Low-dose computed tomography in COVID-19: systematic review
Digital Diagnostics
; 4(1):25-37, 2023.
Article
in Russian
| Scopus | ID: covidwho-2322044
ABSTRACT
BACKGROUND:
The increased number of computed tomography scans during the COVID-19 pandemic has emphasized the task of decreasing radiation exposure of patients, since it is known to be associated with an elevated risk of cancer development. The ALARA (as low as reasonably achievable) principle, proposed by the International Commission on Radiation Protection, should be adhered to in the operation of radiation diagnostics departments, even during the pandemic.AIM:
To systematize data on the appropriateness and effectiveness of low-dose computed tomography in the diagnosis of lung lesions in COVID-19. MATERIALS ANDMETHODS:
Relevant national and foreign literature in scientific libraries PubMed and eLIBRARY, using English and Russian queries "low-dose computed tomography” and "COVID-19,” published between 2020 and 2022 were analyzed. Publications were evaluated after assessing the relevance to the review topic by title and analysis. The references were further analyzed to identify articles omitted during the search that may meet the inclusion criteria.RESULTS:
Published studies summarized the current data on the imaging of COVID-19 lung lesions and the use of computed tomography scans and identified possible options for reducing the effective dose.CONCLUSION:
We present techniques to reduce radiation exposure during chest computed tomography and preserve high-quality diagnostic images potentially sufficient for reliable detection of COVID-19 signs. Reducing radiation dose is a valid approach to obtain relevant diagnostic information, preserving opportunities for the introduction of advanced computational analysis technologies in clinical practice. © Eco-Vector, 2023.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
/
Prognostic study
/
Reviews
/
Systematic review/Meta Analysis
Language:
Russian
Journal:
Digital Diagnostics
Year:
2023
Document Type:
Article
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