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The Role of CT in 2019 Novel Coronavirus Pneumonia (COVID-19)

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (25 December 2022) | Viewed by 9010

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Prof. Dr. Jinshan Tang

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Department of Health Administration and Policy, George Mason University, Fairfax, VA 22030, USA
Interests: AI in medicine; medical image analysis; medical imaging
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Dear Colleagues,

The spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a public health crisis known as the COVID-19 pandemic. The COVID-19 pandemic continues to threaten the world due to its high infectivity and extreme lethality. There are more than 278 million cases, with nearly 5.39 million deaths globally. Although it is difficult to precisely compute economic losses caused by the COVID-19 pandemic, almost all countries are experiencing severe financial damage.

CT imaging plays a key role in fighting against the COVID-19 pandemic. It has been widely used for the detection and diagnosis of COVID-19 diseases and the treatment of COVID-19 patients.

This Special Issue aims to recruit high-quality articles that apply CT imaging to fight against the COVID-19 pandemic. Topics include the following:

Detection and diagnosis of COVID-19 with CT imaging;
Prediction of treatment effectiveness of COVID-19 disease with CT imaging;
Intelligent diagnosis and detection systems and technologies for COVID-19 with CT imaging;
Post-COVID-19 disease detection, diagnosis, and treatment with CT imaging.

Prof. Dr. Jinshan Tang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

12 pages, 2164 KiB

Open AccessArticle

Lung Ultrasonography Is an Acceptable Imaging Modality to Diagnose COVID-19 and Effectively Correlates with HRCT Chest—A Prospective Study

by Muiez Bashir, Wani Inzamam, Mohd Kamran Banday, Sheikh Riaz Rasool, Mudasir Hamid Bhat, Carmen Vladulescu, Fahad A. Al-Misned and Hamed A. El-Serehy

Diagnostics 2023, 13(12), 2091; https://doi.org/10.3390/diagnostics13122091 - 16 Jun 2023

Cited by 2 | Viewed by 1285

Abstract

It has been validated beyond doubt that High-Resolution Computed Tomography (HRCT) chest and to some extent chest radiographs have a role in corona virus disease-19 (COVID-19). Much less is known about the role of lung ultrasonography (LUS) in COVID-19. In this paper, our main purpose was to gauge the relationship between LUS and chest HRCT in reverse transcriptase polymerase chain reaction (RT–PCR) documented cases of COVID-19, as well as in those with high suspicion of COVID-19 with negative RT–PCR. It was a prospective study carried out at our tertiary care hospital, namely, SKIMS Soura. The total number of patients in this study were 152 (200 patients were selected out of which only 152 had undergone both LUS and chest HRCT). The patients were subjected to both LUS and chest HRCT. The radiologist who performed LUS was blinded to clinical findings and HRCT was evaluated by a radiologist with about a decade of experience. The LUS findings compatible with the disease were subpleural consolidations, B-lines and irregular pleural lines. Findings that were compatible with COVID-19 on chest HRCT were bibasilar, subpleural predominant ground glass opacities, crazy paving and consolidations. COVID-19-positive patients were taken up for chest HRCT for disease severity stratification and were also subjected to LUS. On HRCT chest, the imaging abnormalities compatible with COVID-19 were evident in 110 individuals (72.37%), and on Lung Ultrasound they were observed in 120 individuals (78.95%). Imaging of COVID-19 patients assessed by both LUS and HRCT chest,, showed a positive correlation (p < 0.0001). The study revealed a sensitivity of 88%, a specificity of 76.62%, a positive predictive value of 78.57% and a negative predictive value of 86.76%. None of the individuals with a diagnosis of COVID-19 on HRCT were missed on LUS. An excellent correlation was derived between the LUS score and CT total severity score (p < 0.0001 with a kappa of 0.431). Similar precision compared with chest HRCT in the detection of chest flaws in COVID-19 patients was obtained on LUS. Full article

(This article belongs to the Special Issue The Role of CT in 2019 Novel Coronavirus Pneumonia (COVID-19))

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11 pages, 378 KiB

Open AccessArticle

Dose Descriptors and Assessment of Risk of Exposure-Induced Death in Patients Undergoing COVID-19 Related Chest Computed Tomography

by Lejla M. Čiva, Adnan Beganović, Mustafa Busuladžić, Merim Jusufbegović, Ta’a Awad-Dedić and Sandra Vegar-Zubović

Diagnostics 2022, 12(8), 2012; https://doi.org/10.3390/diagnostics12082012 - 19 Aug 2022

