Objective: We aimed to present a novel semiautomated tool for orbital fracture size measurement and to compare the variability of the proposed method with traditional manual measurements. Methods: Maximal anteroposterior (AP) and mediolateral (ML) dimensions of orbital fractures from computed tomography images were measured for 15 patients with unilateral orbital fractures by 2 surgeons manually and with a semiautomatic software. Variability was assessed with Bland-Altman limits of agreement plots and intra-class correlation coefficients (ICCs). Results: The intra-observer ICCs in manual and automatic measurements were high, >0.9. The inter-observer ICCs in manual measurements were 0.926 (AP) and 0.631 (ML) and in automatic measurements 0.989 (AP) and 0.989 (ML). The ICCs for manual and semiautomated variability were 0.899 (AP) and 0.669 (ML). The differences were thus particularly pronounced in the ML dimensions. In addition, with the semiautomated technique, a total fracture area could be measured and compared with the total area of the bony orbit and a 3-dimensional reformatted image could be generated. Conclusions: Intra- and inter-observer variability proved to be very low for measuring fracture maximal AP length and ML width, making both the manual and the semiautomatic methods feasible clinically. The semiautomatic fracture size analysis allows better observer-independent repeatability for fracture size measurements and provides the possibility for total fracture area measurements at any orbital bony site, even in challenging nonplanar topography.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.