Current Advances in Age and Sex Estimation Using Artificial Intelligence in Forensic Odontology

Öz

Forensic odontology plays a crucial role in human identification, particularly in estimating age and sex in individuals without official identification documents. Although traditional methods are widely used, they have significant limitations such as population variability, observer-related errors, and low reproducibility. In recent years, artificial intelligence (AI) and machine learning approaches have provided more objective, rapid, and reproducible alternatives. In particular, convolutional neural network-based algorithms have reduced mean absolute error in age estimation and achieved accuracy rates exceeding 90% in sex classification by automatically extracting discriminative features from panoramic radiographs and cone-beam computed tomography data. National and international studies support the reliability of these technologies, especially in pediatric and adolescent populations where traditional methods are less precise. However, dataset biases, population-specific variability, limited generalizability across ethnic groups, and the non-transparent nature of deep learning models continue to pose challenges for interpretability and legal admissibility. Therefore, ethical and regulatory frameworks emphasizing transparency, data protection, and explainable-AI (XAI) principles are essential for the responsible implementation of these technologies. Future progress will depend on large-scale, multicenter validation studies and the integration of multimodal datasets combining radiographic, morphological, and demographic information. The adoption of XAI frameworks is expected to enhance transparency, accountability, and forensic reliability, thereby enabling. AI-based systems to become scientifically robust and legally valid tools in forensic odontology.

Anahtar Kelimeler

• forensic odontology
• age estimation
• sex estimation
• artificial intelligence

Destekleyen Kurum

The author received no financial support or assistance for this study.

Etik Beyan

This study is a review article and does not involve any experimental procedures on human participants; therefore, ethical committee approval was not required.

Teşekkür

Not applicable.

Adli Diş Hekimliğinde Yapay Zekâ Destekli Yaş ve Cinsiyet Tahminindeki Güncel Gelişmeler

Öz

Adli diş hekimliği, özellikle resmi kimlik belgeleri bulunmayan bireylerde yaş ve cinsiyet tahmininde insan kimliğinin belirlenmesinde önemli bir rol oynar. Geleneksel yöntemler yaygın biçimde kullanılsa da, popülasyon farklılıkları, gözlemciye bağlı hatalar ve düşük tekrarlanabilirlik gibi önemli sınırlılıklara sahiptir. Son yıllarda yapay zekâ (YZ) ve makine öğrenimi yaklaşımları, daha nesnel, hızlı ve tekrarlanabilir alternatifler sunmaktadır. Özellikle evrişimsel sinir ağı tabanlı algoritmalar, panoramik radyograflar ve konik ışınlı bilgisayarlı tomografi verilerinden ayırt edici özellikleri otomatik olarak çıkararak yaş tahmininde ortalama mutlak hatayı azaltmış, cinsiyet sınıflandırmasında ise %90’ın üzerinde doğruluk oranları elde etmiştir. Ulusal ve uluslararası çalışmalar, özellikle geleneksel yöntemlerin daha az hassas olduğu pediatrik ve adölesan popülasyonlarda, bu teknolojilerin güvenilirliğini desteklemektedir. Bununla birlikte, veri seti yanlılıkları, popülasyona özgü değişkenlik, farklı etnik gruplar arasında sınırlı genellenebilirlik ve derin öğrenme modellerinin şeffaf olmayan yapısı, yorumlanabilirlik ve yasal kabul açısından hâlen zorluk oluşturmaktadır. Bu nedenle, şeffaflık, veri koruma ve açıklanabilir. YZ ilkelerini vurgulayan etik ve düzenleyici çerçeveler, bu teknolojilerin sorumlu şekilde uygulanması için gereklidir. Gelecekteki ilerlemeler, geniş ölçekli çok merkezli doğrulama çalışmalarıyla ve radyografik, morfolojik ve demografik verileri birleştiren çok modlu veri setlerinin entegrasyonu ile sağlanacaktır. Açıklanabilir YZ çerçevelerinin benimsenmesi, şeffaflığı, hesap verebilirliği ve adli güvenilirliği artırarak, YZ tabanlı sistemlerin adli diş hekimliğinde bilimsel olarak sağlam ve yasal açıdan geçerli araçlar haline gelmesini sağlayacaktır.

Anahtar Kelimeler

• adli diş hekimliği
• yaş tahmini
• cinsiyet tahmini
• yapay zekâ

Destekleyen Kurum

Yazar bu çalışma için herhangi bir maddi destek veya yardım almamıştır.

