Metric analysis of the patella for sex estimation in a Portuguese sample

The biological profile estimation is the first step toward positive identification. However, it is not always possible to access a complete and well-preserved skeleton due to postmortem damage and taphonomic changes. As such, there is a need to develop new alternatives to analyze different bones of the human skeleton. The present study aims to analyze the patellar osteometry, with attention to its degree of sexual dimorphism, to establish a simple method for estimating sex in Portuguese adults. Six measurements were taken from 222 patella pairs, including 117 females and 105 males from the XXI Century Identified Skeleton Collection of the University of Coimbra. Subsequently, this method was validated in a different sample of 50 individuals equally representing both sexes. Maximum height stands out with a 77.0% of correct sex estimation, reaching 98.0% when applied to the new sample. The linear discriminant function analysis containing all the six variables showed the best results, with 80.2% of correct classification after cross-validation and 96.0% when applied to the independent sampleinfo:eu-repo/semantics/publishedVersio

Estimativa multifatorial da idade-à-morte em antropologia e medicina forense: uma abordagem com "machine learning"

Tese de Doutoramento em Antropologia apresentada à Faculdade de Ciências e TecnologiaA estimativa da idade à morte é uma etapa crucial no processo de identificação de restos humanos esqueléticos. No entanto, em indivíduos adultos, a sua avaliação é particularmente difícil de ser realizada com precisão devido à variabilidade dos processos de senescência. A literatura defende uma abordagem multifatorial para avaliação da idade esquelética de modo a obter estimativas mais exatas e precisas. Esta perspetiva pode ser conceptualmente argumentada como a mais eficaz, porque os diversos marcadores esqueléticos e a sua relação com a idade exibem diferentes trajetórias. Todavia, na estimativa da idade à morte em adultos prevalecem inconsistências metodológicas, uma vez que as técnicas que usam diversos marcadores esqueléticos resumem-se às suturas cranianas e às articulações pélvicas. Têm sido sugeridos procedimentos mais genéricos, mas com pouca utilização em contexto pericial porque requerem técnicas de seriação ou conhecimento matemático avançado. A presente tese procurou solucionar alguns dos problemas da estimativa da idade em adultos por meio de análise macroscópica tendo em conta diversos marcadores osteológicos. O principal objetivo foi desenvolver um novo método para estimativa de idade à morte em adultos usando uma abordagem interdisciplinar que une a antropologia forense e a inteligência artificial. Do ponto de vista antropológico foi proposta uma nova técnica para avaliação macroscópica que incorpora um total de 64 marcadores esqueléticos que cobrem as principais articulações e complexos músculo-esqueléticos, integrando marcadores bem estabelecidos com outros menos explorados. Para estabelecer um novo conjunto de dados de referência, foi estudada uma amostra composta por 500 esqueletos identificados provenientes de duas coleções osteológicas da Universidade de Coimbra (19-101 anos, 250 homens e 250 mulheres). Neste trabalho foi implementada e validada uma nova abordagem computacional tendo por base técnicas de inteligência artificial, concretamente redes neuronais artificiais profundas aleatorizadas. Nesta metodologia a estimativa da idade é tratada como um problema de regressão para a estimativa pontual e intervalar. Foram assim conduzidas computacionalmente duas experiências para testar o seu valor: na primeira, comparam-se modelos multivariados com modelos clássicos usando apenas regiões anatómicas específicas; na segunda avaliou-se a precisão da estimativa de idade a partir de modelos multivariados fracionados, que usam apenas uma parte das características esqueléticas escolhidas aleatoriamente. Os resultados com base na análise de validação cruzada, demonstram que a estimativa de idade a partir de remanescentes osteológicos pode ser inferida com precisão em toda a faixa etária adulta incluindo indivíduos com idade muito avançada, reduzindo erro médio absoluto de para seis anos aproximadamente. Praticamente todas as combinações aleatórias de marcadores ósseos resultaram em modelos com desempenho comparável ou superior ao dos modelos construídos de regiões anatómicas específicas, diminuindo em duas a seis vezes o erro médio absoluto e o viés de estimativa em comparação com os modelos padrão. Estes resultados reforçam a importância de uma análise multifatorial na estimativa da idade em adultos. Foi desenvolvido um novo software, DRNNAGE, para implementar o método supramencionado. O software possui uma interface intuitiva e é distribuído gratuitamente sob uma licença de código aberto.Age-at-death assessment is a crucial step in the identification process of human skeletal