Cited by 4 | Viewed by 2506

Abstract

For more than two years, coronavirus disease 19 (COVID-19) has represented a threat to global health and lifestyles. Computed tomography (CT) imaging provides useful information in patients with COVID-19 pneumonia. However, this diagnostic modality is based on exposure to ionizing radiation, which is associated with an increased risk of radiation-induced cancer. In this study, we evaluated the common dose descriptors, CTDI_vol and DLP, for 1180 adult patients. This data was used to estimate the effective dose, and risk of exposure-induced death (REID). Awareness of the extensive use of CT as a diagnostic tool in the management of COVID-19 during the pandemic is vital for the evaluation of radiation exposure parameters, dose reduction methods development and radiation protection. Full article

(This article belongs to the Special Issue The Role of CT in 2019 Novel Coronavirus Pneumonia (COVID-19))

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17 pages, 2275 KiB

Open AccessArticle

COVID-19 Infection Segmentation and Severity Assessment Using a Self-Supervised Learning Approach

by Yao Song, Jun Liu, Xinghua Liu and Jinshan Tang

Diagnostics 2022, 12(8), 1805; https://doi.org/10.3390/diagnostics12081805 - 26 Jul 2022

Cited by 10 | Viewed by 1840

Abstract

Background: Automated segmentation of COVID-19 infection lesions and the assessment of the severity of the infections are critical in COVID-19 diagnosis and treatment. Based on a large amount of annotated data, deep learning approaches have been widely used in COVID-19 medical image analysis. However, the number of medical image samples is generally huge, and it is challenging to obtain enough annotated medical images for training a deep CNN model. Methods: To address these challenges, we propose a novel self-supervised deep learning method for automated segmentation of COVID-19 infection lesions and assessing the severity of infection, which can reduce the dependence on the annotation of the training samples. In the proposed method, first, many unlabeled data are used to pre-train an encoder-decoder model to learn rotation-dependent and rotation-invariant features. Then, a small amount of labeled data is used to fine-tune the pre-trained encoder-decoder for COVID-19 severity classification and lesion segmentation. Results: The proposed methods were tested on two public COVID-19 CT datasets and one self-built dataset. Accuracy, precision, recall, and F1-score were used to measure classification performance and Dice coefficient was used to measure segmentation performance. For COVID-19 severity classification, the proposed method outperformed other unsupervised feature learning methods by about 7.16% in accuracy. For segmentation, when the amount of labeled data was 100%, the Dice value of the proposed method was 5.58% higher than that of U-Net.; in 70% of the cases, our method was 8.02% higher than U-Net; in 30% of the cases, our method was 11.88% higher than U-Net; and in 10% of the cases, our method was 16.88% higher than U-Net. Conclusions: The proposed method provides better classification and segmentation performance under limited labeled data than other methods. Full article

(This article belongs to the Special Issue The Role of CT in 2019 Novel Coronavirus Pneumonia (COVID-19))

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16 pages, 21886 KiB

Open AccessArticle

COVI³D: Automatic COVID-19 CT Image-Based Classification and Visualization Platform Utilizing Virtual and Augmented Reality Technologies

by Samir Benbelkacem, Adel Oulefki, Sos Agaian, Nadia Zenati-Henda, Thaweesak Trongtirakul, Djamel Aouam, Mostefa Masmoudi and Mohamed Zemmouri

Diagnostics 2022, 12(3), 649; https://doi.org/10.3390/diagnostics12030649 - 07 Mar 2022

Cited by 7 | Viewed by 2127

Abstract

Recently many studies have shown the effectiveness of using augmented reality (AR) and virtual reality (VR) in biomedical image analysis. However, they are not automating the COVID level classification process. Additionally, even with the high potential of CT scan imagery to contribute to research and clinical use of COVID-19 (including two common tasks in lung image analysis: segmentation and classification of infection regions), publicly available data-sets are still a missing part in the system care for Algerian patients. This article proposes designing an automatic VR and AR platform for the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic data analysis, classification, and visualization to address the above-mentioned challenges including (1) utilizing a novel automatic CT image segmentation and localization system to deliver critical information about the shapes and volumes of infected lungs, (2) elaborating volume measurements and lung voxel-based classification procedure, and (3) developing an AR and VR user-friendly three-dimensional interface. It also centered on developing patient questionings and medical staff qualitative feedback, which led to advances in scalability and higher levels of engagement/evaluations. The extensive computer simulations on CT image classification show a better efficiency against the state-of-the-art methods using a COVID-19 dataset of 500 Algerian patients. The developed system has been used by medical professionals for better and faster diagnosis of the disease and providing an effective treatment plan more accurately by using real-time data and patient information. Full article

(This article belongs to the Special Issue The Role of CT in 2019 Novel Coronavirus Pneumonia (COVID-19))

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