Research on the classification of CT images of COVID-19 by pre-training model based on self-attention mechanism

Nowadays, with the outbreak of COVID-19, the prevention and treatment of COVID-19 has gradually become the focus of social disease prevention, and most patients are also more concerned about the symptoms. COVID-19 has symptoms similar to the common cold, and it cannot be diagnosed based on the symptoms shown by the patient, so it is necessary to observe medical images of the lungs to finally determine whether they are COVID-19 positive. As the number of patients with symptoms similar to pneumonia increases, more and more medical images of the lungs need to be generated. At the same time, the number of physicians at this stage is far from meeting the needs of patients, resulting in patients unable to detect and understand their own conditions in time. In this regard, we have performed image augmentation, data cleaning, and designed a deep learning classification network based on the data set of COVID-19 lung medical images. accurate classification judgment. The network can achieve 95.76% classification accuracy for this task through a new fine-tuning method and hyperparameter tuning we designed, which has higher accuracy and less training time than the classic convolutional neural network model. © 2023 SPIE.

Mixup-Inf-Net: A data augmentation algorithm for segmentation of new coronary pneumonia infections

In 2020, the global spread of Coronavirus Disease 2019 exposed entire world to a severe health crisis. This has limited fast and accurate screening of suspected cases due to equipment shortages and and harsh testing environments. The current diagnosis of suspected cases has benefited greatly from the use of radiographic brain imaging, also including X-ray and scintigraphy, as a crucial addition to screening tests for new coronary pneumonia disease. However, it is impractical to gather enormous volumes of data quickly, which makes it difficult for depth models to be trained. To solve these problems, we obtained a new dataset by data augmentation Mixup method for the used chest CT slices. It uses lung infection segmentation (Inf-Net [1]) in a deep network and adds a learning framework with semi-supervised to form a Mixup-Inf-Net semi-supervised learning framework model to identify COVID-19 infection area from chest CT slices. The system depends primarily on unlabeled data and merely a minimal amount of annotated data is required;therefore, the unlabeled data generated by Mixup provides good assistance. Our framework can be used to improve improve learning and performance. The SemiSeg dataset and the actual 3D CT images that we produced are used in a variety of tests, and the analysis shows that Mixup-Inf-Net semi-supervised outperforms most SOTA segmentation models learning framework model in this study, which also enhances segmentation performance. © 2023 SPIE.

COVID-19: comparison lung CT signs and biochemical parameters in the groups of patients with three-time positive RT-PCR and with triple negative RT-PCR test during the period of hospitalization.

