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The coronavirus disease 2019 (COVID-19) pandemic had a major impact on global health and was associated with millions of deaths worldwide. During the pandemic, imaging characteristics of chest X-ray (CXR) and chest computed tomography (CT) played an important role in the screening, diagnosis and monitoring the disease progression. Various studies suggested that quantitative image analysis methods including artificial intelligence and radiomics can greatly boost the value of imaging in the management of COVID-19. However, few studies have explored the use of longitudinal multi-modal medical images with varying visit intervals for outcome prediction in COVID-19 patients. This study aims to explore the potential of longitudinal multimodal radiomics in predicting the outcome of COVID-19 patients by integrating both CXR and CT images with variable visit intervals through deep learning. 2274 patients who underwent CXR and/or CT scans during disease progression were selected for this study. Of these, 946 patients were treated at the University of Pennsylvania Health System (UPHS) and the remaining 1328 patients were acquired at Stony Brook University (SBU) and curated by the Medical Imaging and Data Resource Center (MIDRC). 532 radiomic features were extracted with the Cancer Imaging Phenomics Toolkit (CaPTk) from the lung regions in CXR and CT images at all visits. We employed two commonly used deep learning algorithms to analyze the longitudinal multimodal features, and evaluated the prediction results based on the area under the receiver operating characteristic curve (AUC). Our models achieved testing AUC scores of 0.816 and 0.836, respectively, for the prediction of mortality. © 2023 SPIE.
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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.
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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.
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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.
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Objectives: The aim of this study was to assess Jordanian physicians' awareness about venous thromboembolism (VTE) risk among COVID-19 patients and its treatment protocol. Method(s): This was a cross-sectional-based survey that was conducted in Jordan in 2020. During the study period, a convenience sample of physicians working in various Jordanian hospitals were invited to participate in this study. Physicians' knowledge was evaluated and physicians gained one point for each correct answer. Then, a knowledge score out of 23 was calculated for each. Key Findings: In this study, 102 physicians were recruited. Results from this study showed that most of the physicians realize that all COVID-19 patients need VTE risk assessment (n = 69, 67.6%). Regarding VTE prophylaxis, the majority of physicians (n = 91, 89.2%) agreed that low molecular weight heparin (LMWH) is the best prophylactic option for mild-moderate COVID-19 patients with high VTE risk. Regarding severe/critically ill COVID-19 patients, 75.5% of physicians (n = 77) recognized that LMWH is the correct prophylactic option in this case, while 80.4% of them (n = 82) knew that mechanical prevention is the preferred prophylactic option for severe/critically ill COVID-19 patients with high bleeding risk. Moreover, 77.5% of physicians (n = 79) knew that LMWH is the treatment of choice for COVID-19 patients diagnosed with VTE. Finally, linear regression analysis showed that consultants had an overall higher knowledge score about VTE prevention and treatment in COVID-19 patients compared with residents (P = 0.009). Conclusion(s): All physicians knew about VTE risk factors for COVID-19 patients. However, consultants showed better awareness of VTE prophylaxis and treatment compared with residents. We recommend educational workshops be conducted to enhance physicians' knowledge and awareness about VTE thromboprophylaxis and management in COVID-19 patients.Copyright © 2022 The Author(s). Published by Oxford University Press on behalf of the Royal Pharmaceutical Society. All rights reserved.
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The World Health Organization has labeled the novel coronavirus illness (COVID-19) a pandemic since March 2020. It's a new viral infection with a respiratory tropism that could lead to atypical pneumonia. Thus, according to experts, early detection of the positive cases with people infected by the COVID-19 virus is highly needed. In this manner, patients will be segregated from other individuals, and the infection will not spread. As a result, developing early detection and diagnosis procedures to enable a speedy treatment process and stop the transmission of the virus has become a focus of research. Alternative early-screening approaches have become necessary due to the time-consuming nature of the current testing methodology such as Reverse transcription polymerase chain reaction (RT-PCR) test. The methods for detecting COVID-19 using deep learning (DL) algorithms using sound modality, which have become an active research area in recent years, have been thoroughly reviewed in this work. Although the majority of the newly proposed methods are based on medical images (i.e. X-ray and CT scans), we show in this comprehensive survey that the sound modality can be a good alternative to these methods, providing faster and easiest way to create a database with a high performance. We also present the most popular sound databases proposed for COVID-19 detection. © 2022 IEEE.
