Artificial Intelligence–Driven Innovations in Forensic Odontology for Human Identification: A Narrative Review

Authors

  • Roben S. Pasaribu Health Polytechnic of Jakarta I
  • Dian Muliawati Health Polytechnic of Jakarta I
  • Aditia D. Yunus Indonesian Defense University

DOI:

https://doi.org/10.35790/eg.v14i2.65713

Keywords:

forensic odontology; artificial intelligence; identification process; application

Abstract

Abstract: Forensic odontology is a branch of forensic science that plays a role in identification, including age and sex estimation. The identification results acted as valid evidence based on the science of forensic dentistry, which is used in the judicial process. Traditional methods of identification have the disadvantage of subjectivity on the part of the examiner. The analysis results can be biased, leading to incorrect conclusions. The rapid development of technology has impacted the field of forensic odontology. This review aimed to evaluate the potential and applications of AI in forensic odontology. Artificial intelligence (AI) is a computer-based system designed using principles of human intelligence. The application of artificial intelligence to assist forensic experts is growing and being explored increasingly. Artificial intelligence in forensic odontology plays a role in gender determination, age estimation, lip print analysis, toothmark analysis, personal identification, and facial reconstruction. The AI-based identification process is expected to eliminate the researcher's subjectivity and improve the accuracy of the results. In conclusion, artificial intelligence (AI) has significantly influenced forensic odontology by supporting key forensic tasks, including age estimation, sex determination, cheiloscopy analysis, and facial reconstruction.

Keywords: forensic odontology; artificial intelligence; identification process; application

Author Biographies

Roben S. Pasaribu, Health Polytechnic of Jakarta I

Department of Dental Health, Health Polytechnic of Jakarta I, Indonesia

Dian Muliawati, Health Polytechnic of Jakarta I

Department of Dental Health, Health Polytechnic of Jakarta I, Indonesia

Aditia D. Yunus, Indonesian Defense University

Doctoral Program in Defense Science, Defense Health Science Study Program, Indonesian Defense University, Jakarta, Indonesia

References

1. Pathak J, Swain N, Pathak D, Shrikanth G, Hosalkar R. Role of various stakeholders in application of artificial intelligence to forensic odontology- a potential perspective. Annals of Dental Specialty. 2021;9(1):47–52. Doi: https://doi.org/10.51847/CBwpxBuRc0

2. Nagi R, Aravinda K, Rakesh N, Jain S, Kaur N, Mann A. Digitization in forensic odontology: A paradigm shift in forensic investigations. J Forensic Dent Sci. 2019;11(1):5. Doi: https://doi.org/10.4103/jfo.jfds_55_19

3. Mörch CM, Atsu S, Cai W, Li X, Madathil SA, Liu X, et al. Artificial intelligence and ethics in dentistry: a scoping review. J Dent Res. 2021;100(13):1452-60. Doi: https://doi.org/10.1177/00220345211013808

4. Rigby MJ. From The Editor Ethical dimensions of using artificial intelligence in health care. AMA Journal of Ethics. 2019;21(2):E121-124. Available from: https://journalofethics.ama-assn.org/sites/joedb/files/2019-01/fred1-1902_1.pdf

5. Agrawal P, Nikhade P. Artificial intelligence in dentistry: past, present, and future. Cureus. 2022 Jul 28;14(7):e27405. Doi: https://doi.org/10.7759/cureus.27405

6. Park WJ, Park JB. History and application of artificial neural networks in dentistry. Eur J Dent. 2018;12(4):594-601. Doi: https://doi.org/10.4103/ejd.ejd_325_18

7. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017:69S:S36-S40. Doi: https://doi.org/10.1016/j.metabol.2017.01.011

8. Khanagar SB, Al-ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, et al. Developments, application, and performance of artificial intelligence in dentistry – a systematic review. J Dent Sci. 2021;16(1):508-22. Doi: https://doi.org/10.1016/j.jds.2020.06.019

9. Smitha T. Artificial intelligence in forensic odontology. J Forensic Dent Sci. 2023;13(1):01–2. Doi: https://doi.org/10.18311/jfds/13/1/2021.659

10. Sekhar CM, Thabusum A, Charitha M, Chandrasekhar G, Naik DN. Unfolding the link- lip prints and finger prints in personal identification. Saudi Journal of Oral and Dental Research. 2019;4(3):188–93. Doi: https://doi.org/10.21276/sjodr.2019.4.3.16

11. Tobel JD, Radesh P, Vandermeulen D, Thevissen PW. An automated technique to stage lower third molar development on panoramic radiographs for age estimation- a pilot study. J Forensic Odontostomatol. 2017;35(2):42-54. Available from: https:// pubmed.ncbi.nlm.nih.gov/29384736/

12. Khanagar SB, Vishwanathaiah S, Naik S, Al-Kheraif AA, Divakar DD, Sarode SC, et al. Application and performance of artificial intelligence technology in forensic odontology – a systematic review. Leg Med (Tokyo). 2021:48:101826. Doi: https://doi.org/10.1016/j.legalmed.2020.101826

13. Vila-Blanco N, Carreira MJ, Varas-Quintana P, Balsa-Castro C, Tomas I. Deep neural networks for chronological age estimation from OPG images. IEEE Trans Med Imaging. 2020;39(7):2374-84. Doi: https://doi.org/10.1109/TMI.2020.2968765