Research goal. Comparative characteristics of the dynamics of CT semiotics and biochemical parameters of two groups of patients: with positive RT-PCR and with triple negative RT-PCR. Reflection of the results by comparing them with the data already available in the literature. The aim of the study is to compare the dynamics of CT semiotics and biochemical parameters of blood tests in two groups of patients: with positive RT-PCR and with triple negative RT-PCR. We also reflect the results by comparing them with the data already available in the literature. Materials and methods. We have performed a retrospective analysis of CT images of 66 patients: group I (n1 = 33) consists of patients who had three- time negative RT-PCR (nasopharyngeal swab for SARS-CoV-2 RNA) during hospitalization, and group II (n2 = 33) includes patients with triple positive RT-PCR. An important selection criterion is the presence of three CT examinations (primary, 1st CT and two dynamic examinations - 2nd CT and 3rd CT) and at least two results of biochemistry (C-reactive protein (CRP), fibrinogen, prothrombin time, procalcitonin) performed in a single time interval of +/- 5 days from 1st CT, upon admission, and +/- 5 days from 3st CT. A total of 198 CT examinations of the lungs were analyzed (3 examinations per patient). Results. The average age of patients in the first group was 58 +/- 14.4 years, in the second - 64.9 +/- 15.7 years. The number of days from the moment of illness to the primary CT scan 6.21 +/- 3.74 in group I, 7.0 (5.0-8.0) in group II, until the 2nd CT scan - 12.5 +/- 4, 87 and 12.0 (10.0-15.0), before the 3rd CT scan - 22.0 (19.0-26.0) and 22.0 (16.0-26.0), respectively. In both groups, all 66 patients (100%), the primary study identified the double-sided ground-glass opacity symptom and 36 of 66 (55%) patients showed consolidation of the lung tissue. Later on, a first follow-up CT defined GGO not in all the cases: it was presented in 22 of 33 (67%) patients with negative RT-PCR (group I) and in 28 of 33 (85%) patients with the positive one (group II). The percentage of studies showing consolidation increased significantly: up to 30 of 33 (91%) patients in group I, and up to 32 of 33 (97%) patients in group II. For the first time, radiological symptoms of "involutional changes" appeared: in 17 (52%) patients of the first group and in 5 (15%) patients of the second one. On second follow-up CT, GGO and consolidations were detected less often than on previous CT: in 1 and 27 patients of group I (3% and 82%, respectively) and in 6 and 30 patients of group II (18% and 91%, respectively), although the consolidation symptom still prevailed significantly . The peak of "involutional changes" occurred on last CT: 31 (94%) and 25 (76%) patients of groups I and II, respectively.So, in the groups studied, the dynamics of changes in lung CT were almost equal. After analyzing the biochemistry parameters, we found out that CRP significantly decreased in 93% of patients (p < 0.001) in group I;in group II, there was a statistically significant decrease in the values of C-reactive protein in 81% of patients (p = 0.005). With an increase in CT severity of coronavirus infection by one degree, an increase in CRP by 41.8 mg/ml should be expected. In group I, a statistically significant (p = 0.001) decrease in fibrinogen was recorded in 77% of patients;and a similar dynamic of this indicator was observed in group II: fibrinogen values decreased in 66% of patients (p = 0.002). Such parameters as procalcitonin and prothrombin time did not significantly change during inpatient treatment of the patients of the studied groups (p = 0.879 and p = 0.135), which may indicate that it is inappropriate to use these parameters in assessing dynamics of patients with a similar course of the disease. When comparing the outcomes of the studied groups, there was a statistically significant higher mortality in group II - 30.3%, in group I - 21.2% (p = 0.043). Conclusion. According to our data, a course of the disease does not significantly differ in the groups o patients with positive RT-PCR and three-time negative RT-PCR. A negative RT-PCR analysis may be associated with an individual peculiarity of a patient such as a low viral load of SARS-CoV-2 in the upper respiratory tract. Therefore, with repeated negative results on the RNA of the virus in the oro- and nasopharynx, one should take into account the clinic, the X-ray picture and biochemical indicators in dynamics and not be afraid to make a diagnosis of COVID-19.Copyright © 2021 ALIES. All rights reserved.

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the reso-lution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images;and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution (RFAFN). Specifically, we design a contextual feature extraction block (CFEB) that can extract CT image features more efficiently and accurately than ordinary residual blocks. In addition, we propose a feature-weighted cascading strategy (FWCS) based on attentional feature fusion blocks (AFFB) to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information. Finally, we suggest a global hierarchical feature fusion strategy (GHFFS), which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels. Numerous experiments show that our method performs better than most of the state-of-the-art (SOTA) methods on the COVID-19 chest CT dataset. In detail, the peak signal-to-noise ratio (PSNR) is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at x3 SR compared to the suboptimal method, but the number of parameters and multi-adds are reduced by 22K and 0.43G, respectively. Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.

Characteristics of liver injury in cases of COVID-19.

In novel coronavirus disease 2019 (COVID-19), liver injury was found at a high rate, and reports from outside Japan revealed that such injury was related to severity. We examined the characteristics of liver injury in 15 cases of COVID-19. Thirteen of these patients received antiviral therapy, such as favipiravir, remdesivir, and hydroxychloroquine. Liver injury was observed in eight cases at admission for COVID-19. The hepatic CT attenuation values at admission were significantly lower in nine patients who developed liver damage or showed its exacerbation during the treatment than in the remaining patients. Drug-induced liver injury due to antiviral drug was suspected in six cases. Liver injury due to COVID-19 may be related to low hepatic CT attenuation values and be modified by antiviral drugs.Copyright © 2021 The Japan Society of Hepatology.

