References: Dental Age Assessment and Artificial Intelligence

References arranged chronologically from newest to oldest (2026–2025). Vancouver-style numbered entries; bibliographic details retained as provided in the source document.

  1. Çelik İH, Salmanpour F. Can ChatGPT-5 estimate dental age? A comparative study with Demirjian and Willems methods in paediatric dentistry. BMC Oral Health. 2026;26:849. doi:10.1186/s12903-026-08247-0.
  2. Tomljenović, F., Vodanović, M., Šribar, A. et al. Dental age assessment: orthopantomogram-based comparison of traditional methods and artificial intelligence in the adult population. Int J Legal Med 140, 4167–4179 2026. https://doi.org/10.1007/s00414-026-03908-9
  3. Shi L, Brkić H, Pereira CP, Fredriksson MV, da Silva RHA, Gao D, Vodanović M, Rodrigues A, Sevä K, Figueira-Silva PC, Huang J, Santos R, da Silva MC, Ouyang Y, Haye Biazevic MG, Galić I. Population effects and cross-population generalisability of the third molar maturity index for 18-year age classification: a multi-centre study with machine learning validation. Int J Legal Med. 2026 Aug 20. doi: 10.1007/s00414-026-03965-0. Epub ahead of print. PMID: 42618662.
  4. Rodrigues Moreira LF, Moreira R, Rodrigues LGF. Multi-view deep fusion for forensic dental age estimation in tomographic imaging. Int J Legal Med. 2026 Jul;140(4):2225-2235. doi: 10.1007/s00414-026-03749-6. Epub 2026 Mar 10. PMID: 41803444; PMCID: PMC13275599.
  5. Palmela Pereira, C., Figueiras A., Nushi V., Elbawab M., Fransisco A., Oliveira A., Vaz C., Santos R. 2026. Assessing convolutional neural networks architectures for orthopantomography image analysis: Judicial dental age threshold classification. Archives of Oral Biology, Volume 188, 106626, ISSN 0003-9969. https://doi.org/10.1016/j.archoralbio.2026.106626
  6. Palmela Pereira C. AI decision support in forensic dental age assessment: proposed criteria for living individuals. Int J Legal Med. 2026 May;140(3):1443-1450. doi: 10.1007/s00414-026-03723-2. Epub 2026 Jan 30. PMID: 41612067.
  7. Oliveira W, Oliveira ML, Haiter Neto F, Albuquerque Santos M, Dos Anjos Pontual ML, Beltrão RV, Pontual AA, Ramos-Perez FMM, Freitas DQ, Zanchettin C. Quantifying cross-site effect in AI-based dental age estimation: evidence from Brazilian panoramic radiographs. Dentomaxillofac Radiol. 2026 Jul 1;55(5):477-486. doi: 10.1093/dmfr/twag014. PMID: 41712524.
  8. Oliveira ML, Yang S, Sampaio-Oliveira M, Freitas DQ, Haiter-Neto F, Lim SH, Han J, Kim S, Kim JM, Yi WJ, Heo MS. Multi-Task Deep Learning for Sex and Age Estimation from Panoramic Radiographs in a Brazilian Young Population. Int Dent J. 2026 Apr;76(2):109381. doi: 10.1016/j.identj.2025.109381. Epub 2026 Jan 27. Erratum in: Int Dent J. 2026 Jun;76(3):109504. doi: 10.1016/j.identj.2026.109504. PMID: 41604952; PMCID: PMC12865573.
  9. Nuzzolese, E.; Torreggianti, M. Reframing Adult Age Determination in Forensic Odontology Through a Certainty-Based Hybrid Human–AI Dental Maturity Approach. Oral 2026, 6, 116. https://doi.org/10.3390/oral6050116
  10. Mitsea A, Christoloukas N, Rontogianni A, Subašić M, Milošević D, Vodanović M. A pilot study on AI-driven age estimation and sex determination in Greek individuals. J Imaging. 2026;12(6):239. doi:10.3390/jimaging12060239.
  11. Marcelo R, Rodrigues A, Palmela Pereira C, Figueiras A, Santos R, Figueira JR, Francisco AP, Vaz C. 2026. AIdentifyAGE ontology for decision support in forensic dental age assessment. Appl Ontol. https://doi.org/10.1177/15705838261459602
  12. Luo S, Shi L, Liu M, Sun YX, Yang HK, Qiu LR, Zhou YC, Fan F, Zhang DW, Deng ZH, Chen H, Zhan MJ. Automated Dental Age Estimation from Panoramic Radiographs using an Interpretable Deep Learning Model. Int J Legal Med. 2026 Jul;140(4):2237-2246. doi: 10.1007/s00414-026-03748-7. Epub 2026 Mar 18. PMID: 41845068.
