SEXUAL DIMORPHISM AND AGE ESTIMATION OF THE CHILDREN MANDIBLE FROM DENTAL PANORAMIC TOMOGRAPHY (DPT): A GEOMETRIC MORPHOMETRIC ANALYSIS
DOI:
https://doi.org/10.33102/7c0dz371Keywords:
Forensic odontology, sexual dimorphism, age estimation, paediatric population, geometric morphometric analysisAbstract
Forensic odontology is the field of dentistry that identifies human remains. The mandible is the strongest bone in the face and remains intact even in mass disasters. Geometric morphometrics is a shape analysis that uses landmark coordinates that can visualise the variation of the structure. A total of 305 DPT images of 159 male and 146 female Malay children were classified into two age groups: Group 1 (ages 3–7) and Group 2 (ages 8–12). These images were analysed using geometric morphometric analysis. Twenty landmarks were digitised using the tpsDig2 software. MorphoJ was used to perform discriminant function analysis (DFA), canonical variate analysis (CVA), principal component analysis (PCA), generalised Procrustes analysis (GPA), and Procrustes ANOVA. There were significant differences in mandible shape and size between the two age groups and sexes (p < 0.05). The first five principal components (PC1–PC5) explained 75% of the shape variation. The DFA showed 82% accuracy in classifying children into age groups after cross-validation. However, the accuracy among males and females dropped to 62%, due to overlapping characteristics and the absence of secondary sexual traits in children under 12. Geometric morphometry can capture unique morphological shape variables, thus enabling the assessment of sexual dimorphism and age estimation using mandibles to aid forensic odontology. This research supports SDG 16: Peace, Justice and Strong Institutions, by enhancing scientific tools for victim identification and justice in the aftermath of disasters and crimes involving children.
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Copyright (c) 2025 Siti Aisyah Aminah Rosli, Aspalilah Alias, Ammar Rezadin, Nurjehan Mohamed Ibrahim, Azwa Syuhada Samshuddin, Muhammad Faiz Mohd Fauad, Arofi Kurniawan, Khalid Ayidh Alqahtani

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