SAUNet++: an automatic segmentation model of COVID-19 lesion from CT slices

The coronavirus disease 2019 (COVID-19) epidemic has spread worldwide and the healthcare system is in crisis. Accurate, automated and rapid segmentation of COVID-19 lesion in computed tomography (CT) images can help doctors diagnose and provide prognostic information. However, the variety of lesions and small regions of early lesion complicate their segmentation. To solve these problems, we propose a new SAUNet++ model with squeeze excitation residual (SER) module and atrous spatial pyramid pooling (ASPP) module. The SER module can assign more weights to more important channels and mitigate the problem of gradient disappearance;the ASPP module can obtain context information by atrous convolution using various sampling rates. In addition, the generalized dice loss (GDL) can reduce the correlation between lesion size and dice loss, and is introduced to solve the problem of small regions segmentation of COVID-19 lesion. We collected multinational CT scan data from China, Italy and Russia and conducted extensive comparative and ablation studies. The experimental results demonstrated that our method outperforms state-of-the-art models and can effectively improve the accuracy of COVID-19 lesion segmentation on the dice similarity coefficient (our: 87.38% vs. U-Net++: 84.25%), sensitivity (our: 93.28% vs. U-Net++: 89.85%) and Hausdorff distance (our: 19.99 mm vs. U-Net++: 26.79 mm), respectively.

COVID-19 Image Segmentation Algorithms Based on Conditional Generative Adversarial Network

The size and location of the lesions in CT images of novel corona virus pneumonia (COVID-19) change all the time, and the lesion areas have low contrast and blurred boundaries, resulting in difficult segmentation. To solve this problem, a COVID-19 image segmentation algorithm based on conditional generative adversarial network (CGAN) is proposed. Uses the improved DeeplabV3+ network as a generator, which enhances the extraction of multi-scale contextual features, reduces the number of network parameters and improves the training speed. A Markov discriminator with 6 fully convolutional layers is proposed instead of a common discriminator, with the aim of focusing more on the local features of the CT image. By continuously adversarial training between the generator and the discriminator, the network weights are optimised so that the final segmented image generated by the generator is infinitely close to the ground truth. On the COVID-19 CT public dataset, the area under the curve of ROC, F1-Score and dice similarity coefficient achieved 96.64%, 84.15% and 86.14% respectively. The experimental results show that the proposed algorithm is accurate and robust, and it has the possibility of becoming a safe, inexpensive, and time-saving medical assistant tool in clinical diagnosis, which provides a reference for computer-aided diagnosis. © 2022 ACM.

Semi-Supervised Modified-UNet for Lung Infection Image Segmentation

Automatic lung infection segmentation in computed tomography (CT) scans can offer great assistance in radiological diagnosis by improving accuracy and reducing time required for diagnosis. The biggest challenges for deep learning (DL) models in segmenting infection region are the high variances in infection characteristics, fuzzy boundaries between infected and normal tissues, and the troubles in getting large number of annotated data for training. To resolve such issues, we propose a Modified U-Net (Mod-UNet) model with minor architectural changes and significant modifications in the training process of vanilla 2D UNet. As part of these modifications, we updated the loss function, optimization function, and regularization methods, added a learning rate scheduler and applied advanced data augmentation techniques. Segmentation results on two Covid-19 Lung CT segmentation datasets show that the performance of Mod-UNet is considerably better than the baseline U-Net. Furthermore, to mitigate the issue of lack of annotated data, the Mod-UNet is used in a semi-supervised framework (Semi-Mod-UNet) which works on a random sampling approach to progressively enlarge the training dataset from a large pool of unannotated CT slices. Exhaustive experiments on the two Covid-19 CT segmentation datasets and on a real lung CT volume show that the Mod-UNet and Semi-Mod-UNet significantly outperform other state-of-theart approaches in automated lung infection segmentation. IEEE

