Automated age-at-death estimation from 3D surface scans of the facies auricularis of the pelvic bone.

This work presents an automated data-mining model for age-at-death estimation based on 3D scans of the auricular surface of the pelvic bone. The study is based on a multi-population sample of 688 individuals (males and females) originating from one Asian and five European identified osteological collections. Our method requires no expert knowledge and achieves similar accuracy compared to traditional subjective methods. Apart from data acquisition, the whole procedure of pre-processing, feature extraction and age estimation is fully automated and implemented as a computer program. This program is a part of a freely available web-based software tool called CoxAGE3D. This software tool is available at https://coxage3d.fit.cvut.cz/ Our age-at-death estimation method is suitable for use on individuals with known/unknown population affinity and provides moderate correlation between the estimated age and actual age (Pearson's correlation coefficient is 0.56), and a mean absolute error of 12.4 years.

The computational age-at-death estimation from 3D surface models of the adult pubic symphysis using data mining methods.

Age-at-death estimation of adult skeletal remains is a key part of biological profile estimation, yet it remains problematic for several reasons. One of them may be the subjective nature of the evaluation of age-related changes, or the fact that the human eye is unable to detect all the relevant surface changes. We have several aims: (1) to validate already existing computer models for age estimation; (2) to propose our own expert system based on computational approaches to eliminate the factor of subjectivity and to use the full potential of surface changes on an articulation area; and (3) to determine what age range the pubic symphysis is useful for age estimation. A sample of 483 3D representations of the pubic symphyseal surfaces from the ossa coxae of adult individuals coming from four European (two from Portugal, one from Switzerland and Greece) and one Asian (Thailand) identified skeletal collections was used. A validation of published algorithms showed very high error in our dataset-the Mean Absolute Error (MAE) ranged from 16.2 and 25.1 years. Two completely new approaches were proposed in this paper: SASS (Simple Automated Symphyseal Surface-based) and AANNESS (Advanced Automated Neural Network-grounded Extended Symphyseal Surface-based), whose MAE values are 11.7 and 10.6 years, respectively. Lastly, it was demonstrated that our models could estimate the age-at-death using the pubic symphysis over the entire adult age range. The proposed models offer objective age estimates with low estimation error (compared to traditional visual methods) and are able to estimate age using the pubic symphysis across the entire adult age range.

Age estimation of adult human remains from hip bones using advanced methods.

The assessment of age-at-death is an important and challenging part of investigations of human skeletal remains. The main objective of the present study was to apply different mathematical approaches in order to reach more accurate and reliable results in age estimation. A multi-ethnic dataset (n=941) of evaluated age-related changes on the pubic symphysis and the auricular surface of the hip bone was used. Two research groups examined nine different mathematical approaches. The best results were reached by Multi-linear regression, followed by the Collapsed regression model, with MAE values of 9.7 and 9.9 years, respectively, and with RMSE values of 12.1 and 12.2, respectively. The mean accuracy of decision tree models ranged between 30.7% and 72.3%, with the model using only the PUSx indicator performing the best. Moreover, our results indicate that the limiting factor of age estimation can be the visual evaluation of age-related changes. Further research is required to objectify the proposed methods for estimating age.

Novel age estimation model based on development of permanent teeth compared with classical approach and other modern data mining methods.

In order to analyze and improve the dental age estimation in children and adolescents for forensic purposes, 22 age estimation methods were compared to a sample of 976 orthopantomographs (662 males, 314 females) of healthy Czech children and adolescents aged between 2.7 and 20.5 years. All methods are compared in terms of the accuracy and complexity and are based on various data mining methods or on simple mathematical operations. The winning method is presented in detail. The comparison showed that only three methods provide the best accuracy while remaining user-friendly. These methods were used to build a tabular multiple linear regression model, an M5P tree model and support vector machine model with first-order polynomial kernel. All of them have mean absolute error (MAE) under 0.7 years for both males and females. The other well-performing data mining methods (RBF neural network, K-nearest neighbors, Kstar, etc.) have similar or slightly better accuracy, but they are not user-friendly as they require computing equipment and the implementation as computer program. The lowest estimation accuracy provides the traditional model based on age averages (MAE under 0.96 years). Different relevancy of various teeth for the age estimation was found. This finding also explains the lowest accuracy of the traditional averages-based model. In this paper, a technique for missing data replacement for the cases with missing teeth is presented in detail as well as the constrained tabular multiple regression model. Also, we provide free age prediction software based on this wining model.