The korean journal of orthodontics (대한치과교정학회지)

The Korean Association Of Orthodontists (대한치과교정학회)
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Artificial neural network model for predicting sex using dental and orthodontic measurements

  • Sandra Anic-Milosevic (Department of Orthodontics, School of Dental Medicine, University of Zagreb) ;
  • Natasa Medancic (Private Practice Policlinic IMED) ;
  • Martina Calusic-Sarac (Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek) ;
  • Jelena Dumancic (Department of Dental Anthropology, School of Dental Medicine, University of Zagreb) ;
  • Hrvoje Brkic (Department of Dental Anthropology, School of Dental Medicine, University of Zagreb)
  • Received : 2022.11.24
  • Accepted : 2023.04.25
  • Published : 2023.05.25
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Abstract

Objective: To investigate sex-specific correlations between the dimensions of permanent canines and the anterior Bolton ratio and to construct a statistical model capable of identifying the sex of an unknown subject. Methods: Odontometric data were collected from 121 plaster study models derived from Caucasian orthodontic patients aged 12-17 years at the pretreatment stage by measuring the dimensions of the permanent canines and Bolton's anterior ratio. Sixteen variables were collected for each subject: 12 dimensions of the permanent canines, sex, age, anterior Bolton ratio, and Angle's classification. Data were analyzed using inferential statistics, principal component analysis, and artificial neural network modeling. Results: Sex-specific differences were identified in all odontometric variables, and an artificial neural network model was prepared that used odontometric variables for predicting the sex of the participants with an accuracy of > 80%. This model can be applied for forensic purposes, and its accuracy can be further improved by adding data collected from new subjects or adding new variables for existing subjects. The improvement in the accuracy of the model was demonstrated by an increase in the percentage of accurate predictions from 72.0-78.1% to 77.8-85.7% after the anterior Bolton ratio and age were added. Conclusions: The described artificial neural network model combines forensic dentistry and orthodontics to improve subject recognition by expanding the initial space of odontometric variables and adding orthodontic parameters.

Keywords

Acknowledgement

This research was funded by the Croatian Science Foundation under the project IP-2020-02-9423 - Tooth analysis in forensic and archaeological research.

