Dental Radiography in Age Determination: Contemporary Methods and Trends
The determination of an individual's age assumes paramount significance in forensic and legal contexts, necessitating the utilization of diverse techniques. Dental radiography emerges as a non-invasive approach for determining age-related dental changes. This method grants a comprehensive analysis of various dental features to identify an individual’s precise age, place them within designated age ranges, or define whether they exceed or subordinate to specific age thresholds. This review summarizes age estimation methodologies using dental radiography and conducts the investigations into contemporary trends by reviewing relevant studies published in Pubmed between 2020 and 2023. Age categorization delineates into three distinct phases: pre-natal, neo-natal, and post-natal; childhood and adolescence; and adulthood. Panoramic radiography becomes the predominant radiographic modality, with the Demirjian method is more commonly known for age estimation age in the initial two phases. In contrast, adulthood age estimation relies on anatomical changes. Significantly, artificial intelligence (AI) technology has recently attracted attention for age estimation, yielding promising results. AI demonstrates the potential to enhance the accuracy of conventional methodologies, diminishing human errors and mitigating associated workload burdens, offering inventive ground for future advancements.
1. Hill AJ, Lain R, Hewson I. Preservation of dental evidence following exposure to high temperatures. Forensic Sci Int. 2011;205(1-3):40-3.
2. Kieser J, de Feijter J, TeMoananui R. Automated dental aging for child victims of disasters. Am J Disaster Med. 2008;3(2):109-12.
3. Pramod JB, Marya A, Sharma V. Role of forensic odontologist in post mortem person identification. Dent Res J (Isfahan). 2012;9(5):522-30.
4. Yun JI, Lee JY, Chung JW, Kho HS, Kim YK. Age estimation of Korean adults by occlusal tooth wear. J Forensic Sci. 2007;52(3):678-83.
5. Lu CK, Yee MCS, Ravi SB, Pandurangappa R. Forensic age estimation of Chinese Malaysian adults by evaluating occlusal tooth wear using modified Kim's index. Int J Dent. 2017;2017:4265753.
6. Milošević D, Vodanović M, Galić I, Subašić M. Automated estimation of chronological age from panoramic dental x-ray images using deep learning. Expert Syst Appl. 2022; 189:116038.
7. Helfman P BJ. Aspartic acid racemisation in dentine as a measure of ageing. Nature. 1976;262:279-81.
8. Ohtani S. Estimation of age from dentin by using the racemization reaction of aspartic acid. Am J Forensic Med Pathol. 1995;16(2):158-61.
9. Permsuwan R, Verochana K, Mahakkanukrauh P, Jaikang C, Srilesin C, Intui K, et al. Age estimation using aspartic acid racemization in various forensic samples: a preliminary study. Chiang Mai. Med. J. 2020;59(2):53-9.
10. Duangto P, Janhom A, Prasitwattanaseree S, Mahakkanukrauh P, Iamaroon A. New prediction models for dental age estimation in Thai children and adolescents. Forensic Sci Int. 2016;266:583 e1-83 e5.
11. Jayaraman J, Roberts GJ, Wong HM, King NM. Dental age estimation in southern Chinese population using panoramic radiographs: validation of three population specific reference datasets. BMC Med Imaging. 2018;18(1):5.
12. Jayaraman J, Wong HM, Roberts GJ, King NM, Cardoso HFV, Velusamy P, et al. Age estimation in three distinct east Asian population groups using southern Han Chinese dental reference dataset. BMC Oral Health. 2019;19(1):242.
13. Yang Z, Geng K, Liu Y, Sun S, Wen D, Xiao J, et al. Accuracy of the Demirjian and Willems methods of dental age estimation for children from central southern China. Int J Legal Med. 2019;133(2):593-601.
14. Han MQ, Jia SX, Wang CX, Chu G, Chen T, Zhou H, et al. Accuracy of the Demirjian, Willems and Nolla methods for dental age estimation in a northern Chinese population. Arch Oral Biol. 2020;118:104875.
