SexEst
An open access web application for metric skeletal sex estimation
Bibliographic Data
| ID | 4724754 |
|---|---|
| Authors | Chryso Constantinou (0000-0002-9327-9834, Computation‐Based Science and Technology Research Center The Cyprus Institute Nicosia Cyprus, corresponding author), E Nikita (0000-0003-2094-5047, Science and Technology in Archaeology and Culture Research Center The Cyprus Institute Nicosia Cyprus) |
| Year | 2022 |
| Volume | 32 |
| Issue | 4 |
| Pages | 832-844 |
| Publication date | 2022-07-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Osteoarchaeology (JOURNAL) |
| Journal identifiers | ISSN: 1047-482X • E-ISSN: 1099-1212 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/oa.3109 |
| OpenAlex | W4224110539 |
| Language | EN |
| Citations received | 4 |
| References cited | 60 |
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
Biology · Gradient boosting · Interpretability · Linear discriminant analysis · Machine learning · Postcrania · Random forest · Computer Science · Engineering · Forensic and Genetic Research · Forensic Anthropology and Bioarchaeology Studies · Paleopathology and ancient diseases · Artificial Intelligence
Testing the accuracy of the SexEst software for sex estimation in a modern Greek sample
Skeletal Sex Estimation for Human Remains From Archaeological Contexts
Assessing inter-observer reliability to support availability of osteometric measurement data from the Olivier collection
On the use of collections with unreliably determined sex and age characteristics in model train-ing for sex determination by traits of the standard craniometric program
Sexually dimorphic pelvic morphology in South African whites and blacks
Sexual dimorphism and discriminant function sexing in indigenous South African crania
Sex Estimation in Forensic Anthropology
Sexual dimorphism in modern Japanese crania
Sex estimation of upper long bones by selected measurements in a Radom (Poland) population from the 18th and 19th centuries AD
Bioarchaeology
Stature and body mass estimation from skeletal remains in the European Holocene
Human body mass estimation
Validation and reliability of the sex estimation of the human os coxae using freely available DSP2 software for bioarchaeology and forensic anthropology
Sexual dimorphism in human cranial trait scores
Reliability test of the visual assessment of cranial traits for sex determination
Greater sciatic notch morphology
Sex determination of human skeletal populations using latent profile analysis
Sexual dimorphism in the size and shape of the os coxae and the effects of microevolutionary processes
Limb bone bilateral asymmetry
Sexual determination of long bones in recent Japanese
CalcTalus
New random generalized linear model for sex determination based on cranial measurements
Computational Tools in Forensic Anthropology
| Unique citing works | 4 |
|---|---|
| Citations per year | 1,33 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 4 |