Forensic dental age estimation with deep learning: a modified xception model for panoramic X-Ray images
This study represents a significant advancement in forensic age estimation from OPG images, emphasizing the potential of deep learning models, particularly the "Forensic Xception" model, in accurately classifying individuals based on age, especially in legal contexts. This research suggests a promising avenue for further advancements in forensic dental age estimation, with future studies encouraged to explore additional datasets, refine models, and address ethical and legal considerations.
Authors
- Bengi Öztaş (ORCID: https://orcid.org/0000-0002-1895-051X)
- Cansu Görürgöz (ORCID: https://orcid.org/0000-0002-3083-1660)
- Ercüment Yılmaz (ORCID: https://orcid.org/0000-0002-3712-7086)
- Hatice Cansu Kış (ORCID: https://orcid.org/0000-0003-4956-7537)
- Emin Murat Canger (ORCID: https://orcid.org/0000-0002-0798-9355)
Institutions
- Bursa Uludağ Üni̇versi̇tesi̇ (TR)
- Ankara University (TR)
- Karadeniz Technical University (TR)
- Tokat Gaziosmanpaşa Üniversitesi (TR)
- Software Research and Development Consulting (TR)
- Erciyes University (TR)
Publication Details
- Journal
- Forensic Science Medicine and Pathology
- Published
- 2025-02-12
- DOI
- https://doi.org/10.1007/s12024-025-00962-4
- Citations
- 5
- Primary Topic
- Forensic Anthropology and Bioarchaeology Studies
- Type
- article
- Field-Weighted Citation Impact
- 41.36
Funders
- Erciyes Üniversitesi
- Karadeniz Teknik Üniversitesi