15. Shen C, Pan J, Yang Z, Mou H, Tao J, Ji F. Applicability of 2 dental age estimation methods to Taiwanese population. Am J Forensic Med Pathol. 2020;41(4):269-75.
16. Jayaraman J, Wong HM, King NM, Roberts GJ. Development of a reference data set (RDS) for dental age estimation (DAE) and testing of this with a separate validation set (VS) in a southern Chinese population. J Forensic Leg Med. 2016;43:26-33.
17. Manjunatha BS, Soni NK. Estimation of age from development and eruption of teeth. J Forensic Dent Sci. 2014;6(2):73-6.
18. AlQahtani SJ, Hector MP, Liversidge HM. Brief communication: The London atlas of human tooth development and eruption. Am J Phys Anthropol. 2010;142(3):481-90.
19. Duangto P, Janhom A, Prasitwattanaseree S, Iamaroon A. New equations for age estimation using four permanent mandibular teeth in Thai children and adolescents. Int J Legal Med. 2018;132(6):1743-7.
20. Kim S, Lee YH, Noh YK, Park FC, Auh QS. Author Correction: Age-group determination of living individuals using first molar images based on artificial intelligence. Sci Rep. 2022;12(1):2332.
21. Seyedashrafi M, Payahoo S, Noorizade A, Noruzi M. Relationship of Morphological Changes of the First Molar Pulp Chamber and Mineralization of Developing Third Molar with Cervical Vertebral Maturation on Panoramic Radiographs and Lateral Cephalograms. Int J Clin Ski. 2019;13(2):278-87.
22. Someda H, Saka H, Matsunaga S, Ide Y, Nakahara K, Hirata S, et al. Age estimation based on three-dimensional measurement of mandibular central incisors in Japanese. Forensic Sci Int. 2009;185(1-3):110-4.
23. Shen S, Liu Z, Wang J, Fan L, Ji F, Tao J. Machine learning assisted Cameriere method for dental age estimation. BMC Oral Health. 2021;21(1):641.
24. Shah PH, Venkatesh R. Pulp/tooth ratio of mandibular first and second molars on panoramic radiographs: an aid for forensic age estimation. J Forensic Dent Sci. 2016;8(2):112.
25. Li MJ, Chu G, Han MQ, Chen T, Zhou H, Guo YC. Application of the Kvaal method for age estimation using digital panoramic radiography of Chinese individuals. Forensic Sci Int. 2019;301:76-81.
26. Marroquin TY, Karkhanis S, Kvaal SI, Vasudavan S, Kruger E, Tennant M. Age estimation in adults by dental imaging assessment systematic review. Forensic Sci Int. 2017;275:203-11.
27. Kvaal SI, Kolltveit KM, Thomsen IO, Solheim T. Age estimation of adults from dental radiographs. Forensic Sci Int. 1995;74(3):175-85.
28. Joseph C, Reddy BH, Cherian N , Kannan S, George G. Intraoral digital radiography for adult age estimation: a reliable technique. J Indian Acad Oral Med Radiol. 2013;25(4):287-90.
29. Li M, Zhao J, Chen W, Chen X, Chu G, Chen T, et al. Can canines alone be used for age estimation in Chinese individuals when applying the Kvaal method? Forensic Sci Res. 2022;7(2):132-7.
30. Alharbi HS, Sr., Alharbi AM, Alenazi AO, Kolarkodi SH, Elmoazen R. Age estimation by Kvaal's method using digital panoramic radiographs in the Saudi population. Cureus. 2022;14(4):e23768.
31. Zdravkovic D, Jovanovic M, Papic M, Ristic V, Milojevic Samanovic A, Kocovic A, et al. Application of the Kvaal method in age estimation of the Serbianp Based on dental radiographs. Diagnostics (Basel). 2022;12(4):911.
