Sex estimation based on odontometry of maxillary anterior teeth and arch dimensions predicted by machine learning algorithms

Abstract Estimating sex is a critical step in the identification of unknown human remains, reducing the pool of potential matches by approximately 50%. Among the anatomical structures used for this purpose, human teeth hold particular value due to their structural durability and pronounced sexual dimorphism, making them a preferred focus of forensic research. In recent years, intraoral scanners and machine learning algorithms have emerged as powerful tools in forensic investigations, offering high accuracy and efficiency. This study integrates these technologies to estimate sex from measurements of the upper six anterior teeth and maxillary arch dimensions. Linear measurements of maxillary anterior teeth and upper arch dimensions were obtained from digital impressions of 100 male and 100 female subjects using 3D Slicer software. These features were analyzed using six different machine learning models to predict sex. The cervico‐to‐cusp tip linear measurements of the right and left canines demonstrated the highest discriminative power, with area under the curve values of 0.968 and 0.947, respectively. Among the machine learning models tested, the Support Vector Classifier achieved the highest mean prediction accuracy of 94.5% as estimated by nested cross‐validation. This methodology shows strong potential for accurate sex estimation in forensic contexts. Further research with larger, more diverse samples is recommended to validate and enhance the generalizability of these findings.