ABSTRACT
Pericardial cysts are rare congenital anomalies, often clinically silent and incidentally found on imaging. However, patients with pericardial cysts may present with chest pain, tachypnea, and, rarely, symptoms secondary to cardiac tamponade. Echocardiography (transthoracic or transesophageal) and chest computed tomography (CT) scan with contrast are diagnostic modalities of choice in patients with pericardial cysts. Conservative management is justified in asymptomatic patients, while a surgical approach is recommended in symptomatic patients. Here, we describe the case of a 12-year-old boy who underwent imaging during the coronavirus disease 2019 (COVID-19) pandemic and was incidentally found to have a pericardial cyst.
ABSTRACT
Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.
ABSTRACT
COVID-19 is now an established morbidity across races, regions and clinical risks around the world. From its first detection in Wuhan city-China in 2019 to the recent breakthrough of approved vaccines, that are determinants and deterrents and gradually becoming apparent. The phenotype of its presentation however is both variable and challenging especially. For those presenting with unique skin dermatosis such as erythema multiforme. Case report Our case is on a 36 year- old gentleman who presented to the hospital complaining, initially of only urticarial rash (later established to be erythema multiform), which improved with symptomatic treatment. He was discharged, only to be re-admitted a week later with exacerbation of the former cutaneous manifestation, accompanied by fever and gastrointestinal symptoms. He ultimately made complete recovery and was discharged home.
ABSTRACT
Chest X-ray (CXR) imaging is a low-cost, easy-to-use imaging alternative that can be used to diagnose/screen pulmonary abnormalities due to infectious diseaseX: Covid-19, Pneumonia and Tuberculosis (TB). Not limited to binary decisions (with respect to healthy cases) that are reported in the state-of-the-art literature, we also consider non-healthy CXR screening using a lightweight deep neural network (DNN) with a reduced number of epochs and parameters. On three diverse publicly accessible and fully categorized datasets, for non-healthy versus healthy CXR screening, the proposed DNN produced the following accuracies: 99.87% on Covid-19 versus healthy, 99.55% on Pneumonia versus healthy, and 99.76% on TB versus healthy datasets. On the other hand, when considering non-healthy CXR screening, we received the following accuracies: 98.89% on Covid-19 versus Pneumonia, 98.99% on Covid-19 versus TB, and 100% on Pneumonia versus TB. To further precisely analyze how well the proposed DNN worked, we considered well-known DNNs such as ResNet50, ResNet152V2, MobileNetV2, and InceptionV3. Our results are comparable with the current state-of-the-art, and as the proposed CNN is light, it could potentially be used for mass screening in resource-constraint regions.
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BACKGROUND: Since the onset of the COVID-19 pandemic, the world witnessed disruption on an unprecedented scale affecting our daily lives including but not limited to healthcare, business, education, and transportation. Deep Learning (DL) is a branch of Artificial intelligence (AI) applications, the recent growth of DL includes features that could be helpful in fighting the COVID-19 pandemic. Utilizing such features could support public health efforts. OBJECTIVE: Investigate the literature available in the use of DL technology to support dealing with the COVID-19 crisis. We summarize the literature that uses DL features to analyze datasets for the purpose of a quick COVID-19 detection. METHODS: This review follows PRISMA Extension for Scoping Reviews (PRISMA-ScR). We have scanned the most two commonly used databases (IEEE, ACM). Search terms were identified based on the target intervention (DL) and the target population (COVID-19). Two authors independently handled study selection and one author assigned for data extraction. A narrative approach is used to synthesize the extracted data. RESULTS: We retrieved 53 studies and after passing through PRISMA excluding criteria, only 17 studies are considered in this review. All studies used deep learning for detection of COVID-19 cases in early stage based on different diagnostic modalities. Convolutional Neural Network (CNN) and Transfer Learning (TL) were the most commonly used techniques. CONCLUSION: The included studies showed that DL techniques has significant impact on early detection of COVID-19 with high accuracy rate. However, most of the proposed methods are still in development and not tested in a clinical setting. Further investigation and collaboration are required from the research community and healthcare professionals in order to develop and standardize guidelines for use of DL in the healthcare domain.
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Currently, there are no approved specific antiviral agents for novel coronavirus disease 2019 (COVID-19). Convalescent plasma has not yet been approved for use in patients with COVID-19 infection; however, it is regulated as an investigational product. This is a case report of a 55-year-old male, with COVID-19 pneumonia who has received convalescent plasma as part of a treatment plan which showed significant radiological and clinical improvement post-treatment.
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BACKGROUND: This report and literature review describes a case of a COVID-19 patient who suffered a cerebellar stroke requiring neurosurgical decompression. This is the first reported case of a sub-occipital craniectomy with brain biopsy in a COVID-19 patient showing leptomeningeal venous intimal inflammation. CLINICAL DESCRIPTION: The patient is a 48-year-old SARS-COV-2 positive male with multiple comorbidities, who presented with fevers and respiratory symptoms, and imaging consistent with multifocal pneumonia. On day 5 of admission, the patient had sudden change in mental status, increased C-Reactive Protein, ferritin and elevated Interleukin-6 levels. Head CT showed cerebral infarction from vertebral artery occlusion. Given subsequent rapid neurologic decline from cerebellar swelling and mass effect on his brainstem emergent neurosurgical intervention was performed. Brain biopsy found a vein with small organizing thrombus adjacent to focally proliferative intima with focal intimal neutrophils. CONCLUSION: A young man with COVID-19 and suspected immune dysregulation, complicated by a large cerebrovascular ischemic stroke secondary to vertebral artery thrombosis requiring emergent neurosurgical intervention for decompression with improved neurological outcomes. Brain biopsy was suggestive of inflammation from thrombosed vessel, and neutrophilic infiltration of cerebellar tissue.