32. Miranda JC, Azevedo ACS, Rocha M, Michel-Crosato E, Biazevic MGH. Age estimation in Brazilian adults by Kvaal's and Cameriere's methods. Braz Oral Res. 2020;34:e051.
33. Mengjun Z, Xiaogang C, Lei S, Ting L, Fei F, Kui Z, et al. Age estimation in Western Chinese adults by pulp–tooth volume ratios using cone-beam computed tomography. Aust J Forensic Sci. 2021;53(6):681-92.
34. AlQarni S, Chandrashekar G, Bumann EE, Lee Y. Incremental learning for panoramic radiograph segmentation. Annu Int Conf IEEE Eng Med Biol Soc. 2022;2022:557-61.
35. Wallraff S, Vesal S, Syben C, Lutz R, Maier A. Age estimation on panoramic dental X-ray images using deep learning. In: Palm C, Deserno TM, Handels H, Maier A, Maier-Hein K, Tolxdorff T, editor(s). Bildverarbeitung für die Medizin 2021. Informatik aktuell. Springer Vieweg, Wiesbaden.;2021.p.186-91.
36. Vila-Blanco N, Varas-Quintana P, Aneiros-Ardao A, Tomas I, Carreira MJ. XAS: Automatic yet eXplainable age and sex determination by combining imprecise per-tooth predictions. Comput Biol Med. 2022;149:106072.
37. Pham CV, Lee SJ, Kim SY, Lee S, Kim SH, Kim HS. Age estimation based on 3D post-mortem computed tomography images of mandible and femur using convolutional neural networks. PLoS One. 2021;16(5):e0251388.
38. Stavrianos C, Mastagas D, Stavrianou I, Karaiskou O. Dental age estimation of adults: a review of methods and principals. Res J Med Sci. 2008; 2(5):258-68.
39. Nayyar A, Babu B, Krishnaveni B, Devi M, Gayitri HC. Age estimation: Current state and research challenges. J Med Sci. 2016;36(6):209-16.
40. Kraus BS, Jordan RE. The human dentitionb birth. Philadelphia: Lea and Febiger; 1965.
41. Nolla CM. The development of the permanent teeth. J Dent Child. 1960; 27:254–66.
42. Demirjian A, Goldstein H, Tanner JM. A new system of dental age assessment. Hum Biol. 1973;45(2):211-27.
43. Demirjian A, Goldstein H. New systems for dental maturity based on seven and four teeth. Ann Hum Biol. 1976;3(5):411-21.
44. Willems G, Van Olmen A, Spiessens B, Carels C. Dental age estimation in Belgian children: Demirjian's technique revisited. J Forensic Sci. 2001;46(4):893-5.
45. Mónico LS, Tomás LF, Tomás I, Varela-Patiño P, Martin-Biedma B. Adapting Demirjian standards for Portuguese and Spanish children and adolescents. Int J Environ Res Public Health. 2022;19(19):12706.
46. Duangto P, Janhom A, Iamaroon A. Age estimation using the London Atlas in a Thai population. Aust J Forensic Sci. 2023;55(6):700-7.
47. Lin Y, Maimaitiyiming N, Sui M, Abuduxiku N, Tao J. Performance of the London Atlas, Willems, and a new quick method for dental age estimation in Chinese Uyghur children. BMC Oral Health. 2022;22(1):624.
48. Sharma P, Wadhwan V. Comparison of accuracy of age estimation in Indian children by measurement of open apices in teeth with the London Atlas of tooth development. J Forensic Odontostomatol. 2020;38(1):39-47.
49. Cameriere R, Ferrante L, Cingolani M. Age estimation in children by measurement of open apices in teeth. Int J Legal Med. 2006;120(1):49-52.
