A Primary Study on Application of Artificial Neural Network in Classification of Pediatric Fracture Healing Time of the Lower Limb

Malek, Sorayya; Gunalan, Roshan; Kedija, S. Y.; Lau, Chia Fong; Mosleh, Mogeeb A. A.; Milow, Pozi; Amber, H.; Saw, Aik

doi:10.1007/978-3-319-40126-3_3

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Patients were neither consecutive, nor randomly sampled therefore it was questionable as to how generalisable the study results could be. In the second study [32], children were only included if they had a con rmed lower limb fracture, and were labelled as having either normal fracture healing time or delayed fracture healing (> 12 weeks). The mechanism for follow-up to determine fracture healing time, or the reason for choosing a 12 week time frame were not speci ed and furthermore it was not stated whether children with pre-existing bone fragility disorders were included.…”

Section: Methodological Quality Assessmentmentioning

confidence: 99%

“…Almost half of all studies had unclear/moderate concerns regarding applicability of patient selection (4/9, 44.4%) [31; 34; 36; 37] and most had concerns regarding applicability of index test (6/9, 66.7%) [31][32][33][34][35][36]. This was predominantly due to studies imposing strict exclusion criteria in their patient selection (e.g.…”

Section: Methodological Quality Assessmentmentioning

confidence: 99%

“…The remainder either developed or retrained existing convolutional neural networks. One study evaluated the use of self-organising maps (SOM) and also convolutional neural networks in the evaluation of fracture healing [32].…”

Section: Imaging Algorithm Methodologymentioning

confidence: 99%

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Artificial Intelligence for Radiological Paediatric Fracture Assessment: A Systematic Review

Shelmerdine

White

Liu

et al. 2022

Preprint

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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 & Methods: MEDLINE, Embase and Cochrane Library databases were queried for studies published between 1 January 2011–2021 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. Accuracy rates generated by AI ranged from 88.8–97.9%. In two of the three articles where AI performance was compared to human readers, sensitivity rates for AI was marginally higher, but this was not statistically significant. Conclusions: There is a high diagnostic accuracy for most AI tools for fracture detection in children, although the generalisability of these tools for a wider population is unknown. Opportunities exist for future development of AI tools for cross-sectional imaging and in certain paediatric populations (e.g. <2 years old, those with inherited bone disorders).

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Section: Methodological Quality Assessmentmentioning

confidence: 99%