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Aim: Although most patients with COVID-19 experience respiratory tract infections, severe reactions to the virus may cause coagulation abnormalities that mimic other systemic coagulopathies associated with severe infections, such as disseminated intravascular coagulation and thrombotic microangiopathy. Fluctuations in platelet markers, which are an indicator of the acute phase response for COVID-19, are of clinical importance. The aim of this study is to evaluate the relationship between disease severity and Platelet Mass Index (MPI) parameters in COVID-19 patients. Material(s) and Method(s): This retrospective observational study was conducted with patients who were diagnosed with COVID-19 in a tertiary hospital. The study was continued with the remaining 280 patients. All laboratory data were scanned retrospectively from patient files and hospital information system. Result(s): A very high positive correlation was found between PMI and PLT. The PMI value in women was significantly higher than in men. It was observed that PMI did not differ significantly in terms of mortality, intubation, CPAP and comorbidity. PMI vs. Pneumonia Ct Severity Score, biochemistry parameters (AST, CRP), hemogram parameters (WBC, HGB, HCT, MCV, LYM, MPV EO) and coagulation factors (aPTT and FIB) at various levels of positive/negative, weak and strong, and significant relationship was found. There was no significant relationship between hormone and D-dimer when compared with PMI. Discussion(s): Although platelet count alone does not provide information about the prognosis of the disease, PMI may guide the clinician as an indicator of lung damage in seriously ill patients.Copyright © 2022, Derman Medical Publishing. All rights reserved.
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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.
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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.
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Submission content Introduction: An unusual case of a very young patient without previously known cardiac disease presenting with severe left ventricular failure, detected by a point of care echocardiogram. Main Body: A 34 year old previously well man was brought to hospital after seeing his general practitioner with one month of progressive shortness of breath on exertion. This began around the time the patient received his second covid-19 vaccination. He was sleeping in a chair as he was unable to lie flat. Abnormal observations led the GP to call an ambulance. In the emergency department, the patient required oxygen 5L/min to maintain SpO2 >94%, but he was not in respiratory distress at rest. Blood pressure was 92/53mmHg, mean 67mmHg. Point of care testing for COVID-19 was negative. He was alert, with warm peripheries. Lactate was 1.0mmol/L and he was producing more than 0.5ml/kg/hr of urine. There was no ankle swelling. ECG showed sinus tachycardia. He underwent CT pulmonary angiography which demonstrated no pulmonary embolus, but there was bilateral pulmonary edema. Troponin was 17ng/l, BNP was 2700pg/ml. Furosemide 40mg was given intravenously by the general medical team. Critical care outreach asked for an urgent intensivist review given the highly unusual diagnosis of pulmonary edema in a man of this age. An immediate FUSIC Heart scan identified a dilated left ventricle with end diastolic diameter 7cm and severe global systolic impairment. The right ventricle was not severely impaired, with TAPSE 18mm. There was no significant pericardial effusion. Multiple B lines and trace pulmonary effusions were identified at the lung bases. The patient was urgently discussed with the regional cardiac unit in case of further deterioration, basic images were shared via a cloud system. A potential diagnosis of vaccination-associated myocarditis was considered,1 but in view of the low troponin, the presentation was felt most likely to represent decompensated chronic dilated cardiomyopathy. The patient disclosed a family history of early cardiac death in males. Aggressive diuresis was commenced. The patient was admitted to a monitored bed given the potential risk of arrhythmia or further haemodynamic deterioration. Advice was given that in the event of worsening hypotension, fluids should not be administered but the cardiac centre should be contacted immediately. Formal echocardiography confirmed the POCUS findings, with ejection fraction <35%. He was initiated on ACE inhibitors and beta adrenergic blockade. His symptoms improved and he was able to return home and to work, and is currently undergoing further investigations to establish the etiology of his condition. Conclusion(s): Early echocardiography provided early evidence of a cardiac cause for the patient's presentation and highlighted the severity of the underlying pathology. This directed early aggressive diuresis and safety-netting by virtue of discussion with a tertiary cardiac centre whilst it was established whether this was an acute or decompensated chronic pathology. Ultrasound findings: PLAX, PSAX and A4Ch views demonstrating a severely dilated (7cm end diastolic diameter) left ventricle with global severe systolic impairment.
