in COVID-19. Although MRI is the primary investigative tool for neurological imaging, FDG PET may show additional areas of involvement in the brain in the form of regional hypometabolism or hypermetabolism, secondary to synaptic dysfunction and electrical or glial activation. We present a case series of 4 patients who had neurological symptoms attributable to COVID-19 infection with abnormalities in the brain FDG PET scan., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Rationale and Objectives: Neurological complications associated with coronavirus disease (COVID-19) have been reported in children; however, data on neuroimaging findings remain limited. This study aimed to comprehensively examine neuroimaging patterns of COVID-19 in children and their relationship with clinical outcomes., Materials and Methods: This retrospective cross-sectional study involved reviewing the medical records and MRI scans of 95 children who developed new neurological symptoms within 2-4 weeks of clinical and laboratory confirmation of COVID-19. Patients were categorized into four groups based on guidelines approved by the Centers for Disease Control and Prevention (CDC). Initial brain/spinal MRI was performed. Images were reviewed by three blinded radiologists, and the findings were analyzed and categorized based on the observed patterns in the brain and spinal cord. Follow-up MRI was performed and analyzed to track lesion progression., Results: Encephalopathy was the most common neurological symptom (50.5%). The most common initial MRI involvement patterns were non-confluent multifocal hyperintense white matter (WM) lesions (36.8%) and ischemia (18.9%). Most patients who underwent follow-up MRI (n = 56) showed complete resolution (69.9%); however, some patients developed encephalomalacia and myelomalacia (23.2% and 7.1%, respectively). Non-confluent hyperintense WM lesions were associated with good outcomes (45.9%, P = 0.014), whereas ischemia and hemorrhage were associated with poor outcomes (44.1%, P < 0.001)., Conclusion: This study revealed diverse neuroimaging patterns in pediatric COVID-19 patients. Non-confluent WM lesions were associated with good outcomes, whereas ischemia and hemorrhage were associated with poorer prognoses. Understanding these patterns is crucial for their early detection, accurate diagnosis, and appropriate management., Competing Interests: Declaration of Competing Interest The authors of this manuscript declare no relevant conflicts of interest and no relationships with any companies whose products or services may be related to the subject matter of the article., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background and Objectives: Pulmonary manifestation of coronavirus disease 2019 (COVID-19) is described using standardized computed tomography (CT) morphologic criteria. In this study, we investigated possible associations between thoracic CT manifestations in COVID-19 pneumonia and typical comorbidities, as well as clinical course., Methods: We analyzed clinical data and pulmonary imaging of 61 patients with positive PCR test. Pulmonary changes were categorized and reviewed for associations with pre-existing comorbidities and clinical course., Results: Compared to patients with atypical infiltrate patterns (2/19, 10.5%), 25 patients with typical infiltrate patterns (25/42, 59.5%) were significantly more likely to receive intensive care (p<0.001). In addition, patients with typical infiltrate patterns were more likely to receive non-invasive ventilation (12/42, 28.6%, p=0.040) and high-flow therapy (8/42, 19%, p=0.041) compared to patients with atypical infiltrate patterns. Mortality was also higher in patients with typical infiltrate patterns, with 15 patients (15/42, 35.7%) dying during follow-up compared to only 1 patient with atypical infiltrate pattern (1/19, 10.5%, p=0.012). No significant association between specific comorbidities and the resulting infiltrate pattern could be demonstrated., Conclusions: Patients with a typical COVID-19 infiltrate pattern are more likely to receive intensive care and show higher mortality rates. Further analysis with larger patient collectives is needed to identify specific risk factors for typical COVID-19 pneumonia., (© 2023. The Author(s).)