Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/103187
Title: Adult Skeletal Age-at-Death Estimation through Deep Random Neural Networks: A New Method and Its Computational Analysis
Authors: Navega, David 
Costa, Ernesto 
Cunha, Eugénia 
Keywords: forensic anthropology; age-at-death estimation; machine learning; neural networks
Issue Date: 30-Mar-2022
Project: FCT - SFRH/BD/99676/2014 
Serial title, monograph or event: Biology
Volume: 11
Issue: 4
Abstract: Age-at-death assessment is a crucial step in the identification process of skeletal human remains. Nonetheless, in adult individuals this task is particularly difficult to achieve with reasonable accuracy due to high variability in the senescence processes. To improve the accuracy of age-at-estimation, in this work we propose a new method based on a multifactorial macroscopic analysis and deep random neural network models. A sample of 500 identified skeletons was used to establish a reference dataset (age-at-death: 19-101 years old, 250 males and 250 females). A total of 64 skeletal traits are covered in the proposed macroscopic technique. Age-at-death estimation is tackled from a function approximation perspective and a regression approach is used to infer both point and prediction interval estimates. Based on cross-validation and computational experiments, our results demonstrate that age estimation from skeletal remains can be accurately (~6 years mean absolute error) inferred across the entire adult age span and informative estimates and prediction intervals can be obtained for the elderly population. A novel software tool, DRNNAGE, was made available to the community.
URI: http://hdl.handle.net/10316/103187
ISSN: 2079-7737
DOI: 10.3390/biology11040532
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais
I&D CFE - Artigos em Revistas Internacionais

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