References

  1. Dietrichkeit Pereira JG, Lima KF, Alves da Silva RH. Mandibular measurements for sex and age estimation in Brazilian sampling. Acta Stomatol Croat 2020;54:294-301. https://doi.org/10.15644/asc54/3/7 
  2. Kieser JA. Human adult odontometrics. Cambridge: Cambridge University Press; 1990. https://doi.org/10.1017/CBO9780511983610 
  3. Machado V, Botelho J, Mascarenhas P, Mendes JJ, Delgado A. A systematic review and metaanalysis on Bolton's ratios: normal occlusion and malocclusion. J Orthod 2020;47:7-29. https://doi.org/10.1177/1465312519886322 
  4. Capitaneanu C, Willems G, Jacobs R, Fieuws S, Thevissen P. Sex estimation based on tooth measurements using panoramic radiographs. Int J Legal Med 2017;131:813-21. https://doi.org/10.1007/s00414-016-1434-0 
  5. Moon G, Sathawane R, Chandak R, Lanjekar A, Bhakte K, Sukhdeve V. Sex determination by odontometric diagonal measurements using discriminant function. J Indian Acad Oral Med Radiol 2021;33:208-14. https://doi.org/10.4103/jiaomr.jiaomr_260_20 
  6. Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci 2016;374:20150202. https://doi.org/10.1098/rsta.2015.0202 
  7. Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform 2002;35:352-9. https://doi.org/10.1016/s1532-0464(03)00034-0 
  8. Gamulin O, Skrabic M, Serec K, Par M, Bakovic M, Krajacic M, et al. Possibility of human gender recognition using Raman spectra of teeth. Molecules 2021;26:3983. https://doi.org/10.3390/molecules26133983 
  9. Shanmuganathan S. Artificial neural network modelling: an introduction. In: Shanmuganathan S, Samarasinghe S, eds. Artificial neural network modelling. Cham: Springer; 2016. p. 1-14. https://link.springer.com/chapter/10.1007/978-3-319-28495-8_1 
  10. Bewes J, Low A, Morphett A, Pate FD, Henneberg M. Artificial intelligence for sex determination of skeletal remains: application of a deep learning artificial neural network to human skulls. J Forensic Leg Med 2019;62:40-3. https://doi.org/10.1016/j.jflm.2019.01.004 
  11. Karamizadeh S, Abdullah SM, Manaf AA, Zamani M, Hooman A. An overview of principal component analysis. J Signal Inf Process 2013;4:173-5. https://doi.org/10.4236/jsip.2013.43B031 
  12. Adams D, Pilloud M. Sex estimation from dental crown and cervical metrics in a contemporary Japanese sample. Forensic Anthropol 2019;2:222-32. https://doi.org/10.5744/fa.2019.1008 
  13. Vodanovic M, Demo Z, Njemirovskij V, Keros J, Brkic H. Odontometrics: a useful method for sex determination in an archaeological skeletal population? J Archaeol Sci 2007;34:905-13. https://doi. org/10.1016/j.jas.2006.09.004 
  14. Agrawal A, Manjunatha BS, Dholia B, Althomali Y. Comparison of sexual dimorphism of permanent mandibular canine with mandibular first molar by odontometrics. J Forensic Dent Sci 2015;7:238-43. https://pubmed.ncbi.nlm.nih.gov/26816466/  https://doi.org/10.4103/0975-1475.172449
  15. Al Rifaiy MQ, Abdullah MA, Ashraf I. Dimorphism of mandibular and maxillary canine teeth in establishing sex identity. Saudi Dent J 1997;9:17-20. https://faculty.ksu.edu.sa/en/malrifaiy/publication/130693 
  16. Alam MK, Alzarea BK, Ganji KK, Kundi I, Patil S. 3D CBCT human adult odontometrics: comparative assessment in Saudi, Jordan and Egypt population. Saudi Dent J 2019;31:336-42. https://doi.org/10.1016/j.sdentj.2019.03.007 
  17. Kuntz TR, Staley RN, Bigelow HF, Kremenak CR, Kohout FJ, Jakobsen JR. Arch widths in adults with Class I crowded and Class III malocclusions compared with normal occlusions. Angle Orthod 2008;78:597-603. https://doi.org/10.2319/0003-3219(2008)078[0597:AWIAWC]2.0.CO;2 
  18. Strujic M, Anic-Milosevic S, Mestrovic S, Slaj M. Tooth size discrepancy in orthodontic patients among different malocclusion groups. Eur J Orthod 2009;31:584-9. https://doi.org/10.1093/ejo/cjp013 
  19. Hashim H, Dweik YG, Al-Hussain H. An odontometric study of arch dimensions among Qatari population sample with different malocclusions. Int J Orthod Rehabili 2018;9:93-100. https://doi.org/10.4103/ijor.ijor_12_18 
  20. Hashim HA, Al-Sayed N, Al-Hussain H. Bolton tooth size ratio among Qatari population sample: an odontometric study. J Orthod Sci 2017;6:22-7. https://doi.org/10.4103/2278-0203.197395 