50. Cameriere R, Ferrante L, De Angelis D, Scarpino F, Galli F. The comparison between measurement of open apices of third molars and Demirjian stages to test chronological age of over 18 year olds in living subjects. Int J Legal Med. 2008;122(6):493-7.
51. De Micco F, Martino F, Velandia Palacio LA, Cingolani M, Campobasso CP. Third molar maturity index and legal age in different ethnic populations: Accuracy of Cameriere's method. Med Sci Law. 2021;61(1 suppl):105-12.
52. Ivan G, Tomislav L, Hrvoje B, Marin V, Elizabeta G, Maria Gabriela Haye B, et al. Cameriere's third molar maturity index in assessing age of majority. Forensic Sci Int. 2015;252:191.e1-91.e5.
53. Zelic K, Galic I, Nedeljkovic N, Jakovljevic A, Milosevic O, Djuric M, et al. Accuracy of Cameriere's third molar maturity index in assessing legal adulthood on Serbian population. Forensic Sci Int. 2016;259:127-32.
54. Ikeda N, Umetsu K, Kashimura S, Suzuki T, Oumi M. Estimation of age from teeth with their soft X-ray findings. Jpn J Leg Med. 1985;39(3):244-50.
55. Gotmare SS, Shah T, Periera T, Waghmare MS, Shetty S, Sonawane S, et al. The coronal pulp cavity index: a forensic tool for age determination in adults. Dent Res J (Isfahan). 2019;16(3):160-5.
56. Cameriere R, Ferrante L, Cingolani M. Variations in pulp/tooth area ratio as an indicator of age: a preliminary study. J Forensic Sci. 2004;49(2):317-9.
57. Yang F, Jacobs R, Willems G. Dental age estimation through volume matching of teeth imaged by cone-beam CT. Forensic Sci Int. 2006;159 (Suppl 1):S78-83.
58. Jagannathan N, Neelakantan P, Thiruvengadam C, Ramani P, Premkumar P, Natesan A, et al. Age estimation in an Indian population using pulp/tooth volume ratio of mandibular canines obtained from cone beam computed tomography. J Forensic Odontostomatol. 2011;29(1):1-6.
59. Biuki N, Razi T, Faramarzi M. Relationship between pulp-tooth volume ratios and chronological age in different anterior teeth on CBCT. J Clin Exp Dent. 2017;9(5):e688-e93.
60. Kazmi S, Mânica S, Revie G, Shepherd S, Hector M. Age estimation using canine pulp volumes in adults: a CBCT image analysis. Int J Legal Med. 2019;133(6):1967-76.
61. Olze A, Solheim T, Schulz R, Kupfer M, Schmeling A. Evaluation of the radiographic visibility of the root pulp in the lower third molars for the purpose of forensic age estimation in living individuals. Int J Legal Med. 2010;124(3):183-6.
62. Guo YC, Han M, Chi Y, Long H, Zhang D, Yang J, et al. Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images. Int J Legal Med. 2021;135(4):1589-97.
63. 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 artificial intelligence-based approaches. Int J Legal Med. 2023;137(4):1117-46.
64. Wang R, Chaudhari P, Davatzikos C. Embracing the disharmony in medical imaging: a simple and effective framework for domain adaptation. Med Image Anal. 2022;76:102309.
65. Albernaz Neves J, Antunes-Ferreira N, Machado V, Botelho J, Proença L, Quintas A, et al. Validation of the Third Molar Maturation Index (I3M) to assess the legal adult age in the Portuguese population. Sci Rep. 2020;10(1):18466.
66. Cameriere R, Velandia Palacio LA, Marchetti M, Baralla F, Cingolani M, Ferrante L. Child brides: the age estimation problem in young girls. J Forensic Odontostomatol. 2020;38(3):2-7.
67. Gonçalves do Nascimento L, Ribeiro Tinoco RL, Lacerda Protasio AP, Arrais Ribeiro IL, Marques Santiago B, Cameriere R. Age estimation in north east Brazilians by measurement of open apices. J Forensic Odontostomatol. 2020;38(2):2-11.
