Estimation of age in forensic medicine using multivariate approach to image analysis

Kucheryavski, Sergey; Belyaev, I. A.; Fominykh, Sergey

doi:10.1016/j.chemolab.2008.07.011

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…Moreover, research in forensic anthropology often includes cadaveric remains with soft tissues. Thus, medical imaging techniques, such as DXA or X-ray computed tomography, are valuable to assess age at death in individuals not entirely skeletonized in which skeletal preparation is not practical, or culturally reasonable (38,(69)(70)(71)(72)(73)(74)(75). DXA has been uncommonly exploited in forensic anthropology; nonetheless, it can be used, not only to estimate age at death, but also to assess sex (76)(77)(78)(79).…”

Section: Discussionmentioning

confidence: 99%

DXAGE: A New Method for Age at Death Estimation Based on Femoral Bone Mineral Density and Artificial Neural Networks

Navega

Coelho

Cunha

et al. 2017

Journal of Forensic Sciences

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Age at death estimation in adult skeletons is hampered, among others, by the unremarkable correlation of bone estimators with chronological age, implementation of inappropriate statistical techniques, observer error, and skeletal incompleteness or destruction. Therefore, it is beneficial to consider alternative methods to assess age at death in adult skeletons. The decrease in bone mineral density with age was explored to generate a method to assess age at death in human remains. A connectionist computational approach, artificial neural networks, was employed to model femur densitometry data gathered in 100 female individuals from the Coimbra Identified Skeletal Collection. Bone mineral density declines consistently with age and the method performs appropriately, with mean absolute differences between known and predicted age ranging from 9.19 to 13.49 years. The proposed method-DXAGE-was implemented online to streamline age estimation. This preliminary study highlights the value of densitometry to assess age at death in human remains.

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Section: Discussionmentioning

confidence: 99%

DXAGE: A New Method for Age at Death Estimation Based on Femoral Bone Mineral Density and Artificial Neural Networks

Navega

Coelho

Cunha

et al. 2017

Journal of Forensic Sciences

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“…Therefore, it will be a waste of money and time. There are many reported methods that used bones for age estimation and they are subdivided into three main categories: image processing [29][30][31], by comparing with bone age atlas [32][33][34], and statistical regression analysis [35][36][37][38]. As being compared with the atlas, as the name proposes, X-ray image of the subjects are being made comparison with an atlas which contains a set of radiographs of identified gender and age.…”

Section: Results and Analysismentioning

confidence: 99%

Random forest age estimation model based on length of left hand bone for Asian population

Darmawan¹,

Abidin²,

Kasim³

et al. 2020

IJECE

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In forensic anthropology, age estimation is used to ease the process of identifying the age of a living being or the body of a deceased person. Nonetheless, the specialty of the estimation models is solely suitable to a specific people. Commonly, the models are inter and intra-observer variability as the qualitative set of data is being used which results the estimation of age to rely on forensic experts. This study proposes an age estimation model by using length of bone in left hand of Asian subjects range from newborn up to 18-year-old. One soft computing model, which is Random Forest (RF) is used to develop the estimation model and the results are compared with Artificial Neural Network (ANN) and Support Vector Machine (SVM), developed in the previous case studies. The performance measurement used in this study and the previous case study are R-square and Mean Square Error (MSE) value. Based on the results produced, the RF model shows comparable results with the ANN and SVM model. For male subjects, the performance of the RF model is better than ANN, however less ideal than SVM model. As for female subjects, the RF model overperfoms both ANN and SVM model. Overall, the RF model is the most suitable model in estimating age for female subjects compared to ANN and SVM model, however for male subjects, RF model is the second best model compared to the both models. Yet, the application of this model is restricted only to experimental purpose or forensic practice.

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“…All subsequent analyses were conducted in the segmented regions only. Previous approaches to age estimation from bone images or radiological images relied on the macroscopic observation or the calculation of first and second order statistics of pixel distribution [46]. The approach presented here is based on a construction of appearance-based model to code information from proximal femur images using Principal Components Analysis (PCA).…”

Section: Acquisition and Segmentationmentioning

confidence: 99%

Eigenfemora – Age at Death Estimation in the Proximal Femur through an Image Processing Approach

Navega,

Ferreira,

Curate

2023

Preprint

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Estimating age at death is essential to establish a biological profile from human skeletal remains in both forensic and archeological settings. Imaging studies of skeletal age changes in adults have described 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 the consistency in the 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 which can lead to inconsistent age estimates among experts.

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Estimation of age in forensic medicine using multivariate approach to image analysis

Cited by 14 publications

References 19 publications

DXAGE: A New Method for Age at Death Estimation Based on Femoral Bone Mineral Density and Artificial Neural Networks

DXAGE: A New Method for Age at Death Estimation Based on Femoral Bone Mineral Density and Artificial Neural Networks

Random forest age estimation model based on length of left hand bone for Asian population

Eigenfemora – Age at Death Estimation in the Proximal Femur through an Image Processing Approach

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