Australasian radiographers’ choices of immobilisation strategies for paediatric radiological examinations

Christie, Simon; Ng, Curtise; Reis, Cláudia Sà dos

doi:10.1016/j.radi.2019.06.003

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…The dose optimization is particularly important for pediatric patients because they have longer life and more rapid cell proliferation, leading to two to three times more susceptibility to the potential harmful effects of ionizing radiation than the adult counterpart [ 17 , 33 , 36 , 40 ]. Nonetheless, dose optimization in pediatric radiology is challenging, as there is a greater variation of body size and composition within and across age groups [ 4 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

2022

Children

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Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has been published yet. The purpose of this systematic review is to answer the question “What are the AI techniques and architectures introduced in pediatric radiology for dose optimization, their specific application areas, and performances?” Literature search with use of electronic databases was conducted on 3 June 2022. Sixteen articles that met selection criteria were included. The included studies showed deep convolutional neural network (CNN) was the most common AI technique and architecture used for dose optimization in pediatric radiology. All but three included studies evaluated AI performance in dose optimization of abdomen, chest, head, neck, and pelvis CT; CT angiography; and dual-energy CT through deep learning image reconstruction. Most studies demonstrated that AI could reduce radiation dose by 36–70% without losing diagnostic information. Despite the dominance of commercially available AI models based on deep CNN with promising outcomes, homegrown models could provide comparable performances. Future exploration of AI value for dose optimization in pediatric radiology is necessary due to small sample sizes and narrow scopes (only three modalities, CT, positron emission tomography/magnetic resonance imaging and mobile radiography, and not all examination types covered) of existing studies.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

2022

Children

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“…For the 11-point scale items of the four questionnaires and two image quality assessment forms, mean and standard deviation were calculated and a paired t-test was used for comparing the mean values of the pre-and postintervention pairs of the 11-point scale items to determine any XROs' radiography competence (including image quality) improvements and enable findings comparison with the similar study [3,27,28]. A p-value less than 0.05 represented statistical significance [3,26,[29][30][31]. Content analysis with quasi-statistics as an accounting system was used to analyze the open questions about the usability of the assisted reality equipment [25,32].…”

Section: Discussionmentioning

confidence: 99%

Use of Smart Glasses (Assisted Reality) for Western Australian X-ray Operators’ Continuing Professional Development: A Pilot Study

Ng,

Baldock,

Newman

2024

Healthcare

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Previous studies have explored use of smart glasses in telemedicine, but no study has investigated its use in teleradiography. The purpose of this study was to implement a six-month pilot program for Western Australian X-ray operators (XROs) to use smart glasses to obtain assisted reality support in their radiography practice from their supervising radiographers, and evaluate its effectiveness in terms of XROs’ competence improvement and equipment usability. Pretest–posttest design with evaluation of the XROs’ competence (including their X-ray image quality) and smart glasses usability by XROs in two remote centers and their supervising radiographers from two sites before and after the program using four questionnaire sets and X-ray image quality review was employed in this experimental study. Paired t-test was used for comparing mean values of the pre- and post-intervention pairs of 11-point scale questionnaire and image quality review items to determine any XROs’ radiography competence improvements. Content analysis was used to analyze open questions about the equipment usability. Our study’s findings based on 13 participants (11 XROs and 2 supervising radiographers) and 2053 X-ray images show that the assisted reality support helped to improve the XROs’ radiography competence (specifically X-ray image quality), with mean post-intervention competence values of 6.16–7.39 (out of 10) and statistical significances (p < 0.001–0.05), and the equipment was considered effective for this purpose but not easy to use.

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“…An issue highlighted in this study is immobilisation aids and current best-practice principles. Christie et al also found more than 30% of radiographers believed their training in the use of immobilisation aids for paediatric patients was poor or lacking [47]. Similarly, Satharasinghe et al found radiographers were tending to use a larger than required x-ray field in an attempt to avoid repeating the imaging examination [48].…”

Section: Discussionmentioning

confidence: 99%

Radiographers’ perceptions on the quality of managing general radiographic paediatric examinations through the use of a reflective tool

Caruana,

Hayre,

Makanjee

2023

PLoS ONE

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Introduction Paediatric patients are a vulnerable population that require additional care by healthcare professionals. Quality managing these examinations ensures that effective and quality care is provided to individual patients, whilst encouraging consistency within the medical imaging department. This study explored radiographers’ perspectives on quality management strategies of general radiographic paediatric examinations using a paediatric imaging reflective checklist. Methods A quantitative descriptive research design with qualitative questions was used through a purposive sampling method from both public and private Australian diagnostic imaging qualified radiographers who had experience in paediatric imaging examinations. The paediatric imaging service reflective tool consisted of 65 items in total. Data analysis entailed Microsoft Excel version 16.16.6 and Jamovi version 2.3.21 for the closed-ended questions and for the open-ended responses a thematic analysis. Results The participation rate was 13.2% and the most significant findings were: lead shielding was still being used at their organisation, despite recent recommendations to suspend its use; access to paediatric patient related information resources is limited; there was no involvement of families and communities regarding policy development or quality improvement measures as advocated in literature; and there was a need for enhanced specialised paediatric education, training and protocols. Conclusion Using the paediatric patient-centred imaging reflective checklist, radiographers had an opportunity to identify quality improvement indicators as well as issues that could further enhance best practice principles. Further studies could inform on the validity of this reflective tool.

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Australasian radiographers’ choices of immobilisation strategies for paediatric radiological examinations

Cited by 6 publications

References 35 publications

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

Use of Smart Glasses (Assisted Reality) for Western Australian X-ray Operators’ Continuing Professional Development: A Pilot Study

Radiographers’ perceptions on the quality of managing general radiographic paediatric examinations through the use of a reflective tool

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