Etik Beyan

Bu çalışma bir derleme makalesi olup, insan katılımcılar üzerinde herhangi bir deneysel uygulama içermediğinden etik kurul onayı gerektirmemektedir.

Teşekkür

Uygulanamaz.

Kaynakça

1. Hinchliffe J. Forensic odontology, part 2. Major disasters. Br Dent J. 2011;210(6):269–74.​
2. Singh S, Singha B, Kumar S. Artificial intelligence in age and sex determination using maxillofacial radiographs: a systematic review. J Forensic Odonto-Stomatol. 2024;42(1):30–7.​
3. Blackwell S, Taylor R, Gordon I, Ogleby C, Tanijiri T, Yoshino M. 3-D imaging and quantitative comparison of human dentitions and simulated bite marks. Int J Legal Med. 2007;121(1):9–17.​
4. Çarıkçıoğlu B, Sezer B. Dental age estimation with fewer than mandibular seven teeth: an accuracy study of Bedek models in Turkish children. Clin Oral Investig. 2022;26(9):5773–84.​
5. Munhoz L, Okada S, Hisatomi M, Yanagi Y, Arita ES, Asaumi J. Are computed tomography images of the mandible useful in age and sex determination? A forensic science meta-analysis. J Forensic Odonto-Stomatol. 2024;42(1):38–46.​
6. Çarıkçıoğlu B, Değirmenci A. Accuracy of the radiographic methods of Willems, Cameriere and Blenkin–Evans on age estimation for Turkish children. Aust J Forensic Sci. 2023;55(2):156–67.​
7. Kurniawan A, Saelung M, Rizky BN, et al. Dental age estimation using a convolutional neural network algorithm on panoramic radiographs: a pilot study in Indonesia. Imaging Sci Dent. 2025;55(1):28–36.​
8. Vila-Blanco N, Varas-Quintana P, Tomás I, Carreira MJ. A systematic overview of dental methods for age assessment in living individuals: from traditional to AI-based approaches. Int J Legal Med. 2023;137(4):1117–46.​

