An automatic method for skeletal patterns classification using craniomaxillary variables on a Colombian population

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• 作者：Tania Camila Niñ ; o-Sandoval^a ; ^{kotc2578@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Sonia V. Guevara Perez^b ; ^{svguevarap@unal.edu.co" class="auth_mail" title="E-mail the corresponding author} ; Fabio A. Gonzá ; lez^c ; ^{fagonzalezo@unal.edu.co" class="auth_mail" title="E-mail the corresponding author} ; Robinson André ; s Jaque^c ; ^{rajaquep@unal.edu.co" class="auth_mail" title="E-mail the corresponding author} ; Clementina Infante-Contreras^a ; ^{ccontrerasi@unal.edu.co" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Support Vector Machines ; Class I ; II ; III skeletal pattern ; Craniomaxillary variables ; Colombian population ; Forensic anthropology population data
• 刊名：Forensic Science International
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：261
• 期：Complete
• 页码：159.e1-159.e6
• 全文大小：1125 K

文摘

The mandibular bone is an important part of the forensic facial reconstruction and it has the possibility of getting lost in skeletonized remains; for this reason, it is necessary to facilitate the identification process simulating the mandibular position only through craniomaxillary measures, for this task, different modeling techniques have been performed, but they only contemplate a straight facial profile that belong to skeletal pattern Class I, but the 24.5% corresponding to the Colombian skeletal patterns Class II and III are not taking into account, besides, craniofacial measures do not follow a parametric trend or a normal distribution.

Objective

The aim of this study was to employ an automatic non-parametric method as the Support Vector Machines to classify skeletal patterns through craniomaxillary variables, in order to simulate the natural mandibular position on a contemporary Colombian sample.

Materials and methods

Lateral cephalograms (229) of Colombian young adults of both sexes were collected. Landmark coordinates protocols were used to create craniomaxillary variables. A Support Vector Machine with a linear kernel classifier model was trained on a subset of the available data and evaluated over the remaining samples. The weights of the model were used to select the 10 best variables for classification accuracy.

Results

An accuracy of 74.51% was obtained, defined by Pr-A-N, N-Pr-A, A-N-Pr, A-Te-Pr, A-Pr-Rhi, Rhi-A-Pr, Pr-A-Te, Te-Pr-A, Zm-A-Pr and PNS-A-Pr angles. The Class Precision and the Class Recall showed a correct distinction of the Class II from the Class III and vice versa.

Conclusions

Support Vector Machines created an important model of classification of skeletal patterns using craniomaxillary variables that are not commonly used in the literature and could be applicable to the 24.5% of the contemporary Colombian sample.