The mandible or lower jaw is the largest and hardest bone in the human facial skeleton. Fractures of the mandible are reported to be a common facial trauma in emergency medicine and gaining insights into mandibular morphology in different facial types can be helpful for trauma treatment. Furthermore, features of the mandible play an important role in forensics and anthropology for identifying gender and individuals. Thus, discovering hidden information of the mandible can benefit interdisciplinary research. Here, for the first time, a method of artificial intelligence-based nonlinear dynamics and network analysis are used for discovering dissimilar and similar radiographic features of mandibles between male and female subjects. Using a public dataset of 10 computed tomography scans of mandibles, the results suggest a difference in the distribution of spatial autocorrelation between genders, uniqueness in network topologies among individuals and shared values in recurrence quantification.
Keywords: artificial intelligence; computed tomography; image processing; mandible; network theory; nonlinear data analysis.
© 2024 The Authors.