68. Helmy MA, Osama M, Elhindawy MM, Mowafey B. Volume analysis of second molar pulp chamber using cone beam computed tomography for age estimation in Egyptian adults. J Forensic Odontostomatol. 2020;38(3):25-34.
69. Marrero-Ramos MD, López-Urquía L, Suárez-Soto A, Sánchez-Villegas A, Vicente-Barrero M. Estimation of the age of majority through radiographic evaluation of the third molar maturation degree. Med Oral Patol Oral Cir Bucal. 2020;25(3):e359-e63.
70. Memorando JR. Evaluation of mandibular third molar for age estimation of Filipino population age 9 - 23 years. J Forensic Odontostomatol. 2020;38(1):26-33.
71. Paz Cortés MM, Rojo R, Alía García E, Mourelle Martínez MR. Accuracy assessment of dental age estimation with the Willems, Demirjian and Nolla methods in Spanish children: Comparative cross-sectional study. BMC Pediatr. 2020;20(1):361.
72. Rezende Machado AL, Borges BS, Cameriere R, Palhares Machado CE, Alves da Silva RE. Evaluation of Cameriere and Willems age estimation methods in panoramic radiographs of Brazilian children. J Forensic Odontostomatol. 2020;38(3):8-15.
73. Scendoni R, Zolotenkova GV, Vanin S, Pigolkin YI, Cameriere R. Forensic validity of the third molar maturity index (I3M) for age estimation in a Russian population. Biomed Res Int. 2020;2020:6670590.
74. Sheriff SO, Medapati RH, Ankisetti SA, Gurrala VR, Haritha K, Pulijala S, et al. Testing the accuracy of Bedek et al's new models based on 1-to-7 mandibular teeth for age estimation in 7-15 year old south Indian children. J Forensic Odontostomatol. 2020;38(2):22-39.
75. 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.
76. Yang Z, Fan L, Kwon K, Pan J, Shen C, Tao J, et al. Age estimation for children and young adults by volumetric analysis of upper anterior teeth using cone-beam computed tomography data. Folia Morphol (Warsz). 2020;79(4):851-9.
77. Yassin SM, Alalmai BAM, Ali Huaylah SH, Althobati MK, Alhamdi FMA, Togoo RA. Accuracy of estimating chronological age from Nolla's method of dental age estimation in a population of Southern Saudi Arabian children. Niger J Clin Pract. 2020;23(12):1753-8.
78. Alqerban A, Alrashed M, Alaskar Z, Alqahtani K. Age estimation based on Willems method versus country specific model in Saudi Arabia children and adolescents. BMC Oral Health. 2021;21(1):341.
79. Franco R, Franco A, Turkina A, Arakelyan M, Arzukanyan A, Velenko P, et al. Radiographic assessment of third molar development in a Russian population to determine the age of majority. Arch Oral Biol. 2021;125:105102.
80. Kim S, Lee YH, Noh YK, Park FC, Auh QS. Age-group determination of living individuals using first molar images based on artificial intelligence. Sci Rep. 2021;11(1):1073.
81. Manthapuri S, Bheemanapalli SR, Namburu LP, Kunchala S, Vankdoth D, Balla SB, et al. Can root pulp visibility in mandibular first molars be used as an alternative age marker at the 16 year threshold in the absence of mandibular third molars: an orthopantomographic study in a South Indian sample. J Forensic Odontostomatol. 2021;39(2):21-31.
82. Pan J, Shen C, Yang Z, Fan L, Wang M, Shen S, et al. A modified dental age assessment method for 5- to 16-year-old eastern Chinese children. Clin Oral Investig. 2021;25(6):3463-74.
83. Pinchi V, Bianchi I, Pradella F, Vitale G, Focardi M, Tonni I, et al. Dental age estimation in children affected by juvenile rheumatoid arthritis. Int J Legal Med. 2021;135(2):619-29.