  13. Lee D, Oh S, Hwang S, Park J, Oh Y, Kumagai A, et al. Machine learning approach for adult age estimation using dental characteristics on panoramic radiographs. Sci Rep. 2026;16:14401. doi:10.1038/s41598-026-45271-0.
  14. Lakhaouaja F, García Navarro A. Dental age estimation in forensic odontology: a systematic review comparing traditional methods and artificial intelligence approaches. Front Dent Med. 2026;7:1882454. doi:10.3389/fdmed.2026.1882454.
  15. Lacerda-Santos JT, Paulino JAS, Brito HBS, Almeida FT, Bento PM, de Melo DP. Hybrid approach with convolutional neural network and regression models for age estimation in panoramic radiographs. Int J Legal Med. 2026 Jul 29. doi: 10.1007/s00414-026-03927-6. Epub ahead of print. PMID: 42525244.
  16. Kim SK, Lee ES, Kim BI. AI-assisted age estimation from occlusal tooth wear using biofluorescence imaging. Sci Rep. 2026;16:13145. doi:10.1038/s41598-026-42573-1.
  17. Khodwe O, Varadwaj PK, Gupta S. Panoramic Insights: Predicting Age and Gender from Dental X-Rays Using Deep Learning Models. J Imaging Inform Med. 2026 Apr 28. doi: 10.1007/s10278-026-01979-z. Epub ahead of print. PMID: 42050079.
  18. Karauğuz, Z.S., Altan, H. First-stage assessment of AI-based anatomical measurement accuracy for the Cameriere European dental age estimation method. Int J Legal Med. 2026; 140, 3951–3959. https://doi.org/10.1007/s00414-026-03950-7
  19. Karauğuz ZS, Altan H. First-stage assessment of AI-based anatomical measurement accuracy for the Cameriere European dental age estimation method. Int J Legal Med. 2026 Aug 8. doi: 10.1007/s00414-026-03950-7. Epub ahead of print. PMID: 42567969.
  20. Jiachen Ren, Junyi Wu, Wangyue Dai, Xiaorui Zhan, Xiao Li, Junshang Wei, Yuhui Shi, Yuxin Guo, Yucheng Guo, Haotian Meng, Using Kvaal method and machine learning to improve adult dental age estimation with CBCT, Forensic Sci Int, 2026; Volume 384, 112948, ISSN 0379-0738,
  21. Israel, D.S., Cruz Michael, S.H.R., Angelo, J.M. et al. Deep convolutional neural networks for age and gender estimation using orthopantomogram images: a systematic review. Evid Based Dent 27, 43–44 2026. https://doi.org/10.1038/s41432-026-01208-0
  22. Görgün EA, Çakmakoğlu EE, Sağmak S, Kutlu H. Deep learning-based dental age classification from panoramic radiographs: a multi-granularity, multi-model comparative study. BMC Oral Health. 2026;26:1774. doi:10.1186/s12903-026-09068-x.
  23. Guo Tang, Weijie Shan, Xuan Huang, Mingyu Xie, Zixiong Xu, Xia Yue, Monte Carlo-based uncertainty estimation in end-to-end deep learning model for children and adolescents’ dental age assessment, Engineering Applications of Artificial Intelligence, Volume 163, Part 3, 2026, 113098, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2025.113098.
  24. Gamal Mohamed E, El-Saeed AR, Ardah H, Fayek MM, Kayed M. Dental Age-Group Classification from Panoramic Radiographs Using Convolutional Neural Networks. Diagnostics (Basel). 2026 Jun 12;16(12):1816. doi: 10.3390/diagnostics16121816. PMID: 42351475; PMCID: PMC13298189.
  25. Duru A, Kaplan TT. Clinically interpretable deep learning for pediatric dental age estimation with explainable AI. BMC Oral Health. 2026;26:573. doi:10.1186/s12903-026-07961-z.
  26. Diricanlı MG, Akkaya N, Zengin HY. Comparison of cone-beam computed tomography and panoramic radiography in estimating the 18-year age threshold: a retrospective study. BMC Oral Health. 2026;26:29. doi:10.1186/s12903-025-07428-7.
