Objectives: This paper aims to share our experience in reorganising our general radiography service during the coronavirus disease (COVID-19) pandemic from the viewpoint of a large tertiary referral medical centre.
Key Findings: Re-organization of the radiography workforce, patient segregation, and modification of routine radiographic practices are key measures to help radiographic services deal with the COVID-19 pandemic. Specific emphasis on deploying more mobile radiographic units, segregating equipment, developing consistent image acquisition workflows, and strict adherence to infection control protocols are paramount to minimize the possibility of in-hospital transmission and ensure a safe environment for both patients and staff. Streamlining communication channels between leadership and ground staff allows quick dissemination of information to ultimately facilitate safe provision of services.
Conclusion: COVID-19 has drastically altered the way general radiography teams provide services. The institution of several key measures will allow hospitals to safely and sustainably provide radiographic services. To date, there have been zero incidences of radiographer healthcare worker transmission within our institution during the course of work.
Implication For Practice: Radiographers are facing the challenge of providing high-quality services while simultaneously minimizing pathogen exposure to staff and patients. Our experience may lend support to other radiographic services responding to the COVID-19 outbreak and serve as a blueprint for future infectious disease outbreak contingency plans.
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http://dx.doi.org/10.1016/j.radi.2020.05.001 | DOI Listing |
J Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Cancer Screening, American Cancer Society, Atlanta, GA, United States.
Background: The online nature of decision aids (DAs) and related e-tools supporting women's decision-making regarding breast cancer screening (BCS) through mammography may facilitate broader access, making them a valuable addition to BCS programs.
Objective: This systematic review and meta-analysis aims to evaluate the scientific evidence on the impacts of these e-tools and to provide a comprehensive assessment of the factors associated with their increased utility and efficacy.
Methods: We followed the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and conducted a search of MEDLINE, PsycINFO, Embase, CINAHL, and Web of Science databases from August 2010 to April 2023.
JCO Clin Cancer Inform
January 2025
Department of Radiology, Dr BRAIRCH, All India Institute of Medical Sciences, New Delhi, India.
Purpose: To explore the perceived utility and effect of simplified radiology reports on oncology patients' knowledge and feasibility of large language models (LLMs) to generate such reports.
Materials And Methods: This study was approved by the Institute Ethics Committee. In phase I, five state-of-the-art LLMs (Generative Pre-Trained Transformer-4o [GPT-4o], Google Gemini, Claude Opus, Llama-3.
Br J Radiol
January 2025
2nd Department of Radiology, University General Hospital "ATTIKON", Medical School, National and Kapodistrian University of Athens, Greece.
In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalised medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted.
View Article and Find Full Text PDFR I Med J (2013)
February 2025
Alpert Medical School of Brown University, Department of Medicine, Division of Cardiology, Rhode Island Hospital.
Cardiac Positron Emission Tomography (PET) can be used for the assessment of myocardial perfusion. Compared to other cardiac imaging techniques, notably Single Photon Emission Computer Tomography (SPECT), cardiac PET offers superior image resolution, higher accuracy, quantitative measures of myocardial perfusion, lower radiation exposure, and shorter image acquisition time. However, PET tends to be costlier and less widely available than SPECT due to the specialized equipment needed for generating the necessary radiotracers.
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