Metric sexual dimorphism of the skull in Thailand

Techataweewan, Nawaporn; Hefner, Joseph T.; Freas, Laurel; Surachotmongkhon, Nutthaphon; Benchawattananon, Rachadaporn; Tayles, Nancy

doi:10.1016/j.fsir.2021.100236

Cited by 4 publications

(4 citation statements)

References 33 publications

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“…These studies utilise X-ray images [ [98] , [99] , [100] , [101] , [102] ], MRI images [ 103 , 104 ], photography [ 105 ], and CT scans [ 56 , 102 , [106] , [107] , [108] ] of bony anatomy to either cluster or classify individuals’ age and biological sex. While the growing body of literature has presented ML algorithms with promising results, study sample sizes are often limited to below 500 individuals/scans [ 17 , 64 , 65 , 109 , 110 ]. Though small datasets may be sufficient for the training of machine learning algorithms, the generalisability, accuracy, validity, and reliability of developed ML and AI models can be greatly improved with larger high-quality datasets [ 111 ].The larger sample sizes that the pipeline is able to produce will allow ML and AI models to decrease variability in predictive accuracies and increase model robusticity [ 112 ].…”

Section: Discussionmentioning

confidence: 99%

Preparing computed tomography images for machine learning in forensic and virtual anthropology

Mariconti

Nakhaeizadeh

et al. 2023

Forensic Science International: Synergy

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Section: Discussionmentioning

confidence: 99%

Preparing computed tomography images for machine learning in forensic and virtual anthropology

Mariconti

Nakhaeizadeh

et al. 2023

Forensic Science International: Synergy

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“…A number of free web applications have been developed for sex estimation using the Coimbra Identified Skeletal Collection, such as CADOES that employs pelvic measurements (d'Oliveira Coelho & Curate, 2019), the Ammer‐Coelho application that focuses on the olecranon fossa of the humerus (Ammer et al, 2019), and CalcTalus that estimates sex based on measurements from the talus and calcaneus (Curate et al, 2021). Moreover, the SeuPF software estimates sex based on measurements of the proximal femur, using a reference sample from the Luís Lopes Collection (Portugal) (Curate et al, 2016), while the KKU Sex Estimation online tool focuses on craniometric sex estimation for Thai populations (Techataweewan et al, 2021). Finally, 3D‐ID employs geometric morphometric data in sex estimation based on the cranium, using as reference data landmark coordinates from various ancestral groups (Slice & Ross, 2009), COLIPR performs sex estimation also based on cranial landmarks or/and traditional cranial measurements (Urbanová et al, 2014), while SkullProfiler adopts elliptical Fourier analysis for sex estimation from lateral skull photographs based on American and Asian assemblages (Caple et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the SeuPF software estimates sex based on measurements of the proximal femur, using a reference sample from the Luís Lopes Collection (Portugal) (Curate et al, 2016), while the KKU Sex Estimation online tool focuses on craniometric sex estimation for Thai populations (Techataweewan et al, 2021). Finally, 3D-ID employs geometric morphometric data in sex estimation based on the cranium, using as reference data landmark coordinates from various ancestral groups (Slice & Ross, 2009), COLIPR performs sex estimation also based on cranial landmarks or/and traditional cranial measurements (Urbanová et al, 2014), while SkullProfiler adopts elliptical Fourier analysis for sex estimation from lateral skull photographs based on American and Asian assemblages (Caple et al, 2018).…”

mentioning

confidence: 99%

SexEst: An open access web application for metric skeletal sex estimation

Constantinou

Nikita

2022

Intl J of Osteoarchaeology

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Skeletal sex estimation is an essential step in any osteoarcheological study; hence, several metric and morphological methods have been developed for this purpose, employing different skeletal elements. This paper has a dual purpose: (1) test the performance of several machine learning classification models for skeletal sex estimation using worldwide samples of cranial and postcranial measurements and (2) present a free web application for the implementation of the models that exhibit the highest accuracy so that the sex of unknown skeletons can be straightforwardly estimated. Regarding the first objective, using the Goldman database of postcranial metrics and the William W. Howells craniometric database, machine learning classification models were constructed for sex prediction. The models were optimized with respect to their hyperparameters and cross‐validated reaching accuracies ranging from 80.8%–89.5% for the postcranial data and 81.2%–87.7% for the cranial data. The models offering the highest rates of correct sex classification (Extreme Gradient Boosting, Light Gradient Boosting, and Linear Discriminant Analysis) were then selected to construct an open access and open source web application, SexEst, for predicting the sex of unknown skeletons.

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“…While the morphological measurement is related to the observation of visual criteria [3]. Some parts of the body skeleton that are usually analyzed in determining sex are the pelvic [8][9][10][11], skull [12][13][14][15], mandible [16], cranial [17], femur [18][19][20][21], and tibia [22].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology

2022

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Sex classification is part of forensic anthropological identification aimed at determining whether the skeleton belongs to a male or a female. This paper exhibits the performance of the Support Vector Machine (SVM) in classifying the sex of the sacrum in forensic anthropology. Bone data was measured by the metric method based on six variables, namely superior breadth, anterior length, mid ventral breadth, real height, diameter the base, and max-transverse diameter of the base. This study shows performance analysis of SVM using the library libSVM with linear, polynomial, and RBF kernel to observe the results of the comparison of the accuracy of the kernel used. According to the results of the trials, the best accuracy was attained in each kernel function, i.e., the RBF kernel is 83.33% with g = 1 and C = 1, the polynomial is 85.56% at γ = 2, C = 2 and d =1, and the linear kernel obtained best accuracy is 84.44 % with C = 2 and C = 3. In conformity with the experimental result, polynomial attained the highest accuracy of 85.56% at γ = 2, C = 2, and d =1.

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Metric sexual dimorphism of the skull in Thailand

Cited by 4 publications

References 33 publications

Preparing computed tomography images for machine learning in forensic and virtual anthropology

Preparing computed tomography images for machine learning in forensic and virtual anthropology

SexEst: An open access web application for metric skeletal sex estimation

Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology

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