Artificial intelligence fracture recognition on computed tomography: review of literature and recommendations

Abstract

Purpose The use of computed tomography (CT) in fractures is time consuming, challenging and suffers from poor inter-surgeon reliability. Convolutional neural networks (CNNs), a subset of artificial intelligence (AI), may overcome shortcomings and reduce clinical burdens to detect and classify fractures. The aim of this review was to summarize literature on CNNs for the detection and classification of fractures on CT scans, focusing on its accuracy and to evaluate the beneficial role in daily practice. Methods Literature search was performed according to the PRISMA statement, and Embase, Medline ALL, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar databases were searched. Studies were eligible when the use of AI for the detection of fractures on CT scans was described. Quality assessment was done with a modified version of the methodologic index for nonrandomized studies (MINORS), with a seven-item checklist. Performance of AI was defined as accuracy, F1-score and area under the curve (AUC). Results Of the 1140 identified studies, 17 were included. Accuracy ranged from 69 to 99%, the F1-score ranged from 0.35 to 0.94 and the AUC, ranging from 0.77 to 0.95. Based on ten studies, CNN showed a similar or improved diagnostic accuracy in addition to clinical evaluation only. Conclusions CNNs are applicable for the detection and classification fractures on CT scans. This can improve automated and clinician-aided diagnostics. Further research should focus on the additional value of CNN used for CT scans in daily clinics.

A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging

“…(Wu et al, 2013;Ozturk and Kutucu, 2017;Cheng et al, 2019Cheng et al, , 2021Bluthgen et al, 2020;Jones et al, 2020;Krogue et al, 2020;Ajmera et al, 2021;Duron et al, 2021;Lind et al, 2021;Ren and Yi, 2021;Sato et al, 2021;Yoon et al, 2021;Guermazi et al, 2022;Liu et al, 2022Liu et al, , 2023Murphy et al, 2022;Nguyen et al, 2022;Oakden-Rayner et al, 2022;Bousson et al, 2023;Gao et al, 2023;Hendrix et al, 2023). Reviews of AI applications for detecting non-facial bone fractures in adults and children have been reported in the literature (Lindsey et al, 2018;Kalmet et al, 2020;Rainey et al, 2021;Cha et al, 2022;Dankelman et al, 2022;Kuo et al, 2022;Meena and Roy, 2022;Shelmerdine et al, 2022;Zech et al, 2022).…”

Deep Learning-Based Surgical Treatment Recommendation and Nonsurgical Prognosis Status Classification for Scaphoid Fractures by Automated X-ray Image Recognition

“…Using object detection, bones can be segmented. Accordingly, a set of previous works were focused on fracture image recognition [ 21 , 22 , 23 ]. Because our aim is scaphoid fracture recognition, here, we only listed the recent studies on scaphoid fracture recognition.…”