Artificial intelligence in age estimation using panoramic radiography analysis

(1) * Radhityana Luktri Utami Mail (Airlangga University, Indonesia)
(2) Serlie Hosseini Mail (Undergraduate Student, Student Research Committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran, Islamic Republic of)
(3) Mahdian Ghazizadeh Mail (DDS, MSc, MPH, Assistant Professor, Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran, Islamic Republic of)
*corresponding author


Age estimation is a key point in forensic odontology. Age estimation analysis using panoramic radiography is the most used method because it is not invasive, but manual analysis is deemed too complicated and takes too much time so assistance from AI is needed. The purpose of this review was to briefly inform about the recent research regarding AI application in RP analysis for age estimation. 5 of 9 journals that met the inclusion criteria used CNN, 1 journal used ENN, 2 journals used a hybrid application of CNN and KNN, and 1 journal used transfer learning. The AI application that was most used and proved to be accurate and effective for assisting RP analysis for age estimation is CNN.


Artificial Intelligence; Age Estimation; Panoramic Radiography



Article metrics

10.29099/ijair.v8i1.1017 Abstract views : 140





A. Schmeling, W. Reisinger, G. Geserick, and A. Olze, “Age estimation of unaccompanied minors: Part I. General considerations,†Forensic Sci Int, vol. 159, pp. S61–S64, 2006, doi:

D. Ubelaker and R. Parra, “Application of Three Dental Methods of Adult Age Estimation from Intact Single Rooted Teeth to a Peruvian Sample,†J Forensic Sci, vol. 53, pp. 608–611, Jan. 2008, doi: 10.1111/j.1556-4029.2008.00699.x

C. Moorrees, E. Fanning, and E. Hunt, “Age Variation of Formation Stage for Ten Permanent Teeth,†J Dent Res, vol. 42, pp. 1490–1502, Jan. 1963, doi: 10.1177/00220345630420062701

A. Demirjian and H. Goldstein, “New system for dental maturity based on seven and four teeth,†Ann Hum Biol, vol. 3, pp. 411–421, Oct. 1976, doi: 10.1080/03014467600001671.

A. Demirjian, H. Goldstein, and J. M. Tanner, “A New System of Dental Age Assessment,†Hum Biol, vol. 45, pp. 211–227, Jan. 1973.

A. Mughal, N. Hassan, and A. Ahmed, “Bone Age Assessment Methods: A Critical Review,†Pak J Med Sci, vol. 30, pp. 211–215, Mar. 2014, doi: 10.12669/pjms.301.4295.

S. Alqahtani, M. Hector, and H. Liversidge, “Accuracy of Dental Age Estimation Charts: Schour and Massler, Ubelaker and the London Atlas,†Am J Phys Anthropol, vol. 154, Apr. 2014, doi: 10.1002/ajpa.22473.

A. Panchbhai, “Dental radiographic indicators, a key to age estimation,†Dentomaxillofac Radiol, vol. 40, pp. 199–212, May 2011, doi: 10.1259/dmfr/19478385.

G. Willems, A. Olmen, B. Spiessens, and C. Carels, “Dental Age Estimation in Belgian Children: Demirjian’s Technique Revisited,†J Forensic Sci, vol. 46, pp. 893–895, Aug. 2001, doi: 10.1520/JFS15064J.

S. Alkaabi, S. Yussof, and S. Al-Mulla, Evaluation of Convolutional Neural Network based on Dental Images for Age Estimation. 2019. doi: 10.1109/ICECTA48151.2019.8959665.

L. Čular, M. Tomaic, M. Subašić, T. Šarić, V. Sajkovic, and M. Vodanovic, “Dental age estimation from panoramic X-ray images using statistical models,†Jan. 2017.

N. Mualla, E. Houssein, and M. Hassan, “Dental Age Estimation Based on X-ray images,†Computers, Materials & Continua, vol. 61, pp. 591–605, Jan. 2019, doi: 10.32604/cmc.2020.08580.

S. Sharma, “Artificial Intelligence in Dentistry: The Current Concepts and a Peek into the Future,†International Journal of Contemporary Medical Research [IJCMR], vol. 6, Dec. 2019, doi: 10.21276/ijcmr.2019.6.12.7.

T. Davenport and R. Kalakota, “The potential for artificial intelligence in healthcare,†Future Hosp J, vol. 6, pp. 94–98, Jun. 2019, doi: 10.7861/futurehosp.6-2-94.

J. Bewes, A. Low, A. Morphett, F. Pate, and M. Henneberg, “Artificial intelligence for sex determination of skeletal remains: Application of a deep learning artificial neural network to human skulls,†J Forensic Leg Med, vol. 62, Feb. 2019, doi: 10.1016/j.jflm.2019.01.004.

R. Nagi, A. Konidena, D. Rakesh, S. Jain, N. Kaur, and A. Mann, “Digitization in forensic odontology: A paradigm shift in forensic investigations,†J Forensic Dent Sci, vol. 11, p. 5, Jan. 2019, doi: 10.4103/jfo.jfds_55_19.

P. M. Mahasantipiya, U. Yeesarapat, T. Suriyadet, J. Sricharoen, A. Dumrongwanich, and T. Thaiupathump, “Bite Mark Identification Using Neural Networks: A Preliminary Study,†Lecture Notes in Engineering and Computer Science, vol. 1, Mar. 2011.