Automated Detection of Covid-19Waves with Computerized Tomography Scan Using Deep Learning

Covid-19 is a newly found corona virus that causes an infectious disease. An accurate diagnosis of several waves in Covid-19 is still a tremendous confront due to the difficulties of marking infection areas, and it is an emergency and important for worldwide in 2020 and still now. There is almost no difference between common pneumonia and other viral pneumonia using CT scanned images, so false-negative images may be obtained. An ensemble of deep multi-instance learning (DMIL), train a blotch-level classifier and view the chest CT images as a bag of samples to avoid false negative. Mask R-CNN is used to train an image-level classifier that labels input image as common pneumonia or Covid pneumonia. These Ensemble models of DMIL with Mask R-CNN show an accuracy of 98.96%. These advantages make our model an efficient tool in the screening of Covid-19. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

COVID-19 Detection by Using Handcrafted Features Extracted From Chest CT-Scan Images

Coronavirus illness, which was initially diagnosed in 2019 but has propagated rapidly across the globe, has led to increased fatalities. According to professional physicians who examined chest CT scans, COVID-19 behaves differently than various viral cases of pneumonia. Even though the illness only recently emerged, a number of research investigations have been performed wherein the progression of the disease impacts mostly on the lungs are identified using thoracic CT scans. In this work, automated identification of COVID-19 is used by using machine learning classifier trained on more than 1000+ lung CT Scan images. As a result, immediate diagnosis of COVID-19, which is very much necessary in the opinion of healthcare specialists, is feasible. To improve detection accuracy, the feature extraction method are applied on regions of interests. Feature extraction approaches, including Discrete Wavelet Transform (DWT), Grey Level Cooccurrence Matrix (GLCM), Grey Level Run Length Matrix (GLRLM), and Grey-Level Size Zone Matrix (GLSZM) algorithms are used. Then the classification by using Support Vector Machines (SVM) is used. The classification accuracy is assessed by using precision, specificity, accuracy, sensitivity and F-score measures. Among all feature extraction methods, the GLCM approach has given the optimum classification accuracy of 95.6%. . © 2023 IEEE.

Multi-Joint Unit Network for COVID-19 Diagnosis using X-Ray and CT Images

The global COVID-19 pandemic has caused a health crisis globally. Automated diagnostic methods can control the spread of the pandemic, as well as assists physicians to tackle high workload conditions through the quick treatment of affected patients. Owing to the scarcity of medical images and from different resources, the present image heterogeneity has raised challenges for achieving effective approaches to network training and effectively learning robust features. We propose a multi-joint unit network for the diagnosis of COVID-19 using the joint unit module, which leverages the receptive fields from multiple resolutions for learning rich representations. Existing approaches usually employ a large number of layers to learn the features, which consequently requires more computational power and increases the network complexity. To compensate, our joint unit module extracts low-, same-, and high-resolution feature maps simultaneously using different phases. Later, these learned feature maps are fused and utilized for classification layers. We observed that our model helps to learn sufficient information for classification without a performance loss and with faster convergence. We used three public benchmark datasets to demonstrate the performance of our network. Our proposed network consistently outperforms existing state-of-the-art approaches by demonstrating better accuracy, sensitivity, and specificity and F1-score across all datasets. © 2022 ACM.

Uptake of vaccination in older Indian patients with cancer: A cross-sectional observational study

