Computational Intelligence in Metric Analysis of the Skull in the Context of Maxillofacial Surgery

Abstract

Anthropometric studies focusing on facial metrics and their proportions form an important research area devoted to observations of the appearance of the human skull. Many different applications include the use of craniometry for maxillofacial reconstruction and surgery. This paper explores the possibility of using selected craniometric points and associated metric to observe spatial changes during the maxillofacial surgery treatment. The experimental dataset includes observations of 27 individuals. The proposed method is associated with the processing of measurements by selected methods of signal processing and computational intelligence. The statistical results point to changes of facial measures before and after the maxillofacial surgery. The proposed method conclusively demonstrates that the area of the mean upper law triangle after surgical treatment is decreased by 8.5%, at the 5% significance level of the two-sample t-test. The classification of selected measurements by a neural network model reached an accuracy of 84.9%.

Keywords

• Craniometry
• skull identification
• maxillofacial surgery
• computational intelligence
• machine learning