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

Lente H.M. Dankelman*, Sanne Schilstra, Frank IJpma, Job Doornberg, Joost Colaris, Michael H.J. Verhofstad, M. M. E. Wijffels, Jasper Prijs, On Behalf of Machine Learning Consortium

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

7 Citations (Scopus)
24 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)681-691
Number of pages11
JournalEuropean Journal of Trauma and Emergency Surgery
Volume49
Issue number2
Early online date26 Oct 2022
DOIs
Publication statusPublished - Apr 2023

Bibliographical note

Funding Information:
No funding has been received for this study. We would like to acknowledge Maarten Engel of the Medical Library of Erasmus MC for their help in building the final search strategy. Paul Algra; Michel van den Bekerom; Mohit Bhandari; Michiel Bongers; Charles Court-Brown; Anne-Eva Bulstra; Geert Buijze; Sofia Bzovsky; Joost Colaris; Neil Chen; Job Doornberg; Andrew Duckworth; J. Carel Goslings; Max Gordon; Benjamin Gravesteijn; Olivier Groot; Gordon Guyatt; Laurent Hendrickx; Beat Hintermann; Dirk-Jan Hofstee; Frank IJpma; Ruurd Jaarsma; Stein Janssen; Kyle Jeray; Paul Jutte; Aditya Karhade; Lucien Keijser; Gino Kerkhoffs; David Langerhuizen; Jonathan Lans; Wouter Mallee; Matthew Moran; Margaret McQueen; Marjolein Mulders; Rob Nelissen; Miryam Obdeijn; Tarandeep Oberai; Jakub Olczak; Jacobien H.F. Oosterhoff; Brad Petrisor; Rudolf Poolman; Jasper Prijs; David Ring; Paul Tornetta III; David Sanders; Joseph Schwab; Emil H. Schemitsch; Niels Schep; Inger Schipper; Bram Schoolmeesters; Joseph Schwab; Marc Swiontkowski; Sheila Sprague; Ewout Steyerberg; Vincent Stirler; Paul Tornetta; Stephen D. Walter; Monique Walenkamp; Mathieu Wijffels; Charlotte Laane.

Publisher Copyright:
© 2022, The Author(s).

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