Background: Older patients with cancer are at a higher risk of invasive infections. Vaccination is an effective approach to decrease the mortality and morbidity associated with infections. Objective(s): Our primary objective was to evaluate the proportion of older patients with cancer who had received routine vaccinations against pneumococcal, influenza, and coronavirus disease 2019 (COVID-19). Our secondary objective was to identify the factors associated with vaccine uptake such as age, sex, education, marital status, comorbidities, and place of residence. Material(s) and Method(s): This cross-sectional observational study was conducted in the geriatric oncology outpatient clinic of the Department of Medical Oncology at the Tata Memorial Hospital, a tertiary care cancer hospital in Mumbai, India, from February 2020 to January 2023. We included all patients aged >=60 years who were evaluated in the geriatric oncology clinic during the study period and for whom the immunization details were available. The uptake of COVID-19 vaccine was calculated from March 2021 onwards, which was when the COVID-19 vaccine became available to patients aged >=60 years in India. Result(s): We enrolled 1762 patients;1342 (76.2%) were male. The mean age was 68.4 (SD, 5.8) years;795 (45%) patients were from the west zone of India. Only 12 (0.68%) patients had received the pneumococcal vaccine, and 13 (0.7%) had received the influenza vaccine. At least one dose of the COVID-19 vaccine had been taken by 1302 of 1562 patients (83.3%). On univariate logistic regression, education, marital status, geographic zone of residence, and primary tumor site were correlated with the uptake of COVID-19 vaccine. Factors associated with a greater COVID-19 vaccine uptake included education (up to Std 10 and higher vs. less than Std 10: Odds Ratio [OR], 1.46;95% confidence interval [CI], 1.07-1.99;P = 0.018, and illiterate vs. less than Std 10: OR, 0.70;95% CI, 0.50-0.99;P = 0.041), marital status (unmarried vs. married: OR, 0.27;95% CI, 0.08-1.08;P = 0.046, and widow/widower vs. married: OR, 0.67;95% CI, 0.48-0.94;P = 0.017), lung and gastrointestinal vs. head-and-neck primary tumors (lung cancer vs. head-and-neck cancer: OR, 1.60;95% CI, 1.02-2.47;P = 0.038, and gastrointestinal vs.head-and-neck cancer: OR, 2.18;95% CI, 1.37-3.42;P < 0.001), and place of residence (west zone vs. central India: OR, 0.34;95% CI, 0.13-0.75;P = 0.015). Conclusion(s): Fewer than 1 in 100 older Indian patients with cancer receive routine immunization with influenza and pneumococcal vaccines. Hearteningly, the uptake of COVID-19 vaccination in older Indian patients with cancer is over 80%, possibly due to the global recognition of its importance during the pandemic. Similar measures as those used to increase the uptake of COVID-19 vaccines during the pandemic may be beneficial to increase the uptake of routine vaccinations.Copyright © 2023 Cancer Research, Statistics, and Treatment.

Early detection of COVID-19 using deep learning architectures: CNN and resnet-101

The main purpose of this paper was to classify if subject has a COVID-19 or not base on CT scan. CNN and resNet-101 neural network architectures are used to identify the coronavirus. The experimental results showed that the two models CNN and resNet-101 can identify accurately the patients have COVID-19 from others with an excellent accuracy of 83.97 % and 90.05 % respectively. The results demonstrates the best ability of the used models in the current application domain. © 2022 IEEE.

Management of lung cancer in the COVID-19 pandemic: A review

Since its inception, the COVID-19 pandemic has affected health care as a whole. Cancer patients in general and those suffering from lung cancer in particular are a vulnerable group because of their many intrinsic characteristics and care needs. How SARS-CoV-2 (COVID-19) infection affects these patients regarding their risk of infection and outcome in this patient cohort is still to be determined. In this review, we tried to summarize our main concerns regarding COVID-19 in the context of cancer patients from a clinical and multidisciplinary approach. Different types of lung cancer treatments (chemotherapy, radiation therapy and immunotherapy) may also influence the risk of infection and condition the patient's risk of having a worse outcome. Lung cancer patients require frequent radiologic study follow-ups, which may be affected by COVID-19 pandemic. COVID-19 related incidental radiologic findings can appear in routinely scheduled radiology tests, which may be difficult to interpret. Also cancer treatment induced pneumonitis may have similar radiologic features similar to those in acute SARS-CoV-2 pneumonia and lead to a wrong diagnosis. The different health care needs, the requirement for continuous health care access and follow-ups, and the clinical traials in which this patient population might be enrrolled are all being affected by the current COVID-19 health crisis. The COVID-19 pandemic has put health care providers and institutions in difficult situations and obliged them to face challenging ethical scenarios. These issues, in turn, have also affected the psychological well-being of health care workers.Copyright © The Author(s) 2021.