9. Rokhshad R, Nasiri F, Saberi N, Shoorgashti R, Ehsani SS, Nasiri Z, et al. Deep learning for age estimation from panoramic radiographs: a systematic review and meta-analysis. J Dent. 2025;154:105560.​
10. Dashti M, Azimi T, Khosraviani F, Azimian S, Bahanan L, Zahmatkesh H, et al. Systematic review and meta-analysis on the accuracy of artificial intelligence algorithms in individuals gender detection using orthopantomograms. Int Dent J. 2025;75(3):2157–68.​
11. Anić-Milošević S, Medančić N, Čalušić-Šarac M, Dumančić J, Brkić H. Artificial neural network model for predicting sex using dental and orthodontic measurements. Korean J Orthod. 2023;53(3):194–204.​
12. Naeimi SM, Darvish S, Salman BN, Luchian I. Artificial intelligence in adult and pediatric dentistry: a narrative review. Bioengineering. 2024;11(5):431.​
13. Kurniawan A, Chusida AN, Rizky BN, et al. Insights from dental resilience and artificial intelligence for sex determination: literature review. Bull Int Assoc Paleodontol. 2024;18(1):70–7.​
14. Schwendicke F, Blatz M, Uribe S, et al. Artificial intelligence for dentistry. FDI Working Group report. 2023.​
15. Esan TA, Yengopal V, Schepartz LA. The Demirjian versus the Willems method for dental age estimation in different populations: a meta-analysis. PLoS One. 2017;12(11):e0186682.​
16. Tiwari A, Chavali K. Teeth tell tales: a narrative review on dental age estimation methods from traditional to AI-driven approaches. Egypt J Forensic Sci. 2025;15(1):65.​
17. Rojas-Torres J, Martínez-Durán L, de Anta JM, et al. Accuracy and challenges in age estimation in adults: a scoping review. Forensic Sci Med Pathol. 2025. Epub ahead of print.​
18. Han M, Du S, Ge Y, et al. With or without human interference for precise age estimation based on machine learning? Int J Legal Med. 2022;136(3):821–31.​
19. Sivri MB, Taheri S, Ercan RGK, Yağcı Ü, Golrizkhatami Z. Dental age estimation: a comparative study of CNN and Demirjian’s method. J Forensic Leg Med. 2024;103:102679.​
20. Nudel I, Pokhojaev A, Bitterman Y, Shpack N, Fiorenza L, Benazzi S, et al. Secondary dentin formation mechanism: the effect of attrition. Int J Environ Res Public Health. 2021;18(19):9961.​
21. Timme M, Borkert J, Nagelmann N, Schmeling A. Evaluation of secondary dentin formation for forensic age assessment by means of semi-automatic segmented ultrahigh field 9.4 T UTE MRI datasets. Int J Legal Med. 2020;134(6):2283–8.​
22. Gupta P, Kaur H, Shankari GS M, Jawanda MK, Sahi N. Human age estimation from tooth cementum and dentin. J Clin Diagn Res. 2014;8(4):ZC07–10.​
23. Felek T, Tercanlı H, Gök RŞ. Evaluating vision transformers and convolutional neural networks in the context of dental image processing: a systematic review. BMC Oral Health. 2025;25(1):1626.​
24. Kawadkar K. Comparative analysis of vision transformers and convolutional neural networks for medical image classification [Internet]. arXiv [Preprint]. 2025 [cited 2025 Dec 17]. Available from: https://arxiv.org/abs/2507.21156.​
25. Maringka R, Mokodaser WG, Tangka GMW. Deep learning-based dental radiography classification using CNN and EfficientNet. CogITo Smart J. 2025;11(1):218–28.​
26. Akcay HC, Öçbe M. Age estimation in pediatric patients using AI applications: a traditional review. EC Dent Sci. 2024;23(1):1–8.​
27. Shahbazi S, Esmaeili S, Kavousinejad S, Younessian F, Behnaz M. Efficacy of AI in radiographic dental age estimation. Int Orthod. 2025;23(4):101010.​
28. Aljameel SS, Althumairy L, Albassam B, et al. Predictive AI model for detecting dental age using panoramic radiograph images. Big Data Cogn Comput. 2023;7(1):8.​
29. Palmela Pereira C, Carvalho R, Augusto D, et al. Development of AI-based algorithms for human identification through dental evidence. Int J Legal Med. 2025. Epub ahead of print.​
30. Nino-Sandoval TC, Doria-Martinez AM, Escobar RAV, et al. Efficacy of age determination methods using AI in panoramic radiographs: a systematic review. Int J Legal Med. 2024;138(4):1459–96.​
31. Aliyev R, Arslanoğlu E, Yaşa Y, Oktay AB. Age estimation from pediatric panoramic images with CNNs and LightGBM. In: TIPTEKNO 2022; 2022. p.1–4.​
32. Khanagar SB, Vishwanathaiah S, Naik S, et al. Application and performance of AI in forensic odontology: a systematic review. Leg Med. 2021;48:101826.​
33. Galibourg A, Cussat-Blanc S, Dumoncel J, et al. Comparison of machine learning approaches to predict dental age using Demirjian’s staging. Int J Legal Med. 2021;135(2):665–75.​
