Chest radiography or computed tomography for COVID-19 pneumonia? Comparative study in a simulated triage setting

Sverzellati, Nicola; Ryerson, Christopher J.; Milanese, Gianluca; Renzoni, Elisabetta; Volpi, Annalisa; Spagnolo, Paolo; Bonella, Francesco; Comelli, Ivan; Affanni, Paola; Veronesi, Licia; Manna, Carmelinda; Ciuni, Andrea; Sartorio, Carlotta; Tringali, Giulia; Silva, Mario; Michieletti, Emanuele; Colombi, Davide; Wells, Adrian

doi:10.1183/13993003.04188-2020

Cited by 55 publications

(43 citation statements)

References 17 publications

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“…The sensitivity for diagnosing COVID-19 from reconstructed CXR from high-resolution CT images for 300 patients is 81.6%. This value is 95.2% for high-resolution CT images in the same study [ 63 ]. The sensitivity value in Cozzi et al [ 64 ] is 68.1% for CXR images of 234 COVID-19 patients.…”

Section: The Role Of Imaging In Covid-19 Diagnosismentioning

confidence: 69%

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

Nabavi

Ejmalian

Moghaddam

et al. 2021

Computers in Biology and Medicine

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Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.

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Section: The Role Of Imaging In Covid-19 Diagnosismentioning

confidence: 69%

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

Nabavi

Ejmalian

Moghaddam

et al. 2021

Computers in Biology and Medicine

View full text Add to dashboard Cite

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“…Chest CT has higher sensitivity for COVID-19 than reverse transcriptase-PCR 20. Also, chest CT is recommended in some reports to estimate the extent of pneumonia in patients with suspected COVID-19 pneumonia on chest X-ray [ 35 ]. In our study, all asymptomatic patients had normal findings on initial chest radiographs, and two asymptomatic patients (15.38%) had pneumonia on initial chest CT scans.…”

Section: Discussionmentioning

confidence: 99%

Comparison of the Characteristics of Asymptomatic and Presymptomatic Patients with Coronavirus Disease 2019 in the Republic of Korea

Hyun

Lee

Park

et al. 2021

J Epidemiol Glob Health

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Purpose This retrospective study aimed to evaluate the baseline characteristics of asymptomatic patients with coronavirus disease 2019 at admission and to follow-up their clinical manifestations and radiological findings during hospitalization. Methods Patients with coronavirus disease 2019 who were asymptomatic at admission were divided into two groups—those with no symptoms until discharge (group A) and those who developed symptoms after admission (group B). Patients who could not express their own symptoms were excluded. Results Overall, 127 patients were enrolled in the study, of whom 19 and 108 were assigned to groups A and B, respectively. The mean age and median C-reactive protein level were higher in group B than in group A. All patients in group A and one-third of patients in group B had normal initial chest radiographs; 15.8% and 48.1% of patients in groups A and B, respectively, had pneumonia during hospitalization. One patient in group B, whose condition was not severe at the time of admission, deteriorated due to aggravated pneumonia and was transferred to a tertiary hospital. Conclusion We summarize the clinical characteristics during hospitalization of patients with coronavirus disease 2019 who were purely asymptomatic at the time of admission. The majority of asymptomatic patients with coronavirus disease 2019 were discharged without significant events during hospitalization. However, it may be difficult to predict subsequent events from initial chest radiographs or oxygen saturation at admission.

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“…Chest X-ray is used to monitor the patient’s lung condition including progression of disease or even any wound relating to accident. Considering the times of COVID-19, chest X-ray have shown promising results in contrast to CT-scan images ( Sverzellati et al., 2021 ). Furthermore, because of the domain’s rapid expansion, researchers are becoming increasingly oblivious of progress across diverse approaches, and therefore awareness across algorithms is dwindling.…”

Section: Related Workmentioning

confidence: 99%

Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging

Chauhan

Palivela²,

Tiwari

2021

International Journal of Information Management Data Insights

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It’s been more than a year that the entire world is fighting against Covid-19 pandemic. Starting from the Wuhan city in China, COVID-19 has conquered the entire world with its rapid progression. But seeking the importance towards the human situation, it has become essential to build such an automated model to diagnose COVID-19 within less computational time easily. As the disease has spread, there is not enough data to implement an accurate COVID-19 predicting model. But technology is a boon, which makes it possible. Effective techniques based on medical imaging using artificial intelligence have approached to assist humans in needful time. It has become very essential to detect COVID-19 in humans at an early stage to prevent it from becoming more infectious. The neural networks have shown promising results in medical imaging. In this research, a deep learning-based approach is used for image classification to detect COVID-19 using chest X-ray images (CXR). A CNN classifier have been used to classify the normal-healthy images from the COVID-19 images, using transfer learning. The concept of early stopping is used to enhance the accuracy of the proposed DenseNet model. The results of the system have been evaluated using accuracy, precision, recall and F1-score metrics. An automated comparative analysis among multiple optimizers, LR Scheduler and Loss Function is performed to get the highest accuracy suitable for the proposed system. The Adamax optimizer with Cross Entropy loss function and StepLR scheduler have outperformed with 98.45% accuracy for normal-healthy CXR images and 98.32% accuracy for COVID-19 images.

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Chest radiography or computed tomography for COVID-19 pneumonia? Comparative study in a simulated triage setting

Cited by 55 publications

References 17 publications

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

Comparison of the Characteristics of Asymptomatic and Presymptomatic Patients with Coronavirus Disease 2019 in the Republic of Korea

Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging

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