Classification of COVID-19 cases from chest CT volumes using hybrid model of 3D CNN and 3D MLP-Mixer

This paper proposes an automated classification method of COVID-19 chest CT volumes using improved 3D MLP-Mixer. Novel coronavirus disease 2019 (COVID-19) spreads over the world, causing a large number of infected patients and deaths. Sudden increase in the number of COVID-19 patients causes a manpower shortage in medical institutions. Computer-aided diagnosis (CAD) system provides quick and quantitative diagnosis results. CAD system for COVID-19 enables efficient diagnosis workflow and contributes to reduce such manpower shortage. In image-based diagnosis of viral pneumonia cases including COVID-19, both local and global image features are important because viral pneumonia cause many ground glass opacities and consolidations in large areas in the lung. This paper proposes an automated classification method of chest CT volumes for COVID-19 diagnosis assistance. MLP-Mixer is a recent method of image classification using Vision Transformer-like architecture. It performs classification using both local and global image features. To classify 3D CT volumes, we developed a hybrid classification model that consists of both a 3D convolutional neural network (CNN) and a 3D version of the MLP-Mixer. Classification accuracy of the proposed method was evaluated using a dataset that contains 1205 CT volumes and obtained 79.5% of classification accuracy. The accuracy was higher than that of conventional 3D CNN models consists of 3D CNN layers and simple MLP layers. © 2023 SPIE.

Deviation from standard cancer treatment during the first wave of the COVID-19 pandemic in India: A cross-sectional study

Background: During the coronavirus disease 2019 (COVID-19) pandemic, established best practices in cancer care were modified to diminish the risk of COVID-19 infection among patients and health-care workers. Objective(s): We aimed to study the modifications in cancer-directed therapy during the first wave of the COVID-19 pandemic. Material(s) and Method(s): A cross-sectional study of patients with cancers of the head and neck, thoracic, urologic, and central nervous systems who visited the medical oncology department of the Tata Memorial Hospital, Mumbai, India, between April 22, 2020 and June 01, 2020, was conducted. Data were prospectively collected in an online pro forma and supplemented from the electronic medical records. Result(s): Of a total of 514 patients, 363 (71%) were men. The most common malignancy was lung cancer in 234 patients (46%). Cancer-directed therapy was modified in 83 patients (16%). Deviations consisted of modification of the chemotherapy regimen (48%), temporary discontinuation of chemotherapy in 37%, and interim chemotherapy to delay surgery in 5%. Changes in the chemotherapy regimen included a shift to a less intensive regimen in 45%, changing from intravenous to oral in 40%, and less frequent dosing of immunotherapy in 7%. Considering missed appointments as a deviation from planned cancer therapy, 68% of patients had a deviation in the standard planned cancer care. Conclusion(s): Almost two-thirds of the patients could not reach the hospital during the COVID-19 pandemic lockdown in India. Of those who could reach the hospital, one of out every six patients with cancer had a change in their cancer-directed treatment, half of which consisted of a modification in the standard chemotherapy regimens. The effects of these therapy deviations are likely to be long-lasting. (Clinical Trials Registry-India, CTRI/2020/07/026533).Copyright © 2023 Neurology India, Neurological Society of India Published by Wolters Kluwer - Medknow.

