{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T12:30:38Z","timestamp":1777120238918,"version":"3.51.4"},"reference-count":145,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T00:00:00Z","timestamp":1648598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biology"],"abstract":"<jats:p>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\u2013101 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.<\/jats:p>","DOI":"10.3390\/biology11040532","type":"journal-article","created":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T21:22:14Z","timestamp":1648675334000},"page":"532","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Adult Skeletal Age-at-Death Estimation through Deep Random Neural Networks: A New Method and Its Computational Analysis"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4495-3762","authenticated-orcid":false,"given":"David","family":"Navega","sequence":"first","affiliation":[{"name":"Centre for Functional Ecology (CEF), Laboratory of Forensic Anthropology, Department of Life Sciences, University of Coimbra, 3000-456 Coimbra, Portugal"},{"name":"National Institute of Legal Medicine and Forensic Sciences, 3000-548 Coimbra, Portugal"}]},{"given":"Ernesto","family":"Costa","sequence":"additional","affiliation":[{"name":"Centre for Informatics and Systems of the University of Coimbra (CISUC), Evolutionary and Complex Systems Group (ECOS), Department of Informatics Engineering, University of Coimbra, 3030-290 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2998-371X","authenticated-orcid":false,"given":"Eug\u00e9nia","family":"Cunha","sequence":"additional","affiliation":[{"name":"Centre for Functional Ecology (CEF), Laboratory of Forensic Anthropology, Department of Life Sciences, University of Coimbra, 3000-456 Coimbra, Portugal"},{"name":"National Institute of Legal Medicine and Forensic Sciences, 3000-548 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1002\/ajpa.20948","article-title":"New perspectives in forensic anthropology","volume":"137","author":"Dirkmaat","year":"2008","journal-title":"Am. 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