Coupling geometric morphometrics and machine learning for mandibular sex estimation in Late Pleistocene and Late Modern populations.

ABSTRACT: Accurate sex estimation is crucial for studying both modern and ancient human populations, yet methods are often limited to well-preserved skeletons. Here, we combine Geometric Morphometrics (GM) and Machine Learning (ML) to assess mandibular sexual dimorphism and classify sex across a wide chronological and geographic range to bracket the potential of this approach. Sixty-seven individuals from the modern, identified Luis Lopes collection (Portugal) and 18 Late Pleistocene individuals from Jebel Sahaba (Sudan) were surface scanned. Anatomical landmark coordinates were extracted and analyzed with GM, and ML models were trained on a subset of the modern sample to predict sex in both the remaining modern individuals and the Late Pleistocene specimens. GM revealed significant sexual dimorphis

SUBMITTER: Godinho RM 

PROVIDER: S-EPMC12804928 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature