Machine Learning-Based Sex Estimation from Computed Tomography-Derived Pelvic Morphometric Measurements in a...

Background/Objectives: Sex estimation is a fundamental component of the forensic biological profile, and the pelvis is widely regarded as the most sexually dimorphic skeletal element. This study aimed to quantify pelvic sexual dimorphism in a contemporary Turkish sample using computed tomography (CT)-derived morphometric measurements, and to evaluate the performance of several machine learning (ML) algorithms for sex classification. Methods: Fourteen pelvic measurements were obtained from CT reconstructions of 201 individuals (101 males, 100 females) by a single observer; a stratified subset of 30 cases (15 males, 15 females) was remeasured by the same observer and by a second observer to assess intra- and inter-observer reliability. Four supervised ML classifiers-logistic regression, linear discriminant analysis (LDA), random forest, and support vector machine (SVM)-were trained and evaluated using stratified 10-fold cross-validation and an independent 30% hold-out test set. Results: Thirteen of the fourteen measurements differed significantly between sexes (p < 0.05). Intra-observer reliability was excellent for all 14 measurements (ICC = 0.943-0.998), and inter-observer reliability was good to excellent (ICC = 0.834-0.996), with the lowest value observed for pubic length. Cross-validated classification accuracy ranged from 98.0% (random forest) to 99.0% (logistic regression, LDA, and SVM), with corresponding cross-validated AUC values of 0.991-0.994, with logistic regression, LDA, and SVM each achieving 100% accuracy (AUC = 1.000) on the independent hold-out set (random forest: 96.7%, AUC = 0.999). Acetabular width was identified as the single most informative predictor (univariate area under the curve = 0.972), followed by acetabular height, the subpubic angle, ischial length, the transverse pelvic outlet, and the angle of the greater sciatic notch. Conclusions: These findings demonstrate that CT-derived pelvic morphometrics, combined with machine learning classification, provide a highly accurate and reproducible method for sex estimation in a Turkish forensic context, and support the development of population-specific standards for forensic anthropological casework, pending independent external validation; the classification and effect-size estimates reported here were, however, stable under repeated cross-validation and hyperparameter optimisation.