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Tytuł pozycji:

Perfecting detection through education.

Tytuł :
Perfecting detection through education.
Autorzy :
Suleiman ME; L02 H04, Merewether Building, The University of Sydney, Sydney, NSW, 2006, Australia. Electronic address: .
Rickard M; L02 H04, Merewether Building, The University of Sydney, Sydney, NSW, 2006, Australia. Electronic address: .
Brennan PC; The University of Sydney, Faculty of Health Sciences, M205, Cumberland Campus, 75 East St, Lidcombe, NSW, 2141, Australia. Electronic address: .
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Źródło :
Radiography (London, England : 1995) [Radiography (Lond)] 2020 Oct; Vol. 26 Suppl 2, pp. S49-S53. Date of Electronic Publication: 2020 Jul 19.
Typ publikacji :
Journal Article
Język :
English
Imprint Name(s) :
Publication: 2003- : Amsterdam : Elsevier B.V.
Original Publication: London ; Philadelphia : W.B. Saunders Co., c1995-
MeSH Terms :
Clinical Competence*
Radiographic Image Interpretation, Computer-Assisted*
Education, Medical, Continuing/*methods
Radiology/*education
Betacoronavirus ; Breast Neoplasms/diagnostic imaging ; Coronavirus Infections/diagnostic imaging ; Humans ; Lung/diagnostic imaging ; Lung Neoplasms/diagnostic imaging ; Mammography ; Pandemics ; Pneumonia, Viral/diagnostic imaging
Contributed Indexing :
Keywords: COVID-19*; Continuous medical education*; Continuous professional development*; Mammography*; Mammography training*
SCR Disease Name :
COVID-19
Entry Date(s) :
Date Created: 20200724 Date Completed: 20200909 Latest Revision: 20201014
Update Code :
20201023
PubMed Central ID :
PMC7369166
DOI :
10.1016/j.radi.2020.06.006
PMID :
32698948
Czasopismo naukowe
Introduction: Radiologists' image reading skills vary, such variations in image interpretations can influence the effectiveness of the early treatment of disease and may have important clinical and economic implications. In screening mammography, clinical audits are used to assess radiologists' performance annually, however, the nature of these audits prevent robust data analysis due to the low prevalence of breast cancer and the long waiting periods for the audit results. Research-based evidence revealed a need for changes in the methods utilised to optimise the assessment of the efficacy of radiologists' interpretations.
Methods: A cloud-based platform was developed to assess and enhance radiologists' performance help reduce variability in medical image interpretations in a research environment; however, to address a number of limitations, the platform was commercialised to make it available worldwide.
Results: DetectED-X's team have been able to make their cloud-based platform available worldwide, tailored to the needs of radiologists and accredited for continuing medical/professional education; thus, changing the continuous professional development practice globally.
Conclusion: DetectED-X's Rivelato, was developed to address a need for effective, available and affordable educational solutions for clinicians and health care workers wherever they are located. A true fusion of industry, academia, clinics and consumer to adapt to the growing needs of clinicians' around the world, the latest being COVID-19 global pandemic. DetectED-X repurposed its platform to educate physicians around the world on the appearances of COVID-19 on Lung Computed Tomography scans, introducing CovED to clinicians worldwide free of charge as a multi-national consortium of collaboration to help fight COVID-19, showing how research-based evidence can create effective and scalable change globally.
(Copyright © 2020 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.)

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