Abstract

Identification is a key aspect particularly in the cases involving decomposition, mutilation or in mass disasters. Teeth own resilience hence, serves as a reliable marker to serve as a remedy for the purpose of identification. The exploration of teeth with AI revolutionized conventional methods of identification with contemporary ones yielding automation and better accuracy. This review portrays the transformation of conventional/manual comparison procedures of dentition to AI driven architectures. Advantageous enabling of AI strengthens reproducibility, transparency and standardization. However, this review also showcases the challenges associated with teeth identification such as ethical concerns, diversity population and legal acceptance. Eventually, the convergence of computational intelligence with dentistry serves as a transformative era of tracing identity through technology

• 2.   Juvé-Prats O., Raga-Ferrer J., Valiente-López M., Mura-Pérez G. Advancements and challenges in dental forensics for disaster victim identification: A comprehensive review. Int J Disaster Risk Reduct. 2025; 103: 104445.
• 3.   Thete S.G., umbare D.K., Namazi N., Kothawade S., Bajaj S., Thete S.S. Reliability and accuracy of bite mark analysis in forensic odontology. J Neonatal Surg. 2025; 14(8): 10-
• 4.   Khandelwal S., Srivastava R., Dave M., Jain A., Bais K., Shrivastav S. Role of artificial intelligence in forensic odontology: A review. J Neonatal Surg. 2025; 14(20s): 251-256.
• 5.   Dehankar S., Deshmukh V., Pathak D., Saini M. Artificial intelligence in forensic odontology: A review. IP Int J Maxillofac Imaging. 2023; 9(3): 148-152.
• 6.   Thete S.G., umbare D.K., Namazi N., Kothawade S., Bajaj S., Thete S.S. Bitemark evidence in forensic dentistry for human identification using deep learning technique. J Adv Res Dent. 2024; 8(2): 12-16. 07. Palomo M., Rodríguez-Archilla A., Del Carmen M. Palatal rugae as a discriminating factor in determining sex: A new method applicable in forensic odontology?. Appl Sci (Basel). 2021; 11(9): 204.
• 7.   Manjunath T., Hiremath P., Anvekar M. Role of artificial intelligence in validating palatal rugae patterns for individual identification: A systematic review. J Forensic Dent Sci. 2025; 17(3): 150-155.
• 8.   Mura-Pérez G., Valiente-López M., Schütz F. Forensic odontology in the digital era: A narrative review of current methods and emerging trends. Diagnostics (Basel). 2025; 15(20): 2550.
• 9.   Mahasantipiya P., Lertwate T., Wattanaudomsin S., Wongsudhiraks K. Artificial intelligence for automated dental identification: A systematic review. Forensic Sci Int. 2023; 348: 111394.
• 10.   Pini A., Mochi L., Galiè M., Carli C., Fineschi V., Pinchi V. Applications of contemporary artificial intelligence technology in forensic odontology as primary forensic identifier: A scoping review. Front Artif Intell. 2022; 5: 1049584.
• 11.   Varghese K., Gopinath S. The rugae classification sequence: Using machine learning to determine gender from characteristics of palatal rugae. J Forensic Sci Med. 2025; 4(2): 1-5.
• 13.   Sadr S., Afshar A., Khani R. Deep learning for tooth identification and enumeration in panoramic radiographs. Heliyon. 2023; 9(6): e18591.
• 14.   Ghorbani Z., Khajeh F., Tizhoosh H. A novel deep learning-based model for automated tooth detection and numbering in dental photographs. Front Artif Intell. 2025; 8: 1452.
• 15.   Sun Y., Feng J., Du H., Liu J., Pang B.C., Li C., et al. Single tooth segmentation on panoramic X-rays using end-to-end deep neural networks. Open J Stomatol. 2024; 14(6): 316-26.
• 16.   Deb M., Dhar M.K., Madhab D., Yu Z. A deep learning approach to teeth segmentation and orientation from panoramic X-rays. arXiv [Preprint]. 2023
• 17.   Bonfanti-Gris M., Herrera A., Salido RodríguezManzaneque M.P., Soler-Company E., González-Martín B., Oller A. Deep learning for tooth detection and segmentation in panoramic radiographs: A systematic review and metaanalysis. BMC Oral Health. 2025;25:1280.
• 18.   Almalki A., Latecki L.J. Self-supervised learning with masked image modeling for teeth numbering, detection of dental restorations, and instance segmentation in dental panoramic radiographs. arXiv [Preprint]. 2022
• 19.   Jin L., Li Y., Zhang T., Liu B., Ye J., Li B. Detection of three-rooted mandibular first83 IJFMP/Volume 19 Number 1 January–March 2026 Borah Rajshree, Patrick Labella Cornelius, Ghosh Sreya, et al. Tracing Identity from Teeth to Technology: Artificial Intelligence in Forensic Dental Identification. molars on panoramic radiographs using deep learning. Sci Rep. 2024; 14: 2670.
• 20.   Pornprasertsuk-Damrongsri S., Jantana N., Manotham P. Clinical application of deep learning for enhanced tooth detection and caries segmentation on panoramic radiographs. Sci Rep. 2024; 14: 6241.
• 21.   Choi H.R., Kim H., Lee S.J., Kim S.Y. Can deep learning identify humans by automatically recognizing dentition patterns in panoramic radiographs? PLoS One. 2024;19(3):e0301425.
• 22.   Enomoto A., Uehara N., Kawai Y., Miyachi H., Minemoto T., Inui H., et al. Automatic identification of individuals using deep learning with panoramic radiographs. Forensic Imaging. 2023; 3(2): 100092.
• 23.   Silva B., Pinheiro L., Sobrinho B., Lima F., Abdalla K., Pithon M., et al. OdontoAI: A human-in-the-loop labeled data set and an online platform to boost research on dental panoramic radiographs. arXiv [Preprint]. 2022.

Data Sharing Statement

Funding