  21. Alshahrani AA, Alshahrani I, Addas MK, Shaik S, Binhomran FM, AlQahtani J. The tooth size discrepancy among orthodontic patients and normal occlusion individuals from Saudi Arabia: a three-dimensional scan analysis of diagnostic casts. Contemp Clin Dent 2020;11:141-9. https://doi.org/10.4103/ccd.ccd_455_19 
  22. Sravya T, Dumpala RK, Guttikonda VR, Manchikatla PK, Narasimha VC. Mesiodistal odontometrics as a distinguishing trait: a comparative preliminary study. J Forensic Dent Sci 2016;8:99-102. https://pubmed.ncbi.nlm.nih.gov/27555727/  https://doi.org/10.4103/0975-1475.186368
  23. Salam E, Khalifa A, Hassouna D. Odontometric analysis using CBCT for sexual dimorphism in EgyptianFayoum population in case of normal occlusion. Egypt Dent J 2021;67:1319-32. https://www.researchgate.net/publication/350802113_Odontometric_analysis_using_CBCT_for_sexual_dimorphism_in_Egyptian-Fayoum_population_in_case_of_normal_occlusion  https://doi.org/10.21608/edj.2021.66478.1542
  24. Gopakumar M, Hegde AM, Janardhanan L. Gender determination by odontometric parametersa preliminary study. Indian J Contemp Dent 2013;1:1-4. https://www.researchgate.net/publication/271350078_Gender_Determination_by_Odontometric_Parameters-A_Preliminary_Study  https://doi.org/10.5958/j.2320-5962.1.1.001
  25. Sharma S, Sharma S, Athaiya A. Activation functions in neural networks. Int J Eng Appl Sci Technol 2020;4:310-6. https://doi.org/10.33564/IJEAST.2020.v04i12.054 
  26. Obuchowski NA, Bullen JA. Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine. Phys Med Biol 2018;63:07TR01. https://doi.org/10.1088/1361-6560/aab4b1 
  27. Centor RM. Signal detectability: the use of ROC curves and their analyses. Med Decis Making 1991;11:102-6. https://doi.org/10.1177/0272989X9101100205 
  28. Murray PE, Stanley HR, Matthews JB, Sloan AJ, Smith AJ. Age-related odontometric changes of human teeth. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2002;93:474-82. https://doi.org/10.1067/moe.2002.120974 
  29. Khamis MF, Taylor JA, Malik SN, Townsend GC. Odontometric sex variation in Malaysians with application to sex prediction. Forensic Sci Int 2014;234:183. e1-7. https://doi.org/10.1016/j.forsciint.2013.09.019 
  30. Narang RS, Manchanda AS, Singh B. Sex assessment by molar odontometrics in North Indian population. J Forensic Dent Sci 2015;7:54-8. https://pubmed.ncbi.nlm.nih.gov/25709321/  https://doi.org/10.4103/0975-1475.150318
  31. Viciano J, Aleman I, D'Anastasio R, Capasso L, Botella MC. Odontometric sex discrimination in the Herculaneum sample (79 AD, Naples, Italy), with application to juveniles. Am J Phys Anthropol 2011;145:97-106. https://doi.org/10.1002/ajpa.21471 
  32. Grewal DS, Khangura RK, Sircar K, Tyagi KK, Kaur G, David S. Morphometric analysis of odontometric parameters for gender determination. J Clin Diagn Res 2017;11:ZC09-13. https://doi.org/10.7860/JCDR/2017/26680.10341 
  33. Sawyer SF. Analysis of variance: the fundamental concepts. J Man Manip Ther 2009;17:27E-38E. https://doi.org/10.1179/jmt.2009.17.2.27E 
  34. Adserias-Garriga J, Thomas C, Ubelaker DH, C Zapico S. When forensic odontology met biochemistry: multidisciplinary approach in forensic human identification. Arch Oral Biol 2018;87:7-14. https://doi. org/10.1016/j.archoralbio.2017.12.001 
  35. Pretty IA, Sweet D. A look at forensic dentistry--part 1: the role of teeth in the determination of human identity. Br Dent J 2001;190:359-66. https://doi.org/10.1038/sj.bdj.4800972 
  36. Senn DR, Stimson PG. Forensic dentistry. 2nd ed. Boca Raton: CRC Press; 2010. p. 437. https://www.routledge.com/Forensic-Dentistry/Senn-Stimson/p/book/9781420078367 
  37. Senn DR, Weems RA. Manual of forensic odontology. 5th ed. Boca Raton: Taylor & Francis; 2013. p. 445. https://www.routledge.com/Manual-of-ForensicOdontology/Senn-Weems/p/book/9780367778514 
  38. Nagaratnam N, Nagaratnam K, Cheuk G. Oral issues in the elderly. In: Nagaratnam N, Nagaratnam K, Cheuk G, eds. Geriatric diseases. Cham: Springer; 2017. p. 1-8. https://link.springer.com/referenceworkentry/10.1007/978-3-319-32700-6_92-1 
  39. Rai B, Kaur J. Evidence-based forensic dentistry. New York: Springer International Publishing; 2012. https://link.springer.com/book/10.1007/978-3-642-28994-1 
  40. Franklin D. Forensic age estimation