34. Abuabara A, Vilalba PM do Nascimento T, Trentini SM, et al. Evaluating the accuracy of generative AI models in dental age estimation. Front Dent Med. 2025;6:1634006.​
35. Ullah E, Parwani A, Baig MM, Singh R. Challenges and barriers of using large language models such as ChatGPT for diagnostic medicine with a focus on digital pathology: a recent scoping review. Diagn Pathol. 2024;19(1):43.​
36. Omar M, Sorin V, Collins JD, Reich D, Freeman R, Gavin N, et al. Large language models are highly vulnerable to adversarial hallucination attacks in clinical decision support: a multi-model assurance analysis. medRxiv. 2025 Mar.​
37. Hager P, Jungmann F, Holland R, Bhagat K, Hubrecht I, Knauer M, et al. Evaluation and mitigation of the limitations of large language models in clinical decision-making. Nat Med. 2024;30(9):2613–22.​
38. Büyükçakır B, Bertels J, Claes P, et al. OPG-based dental age estimation using a data-technical exploration of deep learning techniques. J Forensic Sci. 2024;69(3):919–31.​
39. Wu TJ, Tsai CL, Huang YH, Fan TY, Chen YP. Efficacy of machine learning-assisted dental age assessment in a local population. Leg Med. 2022;59:102148.​
40. Mazloom Nezhad SM, Mohd Faizal Abdullah ER, Ibrahim N, et al. A hybrid CNN model for dental age estimation using buccal alveolar bone level for Saudi children. BMC Oral Health. 2025;25(1):1331.​
41. Doğan OB, Boyacıoğlu H, Göksuluk D. Machine learning assessment of dental age classification based on CBCT images. Dentomaxillofac Radiol. 2024;53(1):20230426.​
42. Matthijs L, Delande L, De Tobel J, et al. AI and dental age estimation: automated stage allocation on all mandibular tooth types in OPGs. Int J Legal Med. 2024;138(6):2469–79.​
43. Kahm SH, Kim JY, Yoo S, et al. Application of entire dental panorama image data in an AI model for age estimation. BMC Oral Health. 2023;23(1):1007.​
44. Yeom HG, Lee BD, Lee W, Lee T, Yun JP. Estimating chronological age through learning local and global features of orthopantomograms. Sci Rep. 2023;13(1):21857.​
45. Mohammad N, Muad AM, Ahmad R, Mohd Yusof MYP. Reclassification of Demirjian’s mandibular premolars staging. Forensic Imaging. 2021;24:200487.​
46. Milošević D, Vodanović M, Galić I, Subašić M. Automated estimation of chronological age from panoramic dental X-rays using deep learning. Expert Syst Appl. 2022;189:116038.​
47. Zaborowicz M, Zaborowicz K, Biedziak B, Garbowski T. Deep learning neural modeling. Sensors. 2022;22(19):7536.​
48. Olze A, Solheim T, Schulz R, et al. Radiographic visibility of the periodontal ligament in lower third molars for age estimation. Int J Legal Med. 2010;124(5):445–8.​
49. Degermenci M, Ertekin T, Ülger H, Acer N, Coşkun A. Age-related development of the maxillary sinus in children. J Craniofac Surg. 2016;27(1):e38–44.​
50. Mu CC, Li G. Age estimation using panoramic radiographs by transfer learning. Chin J Dent Res. 2022;25(2):119–24.​
51. White TD, Black MT, Folkens PA. Human osteology. 3rd ed. London: Academic Press; 2012.​
52. Saini V, Srivastava R, Rai RK, et al. Mandibular ramus: an indicator for sex in fragmentary mandible. J Forensic Sci. 2011;56(S1):S13–16.​
53. Patil V, Vineetha R, Vatsa S, et al. Artificial neural network for gender determination using mandibular morphometric parameters. Cogent Eng. 2020;7(1):1723783.​
54. Zhang C, Maga AM. An open-source photogrammetry workflow for reconstructing 3D models. Appl Plant Sci. 2017;5(12):1700127.​
55. Wang H, Yin J, Lu P, Yu Q. Three-dimensional geometric morphometric measurement and classification of maxillary central incisors. Arch Oral Biol. 2019;102:141–6.​
56. Ajmal MA, Roberts TS, Beshtawi KR, et al. Sexual dimorphism in odontometric parameters using CBCT. Head Face Med. 2023;19(1):6.​
57. She X, Sun S, Damon BJ, Hill CN, Coombs MC, Wei F, et al. Sexual dimorphisms in three-dimensional masticatory muscle attachment morphometry regulates temporomandibular joint mechanics. J Biomech. 2021;126:110623.​
58. Franklin D, O’Higgins P, Oxnard CE. Sexual dimorphism in the mandible of indigenous South Africans: a geometric morphometric approach. S Afr J Sci. 2008;104(3–4):101–6.​
59. Plotkin LI, Bruzzaniti A, Pianeta R. Sexual dimorphism in the musculoskeletal system: sex hormones and beyond. J Endocr Soc. 2024;8(10):bvae153.​
60. Coquerelle M, Bookstein FL, Braga J, et al. Sexual dimorphism of the human mandible and its association with dental development. Am J Phys Anthropol. 2011;145(2):192–202.​
