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3D surgical planning method for lower jaw osteotomies applied to facial feminization surgery

Authors :
Valeria Marin-Montealegre
Amelia R. Cardinali
Valentina Ríos Borras
M. Camila Ceballos-Santa
Jhon Jairo Osorio-Orozco
Iris V. Rivero
Source :
Annals of 3D Printed Medicine, Vol 15, Iss , Pp 100164- (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

Our proposed method uses a three-dimensional (3D) measurement approach that focuses mainly on the lower jaw from basal, lateral, and frontal views applied to the volumetric skull model derived from a computed tomography (CT) of the head. Likewise, we discuss the geometrical features and clinical considerations involved in the 3D biomodeling of the surgical osteotomy. The workflow that allowed this virtual planning to be developed was composed of medical imaging processing software, data extraction software from images, and statistical software that allows the creation and generation of curve-fitting (nonlinear regression) graphs from data. Thirty-two (32) anatomical points were positioned, sixteen (16) measurements were taken, and two-dimensional (2D) sketches in three views (frontal, lateral, and inferior) were generated to overlap in a 3D environment, which informed the cutting of the desired bone segments. Implementing a nonlinear regression curve-fitting on the contours of the original jaws allowed optimal planning of the osteotomy. Desired cutting shapes were extrapolated for the front view by third-order equations, while for the side and bottom views, log-normal distribution curves and second-order polynomial curves were used, respectively. The reduction in the mandibular volume was between 6.55 and 10.27 %, with two of the most important measurements related to vertical reduction in the lateral views and the difference to determine gonion reduction.

Details

Language :
English
ISSN :
26669641
Volume :
15
Issue :
100164-
Database :
Directory of Open Access Journals
Journal :
Annals of 3D Printed Medicine
Publication Type :
Academic Journal
Accession number :
edsdoj.1be8c3b98522408b97ee956dff35a31b
Document Type :
article
Full Text :
https://doi.org/10.1016/j.stlm.2024.100164