Hirunchavarod N.Sributsayakarn N.Pornprasertsuk-Damrongsri S.Jirarattanasopha V.Intharah T.Mahidol University2026-03-202026-03-202025-01-01Proceedings 2025 International Conference on Digital Image Computing Techniques and Applications Dicta 2025 (2025)https://repository.li.mahidol.ac.th/handle/123456789/115791We 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.Computer ScienceConference PaperSCOPUS10.1109/DICTA68720.2025.113024582-s2.0-105032534850