Navigating the iodinated contrast media shortage: A review

The current COVID-19 climate has caused an unforeseen supply shortage of iodinated contrast media (ICM) worldwide, disrupting global distribution.1 In addition, the scarcity has resulted in a ripple effect in healthcare facilities such as radiology departments where ICM is required to perform contrast-enhanced examinations. ICM plays a significant part in contrast-enhanced CT, angiography and fluoroscopic procedures within the radiology department, holding a primary role in the differentiation and diagnosis of pathologies which range from pulmonary emboli to tumours.1 Its use extends beyond radiology, where ICM is heavily relied on in cardiology, urology and gastrointestinal studies, further highlighting the heavy dependence on the critical agent.2 With the global increase in the number of CT examinations requested, where approximately 60% of studies require ICM, optimal usage of ICM must be considered to meet heightened demand.3 The shortage has represented an opportunity for imaging providers to re-examine current imaging protocols and identify whether non-contrast imaging, alternative contrast agents and other imaging modalities could be viable options moving forward.1,2 Additionally, current literature has discussed volume-reduction strategies and dual-energy use in newer-generation CT scanners to conserve ICM.1,4 This review will explore currently proposed solutions that can be implemented in the radiology department to maximise ICM supply with minimal impact on patient care.

Acute aortic thrombosis in a patient with SARS-CoV-2 coronavirus disease (COVID-19).

One of the rare and life-threatening conditions is acute aortic thrombosis. We have described a case of thrombosis of the aorta and iliac arteries in a patient against the background of viral pneumonia COVID-19, with newly diagnosed diabetes mellitus and arterial hypertension.Copyright © 2021 The authors. All right reserved.

Comparative Analysis of Linear Regression and Logistic Regression for the COVID-19 Detection from CT-Scans

The new emerging Coronavirus disease (COVID-19) is a pandemic disease due to its enormous infectious capability. Generally affecting the lungs, COVID-19 engenders fever, dry cough, and tiredness. However, some patients may not show symptoms. An imaging test, such as a chest X-ray or a chest CT scan, is therefore requested for reliable detection of this pneumonia type. Despite the decreasing trends both in the new and death reported cases, there is an extent need for quick, accurate, and inexpensive new methods for diagnosis. In this framework, we propose two machine learning (ML) algorithms: linear regression and logistic regression for effective COVID-19 detection in the abdominal Computed Tomography (CT) dataset. The ML methods proposed in this paper, effectively classify the data into COVID-19 and normal classes without recourse to image preprocessing or analysis. The effectiveness of these algorithms was shown through the use of the performance measures: accuracy, precision, recall, and F1-score. The best classification accuracy was obtained as 96% with logistic regression using the saga solver with no added penalty against 95.3% with linear regression. As for precision, recall, and F1-score the value of 0.89 was reached by logistic regression for all these metrics, as well as the value of 0.87 by linear regression. © 2022 IEEE.

Isometric thumb extension exercise as part of a multimodal intervention for de Quervain's syndrome: A randomised feasibility trial

Introduction: de Quervain's syndrome is a painful condition commonly presented to hand therapists. Exercise is utilised as an intervention, but isometric exercise has not been investigated. We aimed to assess the feasibility and safety of isometric thumb extension exercise for de Quervain's syndrome and to explore differences between high-load and low-load isometric exercise. Method(s): This parallel-group randomised clinical feasibility trial included individuals with de Quervain's syndrome. All participants underwent a 2 week washout period where they received an orthosis, education, and range of motion exercises. Eligible participants were then randomised to receive high or low-load isometric thumb extension exercises, performed daily for 4 weeks. Feasibility and safety were assessed by recruitment and drop-out rates, adherence, adverse events, and participant feedback via semi-structured interviews. Secondary outcomes included patient-reported outcomes for pain and function, and blinded assessment of range of motion and strength. Result(s): Twenty-eight participants were randomised. There were no drop-outs after randomisation, and no serious adverse events. Adherence to exercise was 86.7%, with 84% of participants stating they would choose to participate again. There were clinically and statistically significant improvements in pain and function over time (p < 0.001) but not in range of motion or strength. There were no statistically significant between-group differences. Conclusion(s): Isometric thumb extension exercise within a multimodal approach appears a safe and feasible intervention for people with de Quervain's syndrome. A large multi-centre trial would be required to compare high- and low-load isometric exercises. Further research investigating exercise and multimodal interventions in this population is warranted.Copyright © The Author(s) 2023.