Evaluating machine learning and neural network architectures for forensic sex estimation using mandibular ramus and notch features on panoramic radiographs

Estimating a biological profile, such as sex, is a fundamental step in forensic identification when primary identifiers are unavailable for direct individual comparison. In forensic scenarios involving advanced decay, specific taphonomic alterations, or midfacial blunt force impacts, the mandibular ramus serves as a valuable anatomical marker due to its distinct sexual dimorphism and thick cortical structure, making it more resilient to fragmentation than other, more fragile facial bones.

Despite its utility, the application of machine learning (ML) and artificial neural networks (ANN) to evaluate mandibular measurements in Indonesian populations remains limited by sample size constraints and predictive variability. This study analyzed three continuous vertical parameters (condylar-ramus height, projective ramus height, and coronoid-ramus height) and one categorical morphological variable (sigmoid notch) from 1,000 digital panoramic radiographs of Indonesian individuals, employing ten ML algorithms and three ANN architectures.