Journal of Taibah University Medical Sciences · 2023
Age estimation from mandibles in Malay: A 2D geometric morphometric analysis.
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Zulkifli Nur Ariessa Farhana, Mohd Saaid Nur Aliya Syuhada, Alias Aspalilah, Mohamed Ibrahim Nurjehan, Woon Choy Ker, Kurniawan Arofi, Prakoeswa Beshlina Fitri Widayanti Roosyanto
Аннотация
OBJECTIVES: In this study, the sizes and forms of mandibles in various age groups of the Malay population were measured and compared.
METHODS: Geometric morphometric (GM) analysis of mandibles from 400 dental panoramic tomography (DPT) specimens was conducted. The MorphoJ program was used to perform generalized Procrustes analysis (GPA), Procrustes ANOVA, principal component analysis (PCA), discriminant function analysis (DFA), and canonical variate analysis (CVA). In the tpsDig2 program, the 27 landmarks were applied to the DPT radiographs. Variations in mandibular size and form were categorized into four age groups: group 1 (15-24 years), group 2 (25-34 years), group 3 (35-44 years), and group 4 (45-54 years).
RESULTS: The diversity in mandibular shape among the first eight principal components was 81%. Procrustes ANOVA revealed significant shape differences (P < 0.001) among age groups. Mahalanobis distances indicated substantial differences among all age groups; group 1 and group 4 scored highest, at 2.114. The ranges for the cross-validation and discriminant function tests were 90-72% and 81-49%, respectively.
CONCLUSION: GM analysis through radiography is a simple, non-invasive, and non-destructive method of estimating age by using the mandible. GM analysis is unique because it can visualize the changes in mandible shape among age groups. This method should aid in age identification in forensic odontology investigations.
METHODS: Geometric morphometric (GM) analysis of mandibles from 400 dental panoramic tomography (DPT) specimens was conducted. The MorphoJ program was used to perform generalized Procrustes analysis (GPA), Procrustes ANOVA, principal component analysis (PCA), discriminant function analysis (DFA), and canonical variate analysis (CVA). In the tpsDig2 program, the 27 landmarks were applied to the DPT radiographs. Variations in mandibular size and form were categorized into four age groups: group 1 (15-24 years), group 2 (25-34 years), group 3 (35-44 years), and group 4 (45-54 years).
RESULTS: The diversity in mandibular shape among the first eight principal components was 81%. Procrustes ANOVA revealed significant shape differences (P < 0.001) among age groups. Mahalanobis distances indicated substantial differences among all age groups; group 1 and group 4 scored highest, at 2.114. The ranges for the cross-validation and discriminant function tests were 90-72% and 81-49%, respectively.
CONCLUSION: GM analysis through radiography is a simple, non-invasive, and non-destructive method of estimating age by using the mandible. GM analysis is unique because it can visualize the changes in mandible shape among age groups. This method should aid in age identification in forensic odontology investigations.