Journal Article

Evaluation of Sex-Related Morphometry of the Sella Turcica in Autopsy Using Machine Learning

Yılmaz, Humeyra, Depreli, Ahmet, Bakan, Huseyin Ugur, Sahin, Necati Emre, Nasip, Omer Faruk, Ozturk, Mustafa Furkan

2026

Overview

Background/Objectives: The sella turcica is a key anatomical landmark due to its close relationship with the pituitary gland and surrounding structures. This study aimed to compare morphometric characteristics of the sella turcica in autopsy cases according to sex and to develop machine learning (ML)-based sex estimation models using these measurements. Methods: This study included 230 individuals (115 males, 115 females). Sella turcica morphometric measurements (length, depth, anteroposterior, and transverse diameters) were analyzed. In addition, associations with age, height, and weight were evaluated. Sex differences and correlations were assessed using non-parametric tests. Generalized additive models were applied to evaluate non-linear effects of height and weight, and ML algorithms (LR, RF, SVM, XGBoost) were used for sex classification with 10-fold cross-validation. Results: Data from 230 individuals (115 males, 115 females) were analyzed. All sella turcica dimensions were significantly greater in males (p < 0.001). Height showed strong positive correlations with all measurements, whereas weight showed weaker associations and age was not significant. Generalized additive models demonstrated high performance for length and anteroposterior diameter (adjusted R2 ≈ 0.956), high performance for transverse diameter (R2 = 0.936), and moderate performance for depth (R2 = 0.700), with significant non-linear effects of height and weight. ML models achieved high accuracy (>95%), with SVM performing best (accuracy: 0.991; AUC: 0.997), and transverse diameter identified as the most important predictor. Conclusions: Sella turcica morphometry demonstrates strong sexual dimorphism and is primarily influenced by body size parameters, particularly height. Non-linear modelling approaches such as GAM effectively capture complex anatomical relationships, while ML models, especially SVM, provide promising sex estimation. Among all variables, transverse diameter emerges as the most robust and consistent predictor, highlighting its potential utility in forensic and anthropological applications.

• Publisher: MDPI AG,Multidisciplinary Digital Publishing Institute (MDPI)
• Subject: Algorithms / Autopsies / Business metrics / Data integrity / Datasets / Females / Forensic anthropology / Forensic medicine / Human remains / Investigations / Machine learning / Males / Morphology / Research ethics / sella turcica / sex estimation / Statistical power / Tomography / Variables