14. de Back W, Seurig S, Wagner S, Marré B, Roeder I, Scherf N. Forensic age estimation with Bayesian convolutional neural networks based on panoramic dental X-ray imaging. In: Proceedings of Machine Learning Research – Under Review. MIDL; 2019. p. 1–4. Available from: https://pure.mpg.de/pubman/item/item_3166969_2

15. Koch RM, Mentzel HJ, Heinrich A. Deep learning for forensic age estimation using orthopantomograms in children, adolescents, and young adults. Eur Radiol. 2025;35(7):4191-202. Doi: https://doi.org/10.1007/s00330-025-11373-y

16. Štern D, Payer C, Giuliani N, Urschler M. Automatic age estimation and majority age classification from multi-factorial MRI data. IEEE J Biomed Health Inform. 2019;23(4):1392-403. Doi: https://doi.org/10.1109/JBHI.2018.2869606

17. Patil V, Vineetha R, Vatsa S, Shetty DK, Raju A, Naik N, et al. Artificial neural network for gender determination using mandibular morphometric parameters: A comparative retrospective study, Cogent Engineering. 2020;7:1: 1723783. Doi: https://doi.org/10.1080/23311916.2020.1723783

18. Fidya F, Priyambadha B. Automation of gender determination in human canines using artificial intelligence. Dental Journal (Majalah Kedokteran Gigi). 2017;50(3):116. Doi: https://doi.org/10.20473/j.djmkg.v50.i3.p116-120

19. Kozan NM, Kotsyubynska YuZ, Zelenchuk GM. Using the artificial neural networks for identification unknown person. IOSR Journal of Dental and Medical Sciences (IOSR-JDMS). 2017;16(4):107–13. Doi: https://doi.org/10.9790/0853-160403107113

20. Kumar BN, Ramesh T, Reddy S, Chennoju SK, Pavani K, Praveen KNS. Morphometric analysis of mandible for sex determination-a retrospective study. Int J Sci Res Methodol. 2016;4(3):1–9. Available from: www.ijsrm.humanjournals.com

21. Ao SI, International Association of Engineers. International MultiConference of Engineers and Computer Scientists: IMECS 2011: 16-18 March, 2011, Kowloon, Hong Kong. Newswood Limited.; 2011. 1580 p. In 2017. Available from: http://arxiv.org/abs/1703.07834

22. Ibrahim LM, Saleh IA. Face recognition using artificial intelligent techniques. Raf J of Comp & Math’s. 2009;6(2):211–27. Doi: https://doi.org/10.33899/csmj.2009.163809

23. Jackson AS, Bulat A, Argyriou V, Tzimiropoulos G. Large pose 3D face reconstruction from a single image via direct volumetric CNN Regression. Computer Vision and Pattern Recognition (cs.CV). 2017. Doi:

https://doi.org/10.48550/arXiv.1703.07834

24. Sandhya S, Fernandes R, Sapna S, Rodrigues AP. Comparative analysis of machine learning algorithms for lip-print based person identification. Evol Intell. 2022;15(1):743–57. Available from; https://link.springer.com/article/ 10.1007/s12065-020-00561-y

25. Prabhu RV, Dinkar A, Prabhu V. Digital method for lip print analysis: a new approach. J Forensic Dent Sci. 2013;5(2):96-105. Doi: https://doi.org/10.4103/0975-1475.119772

26. Sandhya S, Fernandes R. Lip print: an emerging biometrics technology - a review. In: 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017. Institute of Electrical and Electronics Engineers Inc.; 2018. 2017 IEEE International Conference on Computational Intelligence and Computing Research. Doi: https://doi.org/10.1109/ICCIC.2017.8524457

27. Sabelli AF, Chatterjee P, Pollo-Cattaneo MF. Predictive modeling toward identification of sex from lip prints-machine learning in cheiloscopy. In: Workshops at the Fourth International Conference on Applied Informatics. Buenos Aires: CEUR Workshop Proceedings; 2021. p. 29–43. Available from: http://www.biometrics.us.edu.pl

28. Farrukh W, van der Haar D. Lip print-based identification using traditional and deep learning. IET Biom. 2023;12(1):1–12. Doi: https://doi.org/10.1049/bme2.12073

29. Zhou H. Lip print recognition algorithm based on convolutional network. J Appl Math. 2023;4448861:1–8. Doi: https://doi.org/10.1155/2023/4448861

Downloads

Published

2026-02-23

How to Cite

Pasaribu, R. S., Muliawati, D., & Yunus, A. D. (2026). Artificial Intelligence–Driven Innovations in Forensic Odontology for Human Identification: A Narrative Review. E-GiGi, 14(2), 354–361. https://doi.org/10.35790/eg.v14i2.65713

Issue

Vol. 14 No. 2 (2026): e-GiGi

Section

Articles

License

Copyright (c) 2026 Roben S. Pasaribu, Dian Muliawati, Aditia D. Yunus

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

COPYRIGHT

Authors who publish with this journal agree to the following terms:

Authors hold their copyright and grant this journal the privilege of first publication, with the work simultaneously licensed under a Creative Commons Attribution License that permits others to impart the work with an acknowledgment of the work's origin and initial publication by this journal.

Authors can enter into separate or additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (for example, post it to an institutional repository or publish it in a book), with an acknowledgment of its underlying publication in this journal.

Authors are permitted and encouraged to post their work online (for example, in institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).