  27. Ding, Y., He, Y., Chen, R. et al. AI-assisted age estimation from first molars: an integrated evaluation of regression, machine learning, and 1D-CNN models using CBCT-derived pulp-dentin index. Egypt J Forensic Sci 16, 80 2026. https://doi.org/10.1186/s41935-026-00581-2
  28. de Sousa YA, Silva DF, Borella NR, dos Anjos Pontual ML, Paulino JA, Bento PM, de Melo DP. Age estimation through mandibular radiomic attributes in panoramic radiographs using convolutional neural networks. Imaging Sci Dent. 2026 Sep;56(3):264-272. https://doi.org/10.5624/isd.20260079
  29. Büşra Şenel Demirtaş, Rocharles Cavalcante Fontenele, Fernando Fortes Picoli, Ahmad Rabah, Reinhilde Jacobs, AI-driven 3D wisdom tooth models for age estimation: Novel pictorial tables for caucasians aged 16–22 years, Digital Dentistry Journal, Volume 3, Issue 1, 2026, 100091, ISSN 2950-6433,
  30. Baylis S, Dipnall JF, Bassed R. Dental age estimation: a scoping review comparing the manual application of the Demirjian method and artificial intelligence modalities. Int J Legal Med. 2026 May;140(3):1451-1469. doi: 10.1007/s00414-026-03721-4.
  31. Bashir T, Alwafi HA. Deep learning-based dental age estimation in adolescents using panoramic radiographs: a comparative study with traditional methods. Front Radiol. 2026 May 13;6:1754772. doi: 10.3389/fradi.2026.1754772.
  32. Alsufyani N, Alowa S, Alrasheed H, Alqahtani S. Radiographic legal age estimation based on third molar development using machine-learning algorithms. PLoS One. 2026 Jul 30;21(7):e0354704. doi: 10.1371/journal.pone.0354704.
  33. Akyuz M, Magat G. Radiographic root pulp visibility of the mandibular left second molar as an indicator of legal adulthood (≥18 years): a panoramic study. Sci Rep. 2026;16:23443. doi:10.1038/s41598-026-53748-1.
  34. Akshaya M, Vijayalakshmi KR, Kumar TNR, Rakshana M. Automated dental age estimation model for panoramic radiographs. J Forensic Leg Med. 2026 May;120:103148. doi: 10.1016/j.jflm.2026.103148.
  35. Abuabara A, de Oliveira ME, Antunes LAA, Teixeira EC, Reis CLB, Campos CGDC, de Araujo CM, Kirschneck C, Baratto-Filho F, Küchler EC. Validation of the Dental Age app for automated dental age estimation using Demirjian’s method. Front Dent Med. 2026 Feb 9;7:1743600. doi: 10.3389/fdmed.2026.1743600.
  36. ⁠Pereira, C. P., Correia, M., Augusto, D., Coutinho, F., Salvado Silva, F., & Santos, R. Forensic sex classification by convolutional neural network approach by VGG16 model: accuracy, precision and sensitivity. Int J Legal Med. 2025; 139(3), 1381–1393. https://doi.org/10.1007/s00414-025-03416-2
  37. Yilmaz E, Görürgöz C, Kış HC, Canger EM, Öztaş B. Forensic dental age estimation with deep learning: a modified xception model for panoramic X-ray images. Forensic Sci Med Pathol. 2025 Jun;21(2):565-579. doi: 10.1007/s12024-025-00962-4.
  38. Yavuz BS, Ekmekcioglu O, Ankarali H. Comparison of different dental age estimation methods with deep learning: Willems, Cameriere-European, London Atlas. Int J Legal Med. 2025 Jul;139(4):1661-1672. doi: 10.1007/s00414-025-03452-y.
  39. Upalananda, W., Phisutphithayakun, C., Assawasuksant, P. et al. A fully automated deep learning framework for age estimation in adults using periapical radiographs of canine teeth. Int J Legal Med. 2025; 139, 2869–288. https://doi.org/10.1007/s00414-025-03558-3
  40. Upalananda W, Charuakkra A, Chaichulee S. End-to-end vs. human-defined feature extraction: comparing deep learning approaches for age classification using mandibular third molars. J Forensic Odontostomatol. 2025 Dec 24;43(3):20-30. doi: 10.5281/zenodo.17776415.
  41. Tan WX, Davies JA, Liversidge HM. Dental age estimation using the London atlas- which tooth and which tooth stage predict age best (excluding 3rd molars)? Int J Legal Med. 2025 Nov;139(6):2891-2901. doi: 10.1007/s00414-025-03557-4.