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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.
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Smoking is a significant social problem threatening the population's health, especially during the coronavirus pandemic. Due to the problem's urgency, we present a clinical case of SARS-CoV-2 infection in a patient with 10 years of smoking and concomitant chronic obstructive pulmonary disease (chronic bronchitis and peribronchial pneumosclerosis). Patient L.K., 42 years old, on 13.10.2022, was hospitalized for several hours at the Emergency Hospital of the Ministry of Health of Chuvashia (Cheboksary) with a severe new coronavirus infection. Secondary diagnosis: Chronic obstructive pulmonary disease Case history: for about two to three weeks, the patient noted an increase in body temperature to 37.2-37.4 degreeC and a cough. He has smoked for about 10 years, 1 pack per day. Computed tomography showed signs of bilateral COVID-associated pneumonitis, alveolitis with 85% involvement and consolidation sites, signs of chronic bronchitis, and peribronchial pneumosclerosis. The diagnosis of COVID-19 was confirmed by a polymerase chain reaction in a nasopharyngeal smear. The NEWS2 score was 9. After the treatment started, the patient died. Histological examination showed perivascular sclerosis, peribronchial pneumosclerosis, atrophic changes in the ciliated epithelium, and structural and functional alteration of the bronchial mucosa. In addition, areas of hemorrhage and inflammatory infiltrate in the bronchial wall were found. Coronavirus is known not to cause bronchitis but bronchiolitis. In the presented case, the patient showed signs of transition of bronchitis to the acute stage. Therefore, it can be assumed that the coronavirus acts as a complicating factor. In addition to the described changes, signs of viral interstitial pneumonia, pulmonary edema, and early development of acute respiratory distress syndrome were identified.Copyright © 2023, Media Sphera Publishing Group. All rights reserved.
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This paper proposes a deep learning-based approach to detect COVID-19 infections in lung tissues from chest Computed Tomography (CT) images. A two-stage classification model is designed to identify the infection from CT scans of COVID-19 and Community Acquired Pneumonia (CAP) patients. The proposed neural model named, Residual C-NiN uses a modified convolutional neural network (CNN) with residual connections and a Network-in-Network (NiN) architecture for COVID-19 and CAP detection. The model is trained with the Signal Processing Grand Challenge (SPGC) 2021 COVID dataset. The proposed neural model achieves a slice-level classification accuracy of 93.54% on chest CT images and patient-level classification accuracy of 86.59% with class-wise sensitivity of 92.72%, 55.55%, and 95.83% for COVID-19, CAP, and Normal classes, respectively. Experimental results show the benefit of adding NiN and residual connections in the proposed neural architecture. Experiments conducted on the dataset show significant improvement over the existing state-of-the-art methods reported in the literature. © 2022 ACM.
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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
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This paper introduces the improved method for the COVID-19 classification based on computed tomography (CT) volumes using a combination of a complex-architecture convolutional neural network (CNN) and orthogonal ensemble networks (OEN). The novel coronavirus disease reported in 2019 (COVID-19) is still spreading worldwide. Early and accurate diagnosis of COVID-19 is required in such a situation, and the CT scan is an essential examination. Various computer-aided diagnosis (CAD) methods have been developed to assist and accelerate doctors' diagnoses. Although one of the effective methods is ensemble learning, existing methods combine some major models which do not specialize in COVID-19. In this study, we attempted to improve the performance of a CNN for the COVID-19 classification based on chest CT volumes. The CNN model specializes in feature extraction from anisotropic chest CT volumes. We adopt the OEN, an ensemble learning method considering inter-model diversity, to boost its feature extraction ability. For the experiment, We used chest CT volumes of 1283 cases acquired in multiple medical institutions in Japan. The classification result on 257 test cases indicated that the combination could improve the classification performance. © 2023 SPIE.
