AI-driven differentiation of 2D proximal femur morphometry and hounsfield units for integrated forensic estimation of sex, stature, and age in a modern Thai dry bone sample.

Computed Tomography (CT) imaging has expanded possibilities for biological profile estimation in forensic contexts. This study examined whether two-dimensional (2D) morphometric measurements and Hounsfield Unit (HU) values derived from CT scans of dry proximal femora could reliably estimate sex, stature, and age, and whether machine learning (ML) could meaningfully improve on traditional methods. Three hundred left femora from Thai individuals were scanned, and mid-coronal sections were used to extract measurements from defined anatomical regions. For sex estimation, conventional estimation equations reached 93.2% accuracy, while Naïve Bayes classification achieved 96.5% as the best performance among the ML models tested. Stature estimation using sex-specific 2D parameters yielded a Standard Error of Estimate (SEE) of 4.43 cm, which dropped to 3.96 cm when Support Vector Machines (SVM) and Gaussian Process Regression (GPR) were applied. Age estimation relied on HU values, which showed a consistent negative relationship with age. The lowest SEE for age was 9.67 years from measurements at the Primary Tensile Line (PTL) and Ward's Triangle in females. Models also performed better when applied to older age groups. Although sex-specific equations outperformed mixed-sex ones, the latter were kept in the analysis as a practical alternative when sex cannot be established prior to analysis. Overall, 2D morphometrics proved most useful for sex and stature estimation, while HU values emerged as a reliable, quantitative approach to age estimation. ML consistently improved model performance across all three estimation tasks, supporting its role in modern forensic anthropological practice.