84. Pires AC, Vargas de Sousa Santos RF, Pereira CP. Dental age assessment by the pulp/tooth area proportion in cone beam computed tomography: is medico-legal application for age estimation reliable? J Forensic Odontostomatol. 2021;39(2):2-14.
85. Pyata JR, Kandukuri BA, Gangavarapu U, Anjum B, Chinnala B, Bojji M, et al. Accuracy of four dental age estimation methods in determining the legal age threshold of 18 years among South Indian adolescents and young. J Forensic Odontostomatol. 2021;39(3):2-15.
86. Rebouças PRM, Alencar CRB, Arruda M, Lacerda RHW, Melo DP, Bernardino Í M, et al. Identification of dental calcification stages as a predictor of skeletal development phase. Dental Press J Orthod. 2021;26(4):e2119292.
87. Saranya K, Ponnada SR, Cheruvathoor JJ, Jacob S, Kandukuri G, Mudigonda M, et al. Assessing the probability of having attained 16 years of age in juveniles using third molar development in a sample of South Indian population. J Forensic Odontostomatol. 2021;39(1):16-23.
88. Tantanapornkul WB, Kaomongkolgit R, Tohnak S, Deepho C, Chansamat R. Dental age assessment based on the radiographic visibility of the periodontal ligament in lower third molars in a Thai sample. J Forensic Odontostomatol. 2021;39(2):32-7.
89. Thilak JT, Manisha KM, Sapna DR, Nivedita C. Evaluation of third molar maturity index (I3M) in assessing the legal age of subjects in an Indian Goan population. J Forensic Odontostomatol. 2021;39(3):16-24.
90. Timme M, Borkert J, Nagelmann N, Streeter A, Karch A, Schmeling A. Age-dependent decrease in dental pulp cavity volume as a feature for age assessment: a comparative in vitro study using 9.4-T UTE-MRI and CBCT 3D imaging. Int J Legal Med. 2021;135(4):1599-609.
91. Zirk M, Zoeller JE, Lentzen MP, Bergeest L, Buller J, Zinser M. Comparison of two established 2D staging techniques to their appliance in 3D cone beam computer-tomography for dental age estimation. Sci Rep. 2021;11(1):9024.
92. Bedek I, Dumančić J, Lauc T, Marušić M, Čuković-Bagić I. Applicability of the Demirjian, Willems and Haavikko methods in Croatian children. J Forensic Odontostomatol. 2022;40(2):21-30.
93. Boedi RM, Ermanto H, Skripsa TH, Prabowo YB. Application of third molar maturity index for Indonesia minimum legal age of marriage: a pilot study. J Forensic Odontostomatol. 2022;40(1):12-9.
94. Briem Stamm AD, Cariego MT, Vazquez DJ, Pujol MH, Saiegh J, Bielli MV, et al. Use of the Demirjian method to estimate dental age in panoramic radiographs of patients treated at the Buenos Aires University School of Dentistry. Acta Odontol Latinoam. 2022;35(1):25-30.
95. Caggiano M, Scelza G, Amato A, Orefice R, Belli S, Pagano S, et al. Estimating the 18-Year Threshold with third molars radiographs in the Southern Italy population: accuracy and reproducibility of Demirjian method. Int J Environ Res Public Health. 2022;19(16):10454.
96. Gunacar DN, Bayrak S, Sinanoglu EA. Three-dimensional verification of the radiographic visibility of the root pulp used for forensic age estimation in mandibular third molars. Dentomaxillofac Radiol. 2022;51(3):20210368.
97. Kuremoto K, Okawa R, Matayoshi S, Kokomoto K, Nakano K. Estimation of dental age based on the developmental stages of permanent teeth in Japanese children and adolescents. Sci Rep. 2022;12(1):3345.
