Literature DB >> 33560673

The role of computed tomography scan in the diagnosis of COVID-19 pneumonia.

Ariana Axiaq1, Ahmad Almohtadi2, Samuel A Massias2, Dorette Ngemoh2, Amer Harky3,4.   

Abstract

PURPOSE OF REVIEW: To summarize current literature evidence on the role of computed tomography (CT) scan in the diagnosis and assessment of coronavirus disease 2019 (COVID-19) pneumonia. RECENT
FINDINGS: Recent guidelines on the use of CT scans in COVID-19 vary between countries. However, the consensus is that it should not be used as the first line; a notion supported by the WHO. Currently, several investigations are being used including reverse transcription PCR testing, chest radiographs, and ultrasound scans, and CT scans. They are ideally performed later during the disease process as the sensitivity and specificity are highest by that time. Typical COVID-19 features on CT scans vary but include vascular enlargement, ground-glass opacities, and ground glass opacification together with consolidation.
SUMMARY: Since COVID-19 was declared as a global pandemic, there was a push towards identifying appropriate diagnostic tests that are both reliable and effective. There is a general agreement that CT scans have a high sensitivity but low specificity in diagnosing COVID-19. However, the quality of available studies is not optimal, so this must always be interpreted with the clinical context in mind. Clinicians must aim to weigh up the practicalities and drawbacks of CT scans when considering their use for a patient. The ease and speed of use of CT scans must be balanced with their high radiation doses, and infection control considerations.
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

Entities:  

Mesh:

Year:  2021        PMID: 33560673     DOI: 10.1097/MCP.0000000000000765

Source DB:  PubMed          Journal:  Curr Opin Pulm Med        ISSN: 1070-5287            Impact factor:   3.155


  6 in total

1.  Exploiting probability density function of deep convolutional autoencoders' latent space for reliable COVID-19 detection on CT scans.

Authors:  Sima Sarv Ahrabi; Lorenzo Piazzo; Alireza Momenzadeh; Michele Scarpiniti; Enzo Baccarelli
Journal:  J Supercomput       Date:  2022-02-24       Impact factor: 2.557

2.  Statistical analysis of COVID-19 infection severity in lung lobes from chest CT.

Authors:  Mehdi Yousefzadeh; Mozhdeh Zolghadri; Masoud Hasanpour; Fatemeh Salimi; Ramezan Jafari; Mehran Vaziri Bozorg; Sara Haseli; Abolfazl Mahmoudi Aqeel Abadi; Shahrokh Naseri; Mohammadreza Ay; Mohammad-Reza Nazem-Zadeh
Journal:  Inform Med Unlocked       Date:  2022-04-01

3.  Evaluation of human coronavirus OC43 and SARS-COV-2 in children with respiratory tract infection during the COVID-19 pandemic.

Authors:  Nasrin Keshavarz Valian; Babak Pourakbari; Kosar Asna Ashari; Reihaneh Hosseinpour Sadeghi; Shima Mahmoudi
Journal:  J Med Virol       Date:  2021-11-24       Impact factor: 20.693

4.  How much BiGAN and CycleGAN-learned hidden features are effective for COVID-19 detection from CT images? A comparative study.

Authors:  Sima Sarv Ahrabi; Alireza Momenzadeh; Enzo Baccarelli; Michele Scarpiniti; Lorenzo Piazzo
Journal:  J Supercomput       Date:  2022-08-26       Impact factor: 2.557

5.  Comparison of Convolutional Neural Networks and Transformers for the Classification of Images of COVID-19, Pneumonia and Healthy Individuals as Observed with Computed Tomography.

Authors:  Azucena Ascencio-Cabral; Constantino Carlos Reyes-Aldasoro
Journal:  J Imaging       Date:  2022-09-01

6.  Multisource Smart Computer-Aided System for Mining COVID-19 Infection Data.

Authors:  Mohammad T Abou-Kreisha; Humam K Yaseen; Khaled A Fathy; Ebeid A Ebeid; Kamal A ElDahshan
Journal:  Healthcare (Basel)       Date:  2022-01-06
  6 in total

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