DXAGE: A New Method for Age at Death Estimation Based on Femoral Bone Mineral Density and Artificial Neural Networks |
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| Authors: | David Navega M.Sc. João d'Oliveira Coelho M.Sc. Eugénia Cunha Ph.D. Francisco Curate Ph.D. |
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| Affiliation: | 1. Laboratory of Forensic Anthropology, Department of Life Sciences, University of Coimbra, Coimbra, Portugal;2. Centre for Functional Ecology, Department of Life Sciences, University of Coimbra, Coimbra, Portugal;3. Research Centre for Anthropology and Health, Department of Life Sciences, University of Coimbra, Coimbra, Portugal;4. Interdisciplinary Center for Archaeology and Evolution of Human Behavior, Faculdade das Ci?ncias Humanas e Sociais, University of Algarve, Faro, Portugal |
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| Abstract: | Age at death estimation in adult skeletons is hampered, among others, by the unremarkable correlation of bone estimators with chronological age, implementation of inappropriate statistical techniques, observer error, and skeletal incompleteness or destruction. Therefore, it is beneficial to consider alternative methods to assess age at death in adult skeletons. The decrease in bone mineral density with age was explored to generate a method to assess age at death in human remains. A connectionist computational approach, artificial neural networks, was employed to model femur densitometry data gathered in 100 female individuals from the Coimbra Identified Skeletal Collection. Bone mineral density declines consistently with age and the method performs appropriately, with mean absolute differences between known and predicted age ranging from 9.19 to 13.49 years. The proposed method—DXAGE—was implemented online to streamline age estimation. This preliminary study highlights the value of densitometry to assess age at death in human remains. |
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| Keywords: | forensic science biological profile
BMD
DXA
machine learning forensic anthropology |
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