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An approach to automated measuring morphological parameters of proximal femora on three-dimensional models.

Authors :
Hu, Junlei
Xu, Liyu
Jing, Mengjie
Zhang, Henghui
Wang, Liao
Chen, Xiaojun
Source :
International Journal of Computer Assisted Radiology & Surgery; Jan2020, Vol. 15 Issue 1, p109-118, 10p
Publication Year :
2020

Abstract

Purpose: Analyses of the morphology of proximal femora are essential for preoperative planning and designing customized femoral stems in total hip arthroplasty as well as intramedullary nailing fixation. Various studies reported measurements and analyses on the general geometry of proximal femora three-dimensionally. However, the modeling and measurements are time-consuming and unfriendly to surgeons. Thus, automated measurement and modeling of the femoral medullary canal are critical to promote the clinical application. Methods: An approach to automated measuring morphological parameters of proximal femur was proposed, and a software allowing importing femur models and manually locating the related anatomic landmarks was developed in the current study. 3D modeling of the femoral medullary canal was created by the semispherical iterative searching algorithm, and 16 key anatomic parameters of the proximal femur were automatically calculated by the least-squares fitting algorithm. Results: By experimenting on 196 femur STL models, the average execution time of single measurement was 0.88 (SD = 0.13) s, and the intra-class correlation coefficient of 10 anatomic parameters was between 0.880 and 0.996, showing high intra-rater and inter-rater reliability. Conclusions: The parameters of proximal femur can be easily measured, and the 3D modeling of the femoral medullary canal can be rapidly achieved. The approach will be easily applicable to clinical practice and has the potential to be applied in the design of customized femoral stems. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18616410
Volume :
15
Issue :
1
Database :
Complementary Index
Journal :
International Journal of Computer Assisted Radiology & Surgery
Publication Type :
Academic Journal
Accession number :
141098798
Full Text :
https://doi.org/10.1007/s11548-019-02095-w