remains. Nonetheless, in adult individuals this task is particularly difficult to achieve accurately due to the variability of the senescence processes. The literature argues in favor of a multifactorial approach to skeletal age assessment to obtain precise estimates. Conceptually a multifactorial perspective can be argued as the most effective approach because skeletal traits display different age-related trajectories and onsets. However, adult age estimation struggles with methodological inconsistencies. Techniques that use multiple skeletal indicators are often limited to the cranial sutures and the pelvic joints. More generic procedures for multifactorial analysis have also been proposed, but with poor adoption in forensic casework because they require seriation or advanced mathematical knowledge to be put into action.The present thesis aimed to lay a foundation to tackle some of the challenges of macroscopic adult skeletal age estimation, especially in its holistic or multifactorial aspect. The main objective of this work was to propose a new method for multifactorial age estimation using an interdisciplinary approach bridging anthropology and computer science. From an anthropological perspective, a novel macroscopic technique for skeletal analysis was developed. This proposal incorporates a total of 64 skeletal traits covering major joints and musculoskeletal complexes, integrating well established age-related markers with less explored ones. A dataset comprising information on 500 identified skeletons was used to establish a reference dataset (19–101 years old, 250 males and 250 females) for adult age estimation. A computational framework based on machine learning using randomized deep neural networks was implemented and validated. This approach tackled age estimation from a function approximation perspective as regression problem to infer both point and prediction interval estimates. Two experiments were conducted computationally to assess the value of the multifactorial approach: the first experiment compared multi-trait or multifactorial models against classic models using specific anatomical regions or skeletal traits only; the second experiment assessed the accuracy of age estimation from fractioned multifactorial models using randomly chosen traits. Based on cross-validation analysis, results demonstrate that age estimation from skeletal remains can be accurately inferred across the entire adult age span, approximately with 6 years mean absolute error. Informative estimates and prediction intervals can be obtained for the elderly population. Multifactorial models introduce a two-to-six-fold reduction in the mean absolute error and prediction bias compared to standard models. Virtually every combination of random traits resulted in models with comparable or better performance than the models built of specific anatomic regions as traditionally encounter in macroscopic age estimation methods. This finding supports the value of multifactorial age estimation over methods the focus solely on a single anatomical structure. A novel software, DRNNAGE, was built to operationalized and integrate the new method proposed in this thesis, providing an intuitive interface and freely distributed under an open-source license.Outro - Bolsa de Doutoramento, Fundação para a Ciência e a Tecnologia (SFRH/BD/99676/2014

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

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.</jats:p

Eigenfemora&mdash;Age-at-Death Estimation in the Proximal Femur through an Image Processing Approach

Estimating age at death is essential to establish biological profiles from human skeletal remains in both forensic and archeological settings. Imaging studies of skeletal age changes in adults have described the metamorphosis of trabecular bone structure and bone loss in the proximal femur as well as changes in morphology during different stages of life. This study aims to assess the utility of a digital representation of conventional X-ray films of the proximal femur for the estimation of age at death in a sample of 91 adult individuals (47 females and 44 males) of the Coimbra Identified Skeletal Collection. The proposed approach showed a root mean squared error (RMSE) of 17.32 years (and mean absolute error of 13.47 years) for females and an RMSE of 14.06 years (mean absolute error of 11.08 years) for males. The main advantage of this approach is consistency in feature detection and extraction, as X-ray images projected on the femora space will always produce the same set features to be analyzed for age estimation, while more traditional methods rely heavily on operator experience that can lead to inconsistent age estimates among experts

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

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

SAMS: a decisional tool for selecting and evaluating sub-adult age estimation methods

International audienc