61. Hefetz I. Integrating AI systems in criminal justice: the forensic expert as a corridor between algorithms and courtroom evidence. Forensic Sci. 2025;5(4):53.​
62. Franco A, Porto L, Heng D, et al. Diagnostic performance of convolutional neural networks for dental sexual dimorphism. Sci Rep. 2022;12(1):17279.​
63. Franco A, Cornacchia AP, Moreira D, et al. Radiographic morphology of canines tested for sexual dimorphism via CNN-based artificial intelligence. Morphologie. 2024;108(362):100772.​
64. Şahin TN, Kölüş T. Age and sex estimation in children and young adults using orthopantomograms with convolutional neural networks. Appl Sci. 2024;14(16):7014.​
65. Alam SS, Rashid N, Faiza TA, et al. Estimating age and sex from dental panoramic radiographs using neural networks and vision–language models. Oral. 2025;5(1):3.​
66. Natarajan S, Ahmed J, Sundarraj R, et al. Tooth shape and sex estimation: a three-dimensional geometric morphometric landmark-based comparative analysis of artificial neural network, support vector machine, and random forest. 3 Biotech. 2025;15(8):273.​
67. Pertek H, Kamaşak M, Kotan S, et al. Comparison of mandibular morphometric parameters. Oral Radiol. 2024;40(3):415–23.​
68. de Araujo CM, de Jesus Freitas PF, Ferraz AX, et al. Sex determination through maxillary dental arch and skeletal base measurements using machine learning. Head Face Med. 2024;20(1):44.​
69. Küchler EC, Kirschneck C, Marañón-Vásquez GA, et al. Mandibular and dental measurements for sex determination using machine learning. Sci Rep. 2024;14(1):9587.​
70. Baban MTA, Mohammad DN. A new approach for sex prediction by evaluating mandibular arch and canine dimensions with machine learning classifiers and intraoral scanners. Sci Rep. 2024;14(1):27974.​
71. Norori N, Hu Q, Aellen FM, et al. Addressing bias in big data and AI for healthcare: a call for open science. Patterns. 2021;2(10):100347.​
72. Sadik E, Gökmenoğlu C, Altun G, Kara C. Evaluation of different exposure parameters for accurate diagnosis of peri-implantitis severity in digital panoramic radiography. Med Oral Patol Oral Cir Bucal. 2022;28(1):e16–22.​
73. Trocin C, Mikalef P, Papamitsiou Z, Conboy K. Responsible AI for digital health: a synthesis and a research agenda. Inf Syst Front. 2021. Epub ahead of print.​
74. Shen S, Liu Z, Wang J, et al. Machine learning-assisted Cameriere method for dental age estimation. BMC Oral Health. 2021;21(1):641.​
75. Singh NK, Raza K. Progress in deep learning-based dental and maxillofacial image analysis: a systematic review. Expert Syst Appl. 2022;199:116929.​
76. Kokomoto K, Kariya R, Muranaka A, et al. Automatic dental age calculation from orthopantomograms using deep learning: two-stage approach. BMC Oral Health. 2024;24(1):143.​
77. Nikolinakos NT. Adapting EU liability rules to the digital age and artificial intelligence: the 2021–2022 public consultation. In: Nikolinakos NT, editor. Adapting the EU civil liability regime to the digital age: artificial intelligence, robotics, and other emerging technologies. Cham: Springer; 2024. p.327–75.​
78. Recupero PR. Daubert considerations in forensic evaluations by telepsychiatry. J Am Acad Psychiatry Law. 2022;50(4):517–28.​
79. European Union. Regulation (EU) 2024/1689 of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Off J Eur Union. 2024;L:1–137. Available from: https://eur-lex.europa.eu/eli/reg/2024/1689/oj.​
80. Li Z, Xiao N, Nan X, Chen K, Zhao Y, Wang S, et al. Automatic dental age estimation in adolescents via oral panoramic imaging. Front Dent Med. 2025;6:1618246.​
81. American Dental Association. ANSI/ADA Standard No. 1110-1. Chicago: ADA; 2025.​
82. Balel Y, Sağtaş K, Bülbül HN. Deep learning-based system for dental age estimation using the Demirjian method on orthopantomograms. BMC Oral Health. 2025;25(1):1172.​
83. Guo YC, Han M, Chi Y, et al. Accurate age classification using manual methods and convolutional neural networks based on orthopantomograms. Int J Legal Med. 2021;135(4):1589–97.​
84. Çelik H, Kılıçarslan MA, Boyacıoğlu H, Bilecen B. Application of the Kvaal method to CBCT-reconstructed panoramic images for age estimation. Forensic Sci Med Pathol. 2024;20(3):823–31.​ Fan F, Liu H, Dai X, et al. Automated bone age assessment from knee MRI radiomics with deep learning. Int J Legal Med. 2024;138(3):927–38.