Comparison of the efectiveness of sex estimation from cranial morphological traits by an expert and a machine learning algorithm
Year: 2026, volume 22 Issue: №2 Pages: 264-272
Heading: Anatomy and anthropology Article type: Original article
Authors: Solovyev K.V., Gaivoronsky I.V., Shirobokov I.G., Zaichenko A.A.
Organization: The Almazov National Medical Research Centre, Head of Second Cerebral and Spinal Cord Tumors Department, Doctor of Medical Sciences, St. Petersburg State University, Peter the Great Museum of Anthropology and Ethnography (Kunstkamera), Saint-Petersburg, Russia, Kirov Military Medical Academy, Saint Petersburg, Russia
Heading: Anatomy and anthropology Article type: Original article
Authors: Solovyev K.V., Gaivoronsky I.V., Shirobokov I.G., Zaichenko A.A.
Organization: The Almazov National Medical Research Centre, Head of Second Cerebral and Spinal Cord Tumors Department, Doctor of Medical Sciences, St. Petersburg State University, Peter the Great Museum of Anthropology and Ethnography (Kunstkamera), Saint-Petersburg, Russia, Kirov Military Medical Academy, Saint Petersburg, Russia
Summary:
Objective: to assess the comparative efectiveness of two approaches to sex estimation: visual expert assessment and the cranial morphological scoring method proposed by A. Klales and S. Cole. Material and methods. The study examined 159 adult skulls of documented sex. The material comprised two cranial series from the collection of the Kirov Military Medical Academy. Five ordinal cranial traits (glabella, nuchal crest, mastoid process, supraorbital margin, and mental eminence) were scored according to the Klales and Cole method, to which original additions were proposed. The resulting scores were used both for expert assessment and for algorithmic classifcation employing a random forest model implemented in MorphoPASSE. The data obtained was interpreted by experts and using the MorphoPASSE program. Results. The most informative feature is the glabella (84% accuracy), followed by the mastoid process (75%), the supraorbital margin (68%), the chin elevation (68%) and the external occipital protrusion (67%). In both series, higher accuracy rates were recorded on male turtles (up to 97.6%). The most accurate estimates of determining the sex of female skulls (up to 85.1%) were achieved using a machine learning algorithm. The accuracy of the expert assessment and the MorphoPASSE program in the total sample was 83.6%. Conclusion. The original additions of the Kleils-Cole methodology proposed by us increase the accuracy of expert assessments of female skulls by 12%. The results obtained allow us to conclude that the MorphoPASSE program can be used in modern studies of the skulls of the Eastern European population.
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