References
- Cameriere R, Cunha E, Sassaroli E, Nuzzolese E, Ferrante L. Age estimation by pulp/tooth area ratio in canines: study of a Portuguese sample to test Cameriere's method. Forensic Sci Int 2009; 193: 128.e1-6. https://doi.org/10.1016/j.forsciint.2009.09.011
- Rai A, Acharya AB, Naikmasur VG. Age estimation by pulpto-tooth area ratio using cone-beam computed tomography: a preliminary analysis. J Forensic Dent Sci 2016; 8: 150-4. https://doi.org/10.4103/0975-1475.195118
- Bolanos MV, Manrique MC, Bolanos MJ, Briones MT. Approaches to chronological age assessment based on dental calcification. Forensic Sci Int 2000; 110: 97-106. https://doi.org/10.1016/S0379-0738(00)00154-7
- Cameriere R, Ferrante L, Cingolani M. Precision and reliability of pulp/tooth area ratio (RA) of second molar as indicator of adult age. J Forensic Sci 2004; 49: 1319-23.
- 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: e688-93.
- 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: 1-6.
- Babshet M, Acharya AB, Naikmasur VG. Age estimation in Indians from pulp/tooth area ratio of mandibular canines. Forensic Sci Int 2010; 197: 125.e1-4 https://doi.org/10.1016/j.forsciint.2009.12.065
- Cameriere R, Ferrante L, Belcastro MG, Bonfiglioli B, Rastelli E, Cingolani M. Age estimation by pulp/tooth ratio in canines by peri-apical X-rays. J Forensic Sci 2007; 52: 166-70. https://doi.org/10.1111/j.1556-4029.2006.00336.x
- Kvaal SI, Kolltveit KM, Thomsen IO, Solheim T. Age estimation of adults from dental radiographs. Forensic Sci Int 1995; 74: 175-85. https://doi.org/10.1016/0379-0738(95)01760-G
- 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: 317-9.
- Juneja M, Devi YB, Rakesh N, Juneja S. Age estimation using pulp/tooth area ratio in maxillary canines - a digital image analysis. J Forensic Dent Sci 2014; 6: 160-5.
- Graham JP, O'Donnell CJ, Craig PJ, Walker GL, Hill AJ, Cirillo GN, et al. The application of computerized tomography (CT) to the dental ageing of children and adolescents. Forensic Sci Int 2010; 195: 58-62. https://doi.org/10.1016/j.forsciint.2009.11.011
- Maret D, Molinier F, Braga J, Peters OA, Telmon N, Treil J, et al. Accuracy of 3D reconstructions based on cone beam computed tomography. J Dent Res 2010; 89: 1465-9. https://doi.org/10.1177/0022034510378011
- Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference and prediction. Springer series in statistics. 2rd ed. New York, NY: Springer; 2009.
- Lisboa PJ. A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw 2002; 15: 11-39. https://doi.org/10.1016/S0893-6080(01)00111-3
- Farhadian M, Aliabadi M, Darvishi E. Empirical estimation of the grades of hearing impairment among industrial workers based on new artificial neural networks and classical regression methods. Indian J Occup Environ Med 2015; 19: 84-9. https://doi.org/10.4103/0019-5278.165337
- Devito KL, de Souza Barbosa F, Felippe Filho WN. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2008; 106: 879-84. https://doi.org/10.1016/j.tripleo.2008.03.002
- Moghimi S, Talebi M, Parisay I. Design and implementation of a hybrid genetic algorithm and artificial neural network system for predicting the sizes of unerupted canines and premolars. Eur J Orthod 2012; 34: 480-6. https://doi.org/10.1093/ejo/cjr042
- Eskandarloo A, Mirshekari A, Poorolajal J, Mohammadi Z, Shokri A. Comparison of cone-beam computed tomography with intraoral photostimulable phosphor imaging plate for diagnosis of endodontic complications: a simulation study. Oral Surg Oral Med Oral Pathol Oral Radiol 2012; 114: e54-61.
- Singaraju S, Sharda P. Age estimation using pulp-tooth area ratio: a digital image analysis. J Forensic Dent Sci 2009; 1: 37-41. https://doi.org/10.4103/0974-2948.50888
- De Angelis D, Gaudio D, Guercini N, Cipriani F, Gibelli D, Caputi S, et al. Age estimation from canine volumes. Radiol Med 2015; 120: 731-6. https://doi.org/10.1007/s11547-015-0521-5
- Bagherpour A, Anbiaee N, Partovi P, Golestani S, Afzalinasab S. Dental age assessment of young Iranian adults using third molars: a multivariate regression study. J Forensic Leg Med 2012; 19: 407-12. https://doi.org/10.1016/j.jflm.2012.04.009
- Babyak MA. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med 2004; 66: 411-21. https://doi.org/10.1097/01.psy.0000127692.23278.a9
- 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. https://doi.org/10.1016/j.forsciint.2017.03.007
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