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https://repositorio.ufu.br/handle/123456789/45287| ORCID: |  http://orcid.org/0009-0002-6259-2876 |
| Document type: | Trabalho de Conclusão de Curso |
| Access type: | Acesso Aberto |
| Title: | Machine learning application in sexual dimorphism analysis through zygomatic bone dimensions |
| Author: | Neto, Lelis da Costa |
| First Advisor: | Beaini, Thiago Leite |
| First member of the Committee: | Paranhos, Luiz Renato |
| Second member of the Committee: | Oliveira, João Edson Carmo |
| Summary: | Forensic anthropology (FA) aims to study the human body to aid justice. The human skull is recognized as a structure that houses a series of characteristics subject to individual variability which can occur due to the biological sex of the individuals, genetic action of ancestry, or age. Volumetric examinations allow the observer to assess morphology in a differentiated manner, and volume is one of the ways to represent this topography. However, there is a gap in studies in this field, as various structures can aid in forensic analysis, especially in the context of searching for missing persons. To evaluate the individualizing capacity of the zygomatic bone in studying sexual dimorphism and human identification. The pilot study included 36 anonymous cone-beam computed tomography (CBCT), divided between 18 male and 18 female. The DICOM files were imported into Blender with OrthogOnBlender addon to reconstruct the bone tissues and export it into .stl files. In the Freeform software, using a haptic device to precisely select the zygomatic bone at its borders with the temporal, sphenoid, frontal, and maxillary bones. Tests showed normality in distribution. T-tests demonstrated a statistically significant difference (p=<0.05) in the variables Volume (p=0.033) and Height (z) (p=0.004). The volumetric analysis of the zygomatic bone proves to be a promising tool for sex estimation in human identification services. |
| Keywords: | Forensic Anthropology Antropologia Forense Zygomatic Bone Osso zigomático Sexual Dimorphism Dimorfismo Sexual Cone- Beam Computed Tomography Tomografia Computadorizada de Feixe Cônico |
| Area (s) of CNPq: | CNPQ::CIENCIAS DA SAUDE::ODONTOLOGIA |
| Language: | eng |
| Country: | Brasil |
| Publisher: | Universidade Federal de Uberlândia |
| Quote: | NETO, Lelis da Costa. Machine learning application in sexual dimorphism analysis through zygomatic bone dimensions. 2025. 13 f. Trabalho de Conclusão de Curso (Graduação em Odontologia)- Universidade Federal de Uberlândia, Uberlândia, 2025. |
| URI: | https://repositorio.ufu.br/handle/123456789/45287 |
| Date of defense: | 1-Apr-2025 |
| Appears in Collections: | TCC - Odontologia |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Machinelearningapplication.pdf | Trabalho de Conclusão de Curso | 977.98 kB | Adobe PDF |  View/Open |
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