Are UK radiologists satisfied with the training and support received in suspected child abuse?

Leung, Reynold; Nwachuckwu, C.; Pervaiz, Abida; Wallace, C; Landes, Caren; Offiah, A.C.

doi:10.1016/j.crad.2009.02.012

Cited by 13 publications

(7 citation statements)

References 16 publications

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“…This could be due to fewer children within these categories attending emergency departments to provide the necessary imaging data for training AI models, but the result is that specific paediatric populations may be unintentionally marginalised or poorly served by such new technologies and raises potential ethical considerations about their future usage particularly when performance characteristics are extrapolated beyond the population on which the tool was developed and validated [ 53 ]. An example would be an AI tool which could help to evaluate the particular aspects of fractures relating to suspected physical abuse as an adjunct to clinical practice given that many practising paediatric radiologists do not feel appropriately trained or confident in this aspect of imaging assessment [ 54 – 57 ]. Whilst data are limited, one study did address the topic of using AI for identifying suspected physical abuse through the detection of corner metaphyseal fractures (a specific marker of abuse) [ 58 ] with a high diagnostic accuracy.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence for radiological paediatric fracture assessment: a systematic review

et al. 2022

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Background Majority of research and commercial efforts have focussed on use of artificial intelligence (AI) for fracture detection in adults, despite the greater long-term clinical and medicolegal implications of missed fractures in children. The objective of this study was to assess the available literature regarding diagnostic performance of AI tools for paediatric fracture assessment on imaging, and where available, how this compares with the performance of human readers. Materials and methods MEDLINE, Embase and Cochrane Library databases were queried for studies published between 1 January 2011 and 2021 using terms related to ‘fracture’, ‘artificial intelligence’, ‘imaging’ and ‘children’. Risk of bias was assessed using a modified QUADAS-2 tool. Descriptive statistics for diagnostic accuracies were collated. Results Nine eligible articles from 362 publications were included, with most (8/9) evaluating fracture detection on radiographs, with the elbow being the most common body part. Nearly all articles used data derived from a single institution, and used deep learning methodology with only a few (2/9) performing external validation. Accuracy rates generated by AI ranged from 88.8 to 97.9%. In two of the three articles where AI performance was compared to human readers, sensitivity rates for AI were marginally higher, but this was not statistically significant. Conclusions Wide heterogeneity in the literature with limited information on algorithm performance on external datasets makes it difficult to understand how such tools may generalise to a wider paediatric population. Further research using a multicentric dataset with real-world evaluation would help to better understand the impact of these tools.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence for radiological paediatric fracture assessment: a systematic review

et al. 2022

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“…It has previously been shown that the diagnostic accuracy of relatively senior radiologists for the detection of the subtle fractures of child abuse was low and that there was no correlation between years of experience and expertise, in spite of many experts being experienced (19). A further study demonstrated that UK radiologists perceive they would benefit from improved training in this field (20).…”

Section: B J R U N C O R R E C T E D P R O O F Smentioning

confidence: 99%

Internet-based measurement of visual assessment skill of trainee radiologists: developing a sensitive tool

Thirkettle

Thyoka

Gopalan

et al. 2019

The British Journal of Radiology

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Objective: Expert radiologists exhibit high levels of visual diagnostic accuracy from review of radiological images, doing so after accumulating years of training and experience. To train new radiologists, learning interventions must focus on the development of these skills. By developing a web-based measure of image assessment, a key part of visual diagnosis, we aimed to capture differences in the performance of expert, trainee and non-radiologists. Methods: Twelve consultant paediatric radiologists, twelve radiology registrars, and thirty-nine medical students were recruited to the study. All participants completed a two-part, online task requiring them to visually assess 30 images (25 containing an Title Page

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“…Even with the best quality imaging, acquired by experienced radiographers in a compliant child, subtle findings may be missed and / or others mis-interpreted. Training in this area remains a key priority and is recognised as such [7]. The time interval between the first and second parts of the skeletal survey gives an opportunity to evaluate the child further, and identify radiological changes over time, although potentially delaying the diagnosis [8], and other imaging modalities such as chest CT may need to be considered in the first instance.…”

Section: Imaging In Spamentioning

confidence: 99%

Paediatric radiology: child abuse imaging in the national spotlight

Arthurs

Oates

Halliday³

2021

Clinical Radiology

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Are UK radiologists satisfied with the training and support received in suspected child abuse?

Cited by 13 publications

References 16 publications

Artificial intelligence for radiological paediatric fracture assessment: a systematic review

Artificial intelligence for radiological paediatric fracture assessment: a systematic review

Internet-based measurement of visual assessment skill of trainee radiologists: developing a sensitive tool

Paediatric radiology: child abuse imaging in the national spotlight

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