*Article* **Adult Skeletal Age-at-Death Estimation through Deep Random Neural Networks: A New Method and Its Computational Analysis**

**David Navega 1,2,\* , Ernesto Costa <sup>3</sup> and Eugénia Cunha 1,2**


**Simple Summary:** Age-at-death is of paramount importance in forensic analysis of skeletal remains. In addition to sex, stature, and population affinity, it constitutes baseline information in the identification process of deceased individuals. Despite its long tradition, in anthropological research age-at-death estimation poses many challenges and unanswered questions. It is undisputedly among the most difficult tasks of the forensic anthropologist and its results are often subject to a lackluster performance. In this study, we assessed computationally the efficiency of a holistic approach to skeletal age estimation based on a new proposal for macroscopic examination and the use of machine learning-based models for data analysis. Our results suggest that this approach is key for accurate and efficient age-at-death estimation based on skeletal remains analysis.

**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-atestimation, 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.

**Keywords:** forensic anthropology; age-at-death estimation; machine learning; neural networks