K. Wróbel, R. Doroz, P. Porwik, J. Naruniec, and M. Kowalski, “Using a Probabilistic Neural Network for lip-based biometric verification,†Eng Appl Artif Intell, vol. 64, pp. 112–127, Sep. 2017, doi: 10.1016/j.engappai.2017.06.003.

J. Tao et al., “Dental Age Estimation: A Machine Learning Perspective,†2020, pp. 722–733. doi: 10.1007/978-3-030-14118-9_71.

D. Patil et al., “Artificial neural network for gender determination using mandibular morphometric parameters: A comparative retrospective study,†Cogent Eng, vol. 7, Feb. 2020, doi: 10.1080/23311916.2020.1723783.

D. Hammadi, H. Al-Mashhadani, F. Mahmood, and F. Ramo, “Personal Identification System Using Dental Panoramic Radiograph Based on Meta_Heuristic Algorithm,†Apr. 2019.

S. M. Mousavi Kahaki, M. J. Nordin, N. S. Ahmad, M. Arzoky, and W. Ismail, “Deep convolutional neural network designed for age assessment based on orthopantomography data,†Neural Comput Appl, vol. 32, Jul. 2020, doi: 10.1007/s00521-019-04449-6.

Dr. B. Hemalatha, “Intelligent Identification of Dental Age Assessment using Elman Neural Network with Guaranteed Convergence Particle Swarm Optimization,†Int. J. of Aquatic Science, vol. 12, no. 3, pp. 2375–2386, 2021, [Online]. Available:

M. Zaborowicz, K. Zaborowicz, B. Biedziak, and T. Garbowski, “Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters,†Sensors, vol. 22, p. 637, Jan. 2022, doi: 10.3390/s22020637.

F. Sharifonnasabi et al., “Hybrid HCNN-KNN Model Enhances Age Estimation Accuracy in Orthopantomography,†Front Public Health, vol. 10, p. 879418, May 2022, doi: 10.3389/fpubh.2022.879418.

N. Mohammad, A. Muad, R. Ahmad, and M. Y. P. Mohd Yusof, “Accuracy of advanced deep learning with tensorflow and keras for classifying teeth developmental stages in digital panoramic imaging,†BMC Med Imaging, vol. 22, Apr. 2022, doi: 10.1186/s12880-022-00794-6.

C. Mu and G. Li, “Age Estimation using Panoramic Radiographs by Transfer Learning,†Chin J Dent Res, vol. 25, pp. 119–124, Jun. 2022, doi: 10.3290/j.cjdr.b3086341.

M. Parlak Baydoğan, S. Çoşgun Baybars, and S. A. Tuncer, “Age Detection by Deep Learning from Dental Panoramic Radiographs,†Artificial Intelligence Theory and Application, vol. 2, no. 2, pp. 51–58, 2022.

J. Ko et al., “Dental Panoramic Radiography in Age Estimation for Dental Care using Dark-Net 19,†Journal of Magnetics, vol. 27, no. 4, pp. 485–491, 2022, doi: 10.4283/JMAG.2022.27.4.485.

S. S. Aljameel et al., “Predictive Artificial Intelligence Model for Detecting Dental Age Using Panoramic Radiograph Images,†Big Data and Cognitive Computing, vol. 7, no. 1, Mar. 2023, doi: 10.3390/bdcc7010008.

S. AlQahtani, “Dental Age Assessment,†in Forensic Odontology: An Essential Guide, First Edition., C. Adams, R. Carabott, and S. Evans, Eds., Chichester, West Sussex: John Wiley & Sons, Ltd, 2014, pp. 119–124.

D. Ciresan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber, Flexible, High Performance Convolutional Neural Networks for Image Classification. 2011. doi: 10.5591/978-1-57735-516-8/IJCAI11-210.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,†Nature, vol. 521, pp. 436–444, Jan. 2015, doi: 10.1038/nature14539.

J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,†ArXiv, vol. 79, Nov. 2014.

G. Ren, Y. Cao, S. Wen, T. Huang, and Z. Zeng, “A Modified Elman Neural Network with a New Learning Rate Scheme,†Neurocomputing, vol. 286, Apr. 2018, doi: 10.1016/j.neucom.2018.01.046.

Z. Zhang, “Introduction to machine learning: K-nearest neighbors,†Ann Transl Med, vol. 4, p. 218, Jun. 2016, doi: 10.21037/atm.2016.03.37.

J. Huang, A. Smola, A. Gretton, K. Borgwardt, and B. Schölkopf, Correcting Sample Selection Bias by Unlabeled Data., vol. 19. 2006.

M. Sugiyama, T. Suzuki, S. Nakajima, H. Kashima, P. von Bünau, and M. Kawanabe, “Direct importance estimation for covariate shift adaptation,†Ann Inst Stat Math, vol. 60, pp. 699–746, Feb. 2008, doi: 10.1007/s10463-008-0197-x.

O. Day and T. Khoshgoftaar, “A survey on heterogeneous transfer learning,†J Big Data, vol. 4, p. 29, Sep. 2017, doi: 10.1186/s40537-017-0089-0.

J. Memorando, “Evaluation of mandibular third molar for age estimation of Filipino population age 9 - 23 years,†J Forensic Odontostomatol, vol. 1, pp. 26–33, May 2020.

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


The International Journal of Artificial Intelligence Research

Organized by: Departemen Teknik Informatika
Published by: STMIK Dharma Wacana
Jl. Kenanga No.03 Mulyojati 16C Metro Barat Kota Metro Lampung
phone. +62725-7850671
Fax. +62725-7850671


View IJAIR Statcounter

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