  42. Sributsayakarn N, Intharah T, Hirunchavarod N, Pornprasertsuk-Damrongsri S, Jirarattanasopha V. Automated Age and Sex Estimation From Dental Panoramic Radiographs. Int Dent J. 2025 Dec;75(6):103967. doi: 10.1016/j.identj.2025.103967.
  43. Shyni ID, Sheeja Herobin Rani C, Sheena Daphne DR, Swetha M. Explainable deep learning for age estimation in forensic disaster victim identification. In: 2025 1st International Conference on Advancement in Futuristic Technologies (ICAFT); 2025. doi:10.1109/ICAFT66710.2025.11453128.
  44. Rokhshad R, Nasiri F, Saberi N, Shoorgashti R, Ehsani SS, Nasiri Z, Azadi A, Schwendicke F. Deep learning for age estimation from panoramic radiographs: A systematic review and meta-analysis. J Dent. 2025 Mar;154:105560. doi: 10.1016/j.jdent.2025.105560.
  45. Pishghadam, N., Esmaeilyfard, R. & Paknahad, M. Explainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learning. Sci Rep 15, 18070 2025. https://doi.org/10.1038/s41598-025-03305-z
  46. Pinto R, Cordeiro R, Galego Z, Turkina A, Boedi R, Franco A. Radio-morphologic assessment of dental formation leading to permanent teeth and third molar age estimation models. Morphologie. 2025 Sep;109(366):100966. doi: 10.1016/j.morpho.2025.100966. Epub 2025 May 15. PMID: 40378654.
  47. Peker RB. Artificial intelligence versus human expertise: reliability of ChatGPT and the London atlas for dental age estimation using panoramic radiographs. BMC Oral Health. 2025 Nov 28;25(1):2002. doi: 10.1186/s12903-025-07360-w.
  48. Palmela Pereira, C., Carvalho, R., Augusto, D., Almeida, T., Francisco, A. P., Salvado E Silva, F., & Santos, R. Development of artificial intelligence-based algorithms for the process of human identification through dental evidence. Int J Legal Med. 2025; 139(4), 1835–1850. https://doi.org/10.1007/s00414-025-03453-x
  49. Nushi, V., Santos, R., Brkić, H. et al. Pre-trained VGG16 model for forensic dental age estimation. Egypt J Forensic Sci 15, 73 2025. https://doi.org/10.1186/s41935-025-00489-3
  50. Li Z, Xiao N, Nan X, Chen K, Zhao Y, Wang S, Guo X, Gao C. Automatic dental age estimation in adolescents via oral panoramic imaging. Front Dent Med. 2025 Jun 26;6:1618246. doi: 10.3389/fdmed.2025.1618246.
  51. Kurniawan A, Saelung M, Rizky BN, ChusidaAn’nisaa, Prakoeswa BF, Nefertari G, Pradue AF, Margaretha MS, Alias A, Marya A. Dental age estimation using a convolutional neural network algorithm on panoramic radiographs: A pilot study in Indonesia. Imaging Sci Dent. 2025 Mar;55(1):28-36. https://doi.org/10.5624/isd.20240134
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  59. Alam, S.S.; Rashid, N.; Faiza, T.A.; Ahmed, S.; Hassan, R.A.; Dudley, J.; Farook, T.H. Estimating Age and Sex from Dental Panoramic Radiographs Using Neural Networks and Vision–Language Models. Oral 2025, 5, 3. https://doi.org/10.3390/oral5010003
  60. Abuabara A, do Nascimento TVPM, Trentini SM, Costa Gonçalves AM, Hueb de Menezes-Oliveira MA, Madalena IR, Beisel-Memmert S, Kirschneck C, Antunes LAA, Miranda de Araujo C, Baratto-Filho F, Küchler EC. Evaluating the accuracy of generative artificial intelligence models in dental age estimation based on the Demirjian’s method. Front Dent Med. 2025 Jul 29;6:1634006. doi: 10.3389/fdmed.2025.1634006.
  61. Abhinav Chopra, Anand Gupta, Naveen Aggarwal, Artificial intelligence in dental age estimation- applications, technological advances and legal aspects: A narrative review, J Oral Biology and Craniofacial Research. 2025; 15( 6): 1534-1538, ISSN 2212-4268, https://doi.org/10.1016/j.jobcr.2025.09.010.
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