Coronavirus disease-19: The First 7,755 Cases in the Republic of Korea

Choe, Young June

doi:10.1101/2020.03.15.20036368

Cited by 6 publications

(1 citation statement)

References 4 publications

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“…The coronavirus disease 2019 (COVID-19) epidemic resulted from a novel strain of coronavirus that had not been previously diagnosed in humans and was first discovered in late December 2019; since then, it has spread rapidly, infecting over 410 million individuals globally, killing over 5.8 million people as of 13 February 2022 [1,2]. The gold standard for identifying COVID-19 is now Reverse Transcriptase Quantitative Polymerase Chain Reaction (RTq-PCR) tests [3,4]. Small quantities of viral RNA are collected from a nasal twirling, increased in size, and measured during this test, with virus confirmation displayed visually with a fluorescent dye.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network

Monday

Nneji

et al. 2022

Diagnostics

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Chest X-ray (CXR) is becoming a useful method in the evaluation of coronavirus disease 19 (COVID-19). Despite the global spread of COVID-19, utilizing a computer-aided diagnosis approach for COVID-19 classification based on CXR images could significantly reduce the clinician burden. There is no doubt that low resolution, noise and irrelevant annotations in chest X-ray images are a major constraint to the performance of AI-based COVID-19 diagnosis. While a few studies have made huge progress, they underestimate these bottlenecks. In this study, we propose a super-resolution-based Siamese wavelet multi-resolution convolutional neural network called COVID-SRWCNN for COVID-19 classification using chest X-ray images. Concretely, we first reconstruct high-resolution (HR) counterparts from low-resolution (LR) CXR images in order to enhance the quality of the dataset for improved performance of our model by proposing a novel enhanced fast super-resolution convolutional neural network (EFSRCNN) to capture texture details in each given chest X-ray image. Exploiting a mutual learning approach, the HR images are passed to the proposed Siamese wavelet multi-resolution convolutional neural network to learn the high-level features for COVID-19 classification. We validate the proposed COVID-SRWCNN model on public-source datasets, achieving accuracy of 98.98%. Our screening technique achieves 98.96% AUC, 99.78% sensitivity, 98.53% precision, and 98.86% specificity. Owing to the fact that COVID-19 chest X-ray datasets are low in quality, experimental results show that our proposed algorithm obtains up-to-date performance that is useful for COVID-19 screening.

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Section: Introductionmentioning

confidence: 99%

COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network

Monday

Nneji

et al. 2022

Diagnostics

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Interpreting Sars-Cov-2 Fatality Rate Estimates — A Case for Introducing Standardized Reporting to Improve Communication

Cavataio

Schnell

2020

SSRN Journal

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Epidemiology, Clinical Features and Prognostic Factors of Pediatric SARS-CoV-2 Infection: Results from an Italian Multicenter Study

Garazzino,

Lo Vecchio,

Pierantoni

et al. 2020

SSRN Journal

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Background: Many aspects of SARS-CoV-2 infection in children and adolescents remain unclear and optimal treatment is debated. The objective of our study was to investigate epidemiological, clinical and therapeutic characteristics of pediatric SARS-CoV-2 infection, focusing on risk factors for complicated and critical disease. Methods:The present multicenter Italian study was promoted by the Italian Society of Pediatric Infectious Diseases, involving both pediatric hospitals and general pediatricians/family doctors. All subjects under 18 years of age with documented SARS-CoV-2 infection and referred to the coordinating center were enrolled from March 2020.Results: As of 15 September 2020, 759 children were enrolled (median age 7.2 years, IQR 1.4; 12.4). Among the 688 symptomatic children, fever was the most common symptom (81.9%). Barely 47% of children were hospitalized for COVID-19. Age was inversely related to hospital admission (p < 0.01) and linearly to length of stay (p = 0.014). One hundred forty-nine children (19.6%) developed complications. Comorbidities were risk factors for complications (p < 0.001). Viral coinfections, underlying clinical conditions, age 5-9 years and lymphopenia were statistically related to ICU admission (p < 0.05).Garazzino et al. SARS-CoV-2 in Children and AdolescentsConclusions: Complications of COVID-19 in children are related to comorbidities and increase with age. Viral co-infections are additional risk factors for disease progression and multisystem inflammatory syndrome temporarily related to COVID-19 (MIS-C) for ICU admission.

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Coronavirus disease-19: The First 7,755 Cases in the Republic of Korea

Cited by 6 publications

References 4 publications

COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network

COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network

Interpreting Sars-Cov-2 Fatality Rate Estimates — A Case for Introducing Standardized Reporting to Improve Communication

Epidemiology, Clinical Features and Prognostic Factors of Pediatric SARS-CoV-2 Infection: Results from an Italian Multicenter Study

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