Monalisa, Natasha TanzilaSultana, ShirinAfrin, AnikaHimi, Shinthi Tasnim2026-08-122026-08-122026-01-01N. T. Monalisa, S. Sultana, A. Afrin and S. T. Himi, "A Hierarchical Age-Aware Framework for Dental Age Estimation," 2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN), Chittagong, Bangladesh, 2026, pp. 1-6, doi: 10.1109/QPAIN69676.2026.11546261.97983315499092-s2.0-105042911703https://hdl.handle.net/10361/28997Dental Age Estimation (DAE) is a critical component of forensic odontology, yet many existing models struggle with performance stability across varying data scales. This study proposes a novel DAE framework that synergistically combines a Hierarchical Age-Aware Machine Learning Pipeline with a Hybrid Feature Approach. The methodology integrates deep convolutional features from EfficientNetV2-S with handcrafted texture descriptors including HOG, LBP, and GLCM. Evaluated on the extensive IPCTI dataset, the model achieved a high test accuracy of 0.9206. However, comparative experiments on the smaller OPG-800 dataset revealed a significant performance drop to an accuracy of 0.6206. These results confirm that while the hybrid hierarchical approach offers superior performance for large-scale dental repositories, the efficacy of hierarchical classification boundaries is deeply dependent on data volume.6 pagesen-USfalseDental age estimationEfficientNetV2Forensic odontologyHierarchical classificationHybrid feature fusionDental anthropology.Artificial intelligenceConference Proceeding10.1109/QPAIN69676.2026.11546261