98. Lee YH, Won JH, Auh QS, Noh YK. Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms. Sci Rep. 2022;12(1):11703.
99. Melo M, Ata-Ali F, Ata-Ali J, Martinez Gonzalez JM, Cobo T. Demirjian and Cameriere methods for age estimation in a Spanish sample of 1386 living subjects. Sci Rep. 2022;12(1):2838.
100. Merdietio Boedi R, Shepherd S, Oscandar F, Mânica S, Franco A. Regressive changes of crown-root morphology and their volumetric segmentation for adult dental age estimation. J Forensic Sci. 2022;67(5):1890-8.
101. Milani S, Shahrabi M, H BF, Parvar S, Abdolahzadeh M. Accuracy of Demirjian's and Cameriere's Methods for Age Estimation in 6- to 10-Year-Old Iranian Children Using Panoramic Radiographs. Int J Dent. 2022;2022:4948210.
102. Oh S, Kumagai A, Kim SY, Lee SS. Accuracy of age estimation and assessment of the 18-year threshold based on second and third molar maturity in Koreans and Japanese. PLoS One. 2022;17(7):e0271247.
103. Parvathala P, Chittamuru NR, Kakumanu NR, Yadav L, Hamid Ali S, Ali S, et al. Testing the maturation and the radiographic visibility of the root pulp of mandibular third molars for predicting 21 years. A digital panoramic radiographic study in emerging adults of south Indian origin. J Forensic Odontostomatol. 2022;40(3):22-33.
104. Prakoeswa B, Kurniawan A, Chusida A, Marini MI, Rizky BN, Margaretha MS, et al. Children and adolescent dental age estimation by the Willems and Al Qahtani methods in Surabaya, Indonesia. Biomed Res Int. 2022;2022:9692214.
105. Santos MA, Muinelo-Lorenzo J, Fernández-Alonso A, Cruz-Landeira A, Aroso C, Suárez-Cunqueiro MM. Age estimation using maxillary central incisor analysis on cone beam computed tomography human images. Int J Environ Res Public Health. 2022;19(20):13370.
106. Santosh KC, Pradeep N, Goel V, Ranjan R, Pandey E, Shukla PK, et al. Machine learning techniques for human age and gender identification based on teeth x-ray images. J Healthc Eng. 2022;2022:8302674.
107. Shah PH, Venkatesh R, More CB. Age estimation in Western Indian population by Cameriere's and Drusini's methods. J Oral Maxillofac Pathol. 2022;26(1):116-20.
108. Shan W, Sun Y, Hu L, Qiu J, Huo M, Zhang Z, et al. Boosting algorithm improves the accuracy of juvenile forensic dental age estimation in southern China population. Sci Rep. 2022;12(1):15649.
109. Sharifonnasabi F, Jhanjhi NZ, John J, Obeidy P, Band SS, Alinejad-Rokny H, et al. Hybrid HCNN-KNN model enhances age estimation accuracy in orthopantomography. Front Public Health. 2022;10:879418.
110. Shen S, Yuan X, Wang J, Fan L, Zhao J, Tao J. Evaluation of a machine learning algorithms for predicting the dental age of adolescent based on different preprocessing methods. Front Public Health. 2022;10:1068253.
111. Wang J, Fan L, Shen S, Sui M, Zhou J, Yuan X, et al. Comparative assessment of the Willems dental age estimation methods: a Chinese population-based radiographic study. BMC Oral Health. 2022;22(1):373.
112. Wang X, Liu Y, Miao X, Chen Y, Cao X, Zhang Y, et al. DENSEN: a convolutional neural network for estimating chronological ages from panoramic radiographs. BMC Bioinformatics. 2022;23(Suppl 3):426.
113. Zaborowicz K, Garbowski T, Biedziak B, Zaborowicz M. Robust estimation of the chronological age of children and adolescents using tooth geometry indicators and POD-GP. Int J Environ Res Public Health. 2022;19(5):2952.
