Revisiting Age Estimation on Panoramic Dental Images

• Julian Liang UFBA
• Patricia Cury UFBA
• Luciano Oliveira UFBA

Resumo

Forensic dentistry has traditionally relied on bone or dental indicators, primarily utilizing dental radiographs, for age estimation. However, limited research has been conducted on automatic age estimation on panoramic images, needing a reeval-uation of the existing methodologies to assess the performance of computer-based methods. This study proposes to revisit the analysis of age estimation methods using panoramic dental radio-graphs. We have curated the largest publicly available dataset of panoramic dental images, encompassing diverse dental conditions and age ranges. Specifically, our study focuses on evaluating three distinct classes of deep-learning architectures: ViT, ConvNeXt-V2, and EfficientNets, employing a comprehensive to assess their performances that better favor reproducibility. By comparing our approach with existing studies in the literature, we offer valuable insights for forensic investigations in the field of age estimation.