Modern Methods of Sex Estimation from Human Postcranial Bones. A Critical Review

Abstract

Introduction. Sex determination of individuals fr om postcranial skeletal bones is a significant task in paleoanthropology and forensic medicine, particularly when skull and pelvic bones are fragmented or absent. The traditional osteometric approach, based on sexual dimorphism in size characteristics, requires consideration of population specificity and is continuously being refined. The aim of this review is to systematize and analyze modern methods for sex determination based on osteometric data from postcranial skeletal bones.

Materials and Methods. This review analyzes contemporary scientific publications dedicated to methods of sex determination from the postcranial skeleton. Three main methodological approaches are considered: methods based on univariate statistics (analysis of individual metric traits), methods of multivariate statistics (discriminant analysis, logistic regression), as well as modern technologies, including the use of computed tomography (CT) data for 3D reconstructions and the application of machine learning algorithms (specifically, deep learning) for bone image analysis.

Results and discussion. Univariate methods retain practical value due to their simplicity of application and suitability for working with fragmented material, although their accuracy is typically lower than that of multivariate methods. Multivariate statistical models, which account for a complex of interrelated traits, ensure higher accuracy in sex determination. A key factor influencing the reliability of any method is its population specificity—applying models developed for one population to another leads to a significant reduction in accuracy. The integration of CT data and artificial intelligence methods opens new prospects for automation, increased objectivity, and the discovery of new diagnostic features.

Conclusion. Modern methods for sex determination from the postcranial skeleton constitute an evolving toolkit, wh ere traditional univariate approaches are effectively complemented by complex multivariate models and innovative technologies. To ensure high result reliability, the development and validation of population-specific standards are necessary. The future of the field is linked to the further integration of 3D visualization and machine learning methods, which will enhance the accuracy, speed, and objectivity of expert identification.

Acknowledgements. The study was conducted under the state assignment of Lomonosov Moscow State University.

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