114. Abdul Rahim AH, Davies JA, Liversidge HM. Reliability and limitations of permanent tooth staging techniques. Forensic Sci Int. 2023;346:111654.
115. AlOtaibi NN, AlQahtani SJ. Performance of different dental age estimation methods on Saudi children. J Forensic Odontostomatol. 2023;41(1):27-46.
116. Bjørk MB, Kvaal SI, Bleka Ø, Sakinis T, Tuvnes FA, Haugland MA, et al. Age prediction in sub-adults based on MRI segmentation of 3rd molar tissue volumes. Int J Legal Med. 2023;137(3):753-63.
117. Bui R, Iozzino R, Richert R, Roy P, Boussel L, Tafrount C, et al. Artificial intelligence as a decision-making tool in forensic dentistry: a pilot study with I3M. Int J Environ Res Public Health. 2023;20(5):4620.
118. El-Desouky SS, Kabbash IA. Age estimation of children based on open apex measurement in the developing permanent dentition: an Egyptian formula. Clin Oral Investig. 2023;27(4):1529-39.
119. Kim YR, Choi JH, Ko J, Jung YJ, Kim B, Nam SH, et al. Age group classification of dental radiography without precise age information using convolutional neural networks. Healthcare (Basel). 2023;11(8):1068.
120. Kuhnen B, Fernandes C, Barros F, Scarso Filho J, Gonçalves M, Serra MDC. Chronology of permanent teeth mineralization in Brazilian individuals: age estimation tables. BMC Oral Health. 2023;23(1):165.
121. Kumagai A, Jeong S, Kim D, Kong HJ, Oh S, Lee SS. Validation of data mining models by comparing with conventional methods for dental age estimation in Korean juveniles and young adults. Sci Rep. 2023;13(1):726.
122. Kwon K, Pan J, Guo Y, Ren Q, Yang Z, Tao J, et al. Demirjian method and Willems method to study the dental age of adolescents in Shanghai before and after 10 years. Folia Morphol (Warsz). 2023;82(2):346-58.
123. Lopatin O, Barszcz M, Woźniak KJ. Skeletal and dental age estimation via postmortem computed tomography in Polish subadults group. Int J Legal Med. 2023;137(4):1147-59.
124. Merdietio Boedi R, Shepherd S, Oscandar F, Mânica S, Franco A. 3D segmentation of dental crown for volumetric age estimation with CBCT imaging. Int J Legal Med. 2023;137(1):123-30.
125. Sharma S, Karjodkar F, Sansare K, Mehra A, Sharma A, Saalim M. Age estimation using the tooth coronal index on mandibular first premolars on digital panoramic radiographs in an Indian population. Front Dent. 2023;20(6):1-7.
126. Timme M, Viktorov J, Steffens L, Streeter A, Karch A, Schmeling A. Third molar eruption in orthopantomograms as a feature for forensic age assessment-a comparison study of different classification systems. Int J Legal Med. 2023;137(3):765-72.
127. Topal BG, Tanrikulu A. Assessment of permanent teeth development in children with multiple persistent primary teeth. J Clin Pediatr Dent. 2023;47(2):50-7.
128. Vangala RM, Loshali A, Basa KS, Ch G, Masthan S, Ganachari BC, et al. Validation of radiographic visibility of root pulp in mandibular first, second and third molars in the prediction of 21 years in a sample of south Indian population: a digital panoramic radiographic study. J Forensic Odontostomatol. 2023;41(1):47-56.
129. Wang J, Dou J, Han J, Li G, Tao J. A population-based study to assess two convolutional neural networks for dental age estimation. BMC Oral Health. 2023;23(1):109.
130. Ye X, Jiang F, Sheng X, Huang H, Shen X. Dental age assessment in 7-14-year-old Chinese children: comparison of Demirjian and Willems methods. Forensic Sci Int. 2014;244:36-41.