SPINNE: An app for human vertebral height estimation based on artificial neural networks

Vilas-Boas, D.; Wasterlain, Sofia N.; Coelho, João d’Oliveira; Navega, David; Gonçalves, David

doi:10.1016/j.forsciint.2019.02.056

Cited by 5 publications

(1 citation statement)

References 52 publications

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“…To cope with the difficulties and needs of multifactorial age estimation, novel methods and techniques can be developed by resorting to statistical and machine learning, data science, and artificial intelligence tools and approaches. More than constantly evolving, machine learning, artificial intelligence and data science are ubiquitous, and have various successful applications within forensic anthropology in domains such as biological profiling or craniofacial identification [13,15,[37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

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

Navega

Costa

Cunha

2022

Biology

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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.

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