Estimating sex using discriminant analysis of mandibular measurements from a modern Greek sample

Classification performance of the Sella-Tunis et al. (2017) sex estimation method in Czech population: different posterior probability threshold approaches

In this study we tested classification performance of a sex estimation method from the mandible originally developed by Sella-Tunis et al. (2017) on a heterogeneous Israeli population. Mandibular linear dimensions were measured on 60 CT scans derived from the Czech living population. Classification performance of Israeli discriminant functions (DFs-IL) was analyzed in comparison with calculated Czech discriminant functions (DFs-CZ) while different posterior probability thresholds (currently discussed in the forensic literature) were employed. Our results comprehensively illustrate sensitivity of different discriminant functions to population differences in body size and degree of sexual dimorphism. We demonstrate that the error rate may be biased when presented per posterior probability threshold. DF-IL 1 showed least sensitivity to population origin and fulfilled criteria of sufficient classification performance when applied on the Czech sample with a minimum posterior probability threshold of 0.88 reaching overall accuracy ≥ 95%, zero sex bias, and 80% of classified individuals. The last parameter was higher in DF-CZ 1 which was the main difference between those two DFs suggesting relatively low dependance on population origin. As the use of population-specific methods is often prevented by complicated assessment of population origin, DF-IL 1 is a candidate for a sufficiently robust method that could be reliably applied outside the reference sample, and thus, its classification performance deserves further testing on more population samples.

Comparison of mandibular morphometric parameters in digital panoramic radiography in gender determination using machine learning

OBJECTIVE

This study aimed to evaluate the usability of morphometric features obtained from mandibular panoramic radiographs in gender determination using machine learning algorithms.

MATERIALS AND METHODS

High-resolution radiographs of 200 patients aged 20-77 (41.0 ± 12.7) were included in the study. Twelve different morphometric measurements were extracted from each digital panoramic radiography included in the study. These measurements were used as features in the machine learning phase in which six different machine learning algorithms were used (k-nearest neighbor, decision trees, support vector machines, naive Bayes, linear discrimination analysis, and neural networks). To evaluate the reliability, we have performed tenfold cross-validation and we repeated this 10 times for every classification process. This process enhances the reliability of the results for other datasets.

RESULTS

When all 12 features are used together, the accuracy rate is found to be 82.6 ± 0.5%. The classification accuracies are also compared using each feature alone. Three features that give the highest accuracy are coronoid height (80.9 ± 0.9%), condyle height (78.2 ± 0.5%), and ramus height (77.2 ± 0.4%), respectively. When compared to the classification algorithms, the highest accuracy was obtained with the naive Bayes algorithm with a rate of 84.0 ± 0.4%.

CONCLUSION

Machine learning techniques can accurately determine gender by analyzing mandibular morphometric structures from digital panoramic radiographs. The most precise results are achieved by evaluating the structures in combination, using attributes obtained from applying the MRMR algorithm to all features.

Size and shape of human mandible: Sex differences and influence of age on sex estimation accuracy

The degree of sexual dimorphism expressed by human bones is of primary importance for the development of accurate methods for sex estimation. The objective of the present study was to investigate sex differences in shape and size of the mandible using geometric morphometric methods. The study also aimed to examine the impact of age on the sex classification ability of the size and shape of the mandible. Computed tomography images of 190 Bulgarians (98 males and 92 females) were used in the study. Polygonal surface models of the skulls were generated and used for digitizing 45 landmarks located on the mandible. The raw three-dimensional coordinates of the landmarks were processed via generalized Procrustes superimposition. The sex differences in mandibular size and shape were evaluated for statistical significance. Multivariate regression was applied for correction of the allometric effect. Principal component analysis, discriminant analysis, and canonical variate analysis were also used in the study. Mandibular size differed significantly between males and females and achieved a sex classification accuracy of 87%. The significance of the sex differences in mandibular shape depended on the type of shape variables used in the analysis. The shape variables provided different classification accuracy: 78% using the Procrustes coordinates and 53% using the regression residuals. The male and female mandibles differed significantly in size and shape, including the allometric component. Mandibular size is a more effective sex indicator than shape. Age has an ambiguous effect on the classification accuracy of the size and shape variables of the mandible.

Advanced procedures for skull sex estimation using sexually dimorphic morphometric features

This paper introduces an automated method for estimating sex from cranial sex diagnostic traits by extracting and evaluating specialized morphometric features from the glabella, the supraorbital ridge, the occipital protuberance, and the mastoid process. The proposed method was developed and evaluated using two European population samples, a Czech sample comprising 170 crania reconstructed from anonymized CT scans and a Greek sample of 156 crania from the Athens Collection. It is based on a fully automatic algorithm applied on 3D models for extracting sex diagnostic morphometric features which are further processed by computer vision and machine learning algorithms. Classification accuracy was evaluated in a population specific and a population generic 2-way cross-validation scheme. Population-specific accuracy for individual morphometric features ranged from 78.5 to 96.7%, whereas population generic correct classification ranged from 71.7 to 90.8%. Combining all sex diagnostic traits in multi-feature sex estimation yielded correct classification performance in excess of 91% for the entire sample, whereas the sex of about three fourths of the sample could be determined with 100% accuracy according to posterior probability estimates. The proposed method provides an efficient and reliable way to estimate sex from cranial remains, and it offers significant advantages over existing methods. The proposed method can be readily implemented with the skullanalyzer computer program and the estimate_sex.m GNU Octave function, which are freely available under a suitable license.