This final report addresses the gap in best practice when estimating age in subadult skeletal remains by providing an accurate method with a contemporary sample, as well as the most appropriate statistical frameworks, specifically transition analysis and machine learning methods.
Through this report, the author seeks to provide forensic anthropologists with an accurate age estimation method and tool, based on a large, demographically diverse, and modern subadult sample that accurately captures variation in the dental developmental process. The author used dental development within a transition analysis (TA) framework in order to allow for statistical rigor to be associated with the method. Statistics included within the TA output served as point age estimates along with associated estimate age-at-death ranges, which are unique to the individual. The main objectives for this research project were: to collect data for multiple populations to refine age estimates by tooth, specific to various geographic groups in forensically-significant populations from which to derive reference data and identify patterns in dental development; to develop age estimation models based on the data; to investigate exploratory analyses focused on the type and number of teeth needed to accurately estimate age; and to develop and distribute a freely available open-source computer application that can be easily accessed and used by practitioners when estimating age in unknown subadult remains. The report lays out an overview of the research project design, methodology, findings, and artifacts, as well as an appendix with the data collection procedure. Samples originated from Barts and London School of Medicine, U.K.; South Africa; Universitie de Bordeaux, France; Universidad Complutense de Madrid, Spain; University of Texas Health Science Center; University of New Mexico; and New Mexico Decedent Image Database (NMDID).
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