Through an AI's Looking Glass: Discovering Dental Sexual Dimorphism with Explainable AI

Abstract

We propose a framework that extends explainable AI (XAI) from per-instance interpretation to dataset-wide knowledge discovery in dental morphology. A deep convolutional neural network was trained on 5,132 panoramic radiographs from 2,778 individuals to predict sex. Using OPG-SHAP, a domain-specific XAI method, we identified influential oral parts and validated them with statistical analysis. The upper canine was the most influential region, with females showing a significantly higher width-to-height ratio (0.391) than males (0.347), aligning with existing literature. Additionally, the upper third molar emerged as a novel sexually dimorphic feature, with males showing a higher ratio (1.064) than females (1.036). Both differences were statistically significant (p<0.001). Our results demonstrate how interpretable AI can rediscover known anatomical patterns and reveal new insights, enabling clinically meaningful knowledge extraction from neural networks.