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Disparities in the incidence, prevalence, and morbidity and mortality rates of many respiratory diseases are evident among ethnic groups. Biological, cultural and environmental factors related to ethnicity can all contribute to the differences in respiratory health observed among ethnic minority groups, but the inequalities observed are most commonly due to lower socioeconomic position. People who migrate within a country or across an international border may experience an improvement in respiratory health associated with improvements in socioeconomic position. However, migrants may also experience worse health outcomes in destination countries, as they are faced by barriers in language and culture, discrimination, exclusion and limited access to health services. While some high-quality studies investigating ethnicity and respiratory health are available, further research into ethnic differences is needed. Improving the recording of ethnicity in health records, addressing barriers to accessing respiratory healthcare and improving cultural literacy more generally are some of the ways that inequalities can be tackled.Copyright © ERS 2023.
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Objectives: The purpose of this study was to evaluate the laboratory measurements and thorax computed tomography (CT) findings of pregnant women with COVID-19. Methods: This was a single-center, observational study performed in a Training and Research Hospital from March 1 to May 31, 2020. Laboratory data, clinical conditions, and thorax CT images of pregnant women with COVID-19 were analyzed retrospectively. The patients who agreed to the image and were not suspected of pneumonia were classified according to their degree of lung involvement. Results: A total of 155 pregnant women have included in the study, and the thorax CT of 86 (55.5%) pregnant women who participated in the study was evaluated. While no symptoms were observed in 44 (28.4%) of the pregnant women, the most common symptoms were dyspnea and cough in 27 (17.4%). Of the pregnant women evaluated for thorax CT, 24 (27.9%) had negative, 19 (21.1%) had mild involvement, 30 (34.9%) had moderate involvement, and 13 (15.1%) had heavy involvement. C-reactive protein (CRP) levels of pregnant women with negative tomography were 4.5 (0.7–83.4) mg/L, 13.4 (0.7–107.3) mg/L with mild involvement, 37.7 (3.8–292.6) mg/L with moderate involvement and, 48.6 (5.7–234.1) mg/L with heavy involvement (p<0.001). Conclusion: All factors affecting the prognosis for pregnant women with COVID-19 have not been fully elucidated. It was determined that a significant frequency of pregnant women was asymptomatic. In addition, an increase was observed in the CRP level according to the severity of pneumonia, while no similar difference was found in the D-dimer level. ©Copyright 2023 by Anatolian Journal of Family Medicine.
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Medical Imaging and Data Resource Center (MIDRC) has been built to support AI-based research in response to the COVID-19 pandemic. One of the main goals of MIDRC is to make data collected in the repository ready for AI analysis. Due to data heterogeneity, there is a need to standardize data and make data-mining easier. Our study aims to stratify imaging data according to underlying anatomy using open-source image processing tools. The experiments were performed using Google Colaboratory on computed tomography (CT) imaging data available from the MIDRC. We adopted the existing open-source tools to process CT series (N=389) to define the image sub-volumes according to body part classification, and additionally identified series slices containing specific anatomic landmarks. Cases with automatically identified chest regions (N=369) were then processed to automatically segment the lungs. In order to assess the accuracy of segmentation, we performed outlier analysis using 3D shape radiomics features extracted from the left and right lungs. Standardized DICOM objects were created to store the resulting segmentations, regions, landmarks and radiomics features. We demonstrated that the MIDRC chest CT collections can be enriched using open-source analysis tools and that data available in MIDRC can be further used to evaluate the robustness of publicly available tools. © 2023 SPIE.
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Background: Women are most at risk for Clostridium difficile infection in the early postpartum period. Clostridium difficile-associated colitis may be mistaken for the intestinal form of COVID-19 during the ongoing novel coronavirus infection pandemic. Case report: The paper describes a clinical case of a female patient diagnosed with the novel coronavirus infection and Clostridium difficile-associated pseudomembranous colitis in the early postpartum period. It depicts the diagnosis and treatment of the identified concurrent pathology. It demonstrates data from of an endoscopic examination of the colon and spiral computed tomography of the chest and provides laboratory confirmation of the infectious etiology of comorbidity. There are data available in the literature on the high rate and recurrent course of pseudomembranous colitis in the early postpartum period. It is noted that timely C. difficile eradication and pathogenetic treatment for the novel coronavirus infection allow relief of clinical symptoms. Conclusion(s): The case of the novel coronavirus infection concurrent with Clostridium difficile-associated pseudomembranous colitis in the early postpartum period is of interest in connection with the need for differential diagnosis of the etiology of diarrheal syndrome, the precise identification of which determines the further tactics of patient management and the nature of anti-epidemic measures.Copyright © A group of authors, 2021.