DXAGE: A New Method for Age at Death Estimation Based on Femoral Bone Mineral Density and Artificial Neural Networks. Issue 2 (29th August 2017)

• Record Type: Journal Article
• Authors: Navega, David; Coelho, João d'Oliveira; Cunha, Eugénia; Curate, Francisco
• Is Part Of: Journal of forensic sciences. Volume 63:Issue 2(2018)
• Page Start: 497
• Page End: 503
• Publication Date: 2017-08-29
• Subjects: forensic science -- biological profile -- BMD -- DXA -- machine learning -- forensic anthropology; Medical jurisprudence -- Periodicals; Forensic sciences -- Periodicals; Forensic Medicine -- Periodicals; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; 363.2505
• DOI: 10.1111/1556-4029.13582
• Languages: English
• ISSNs: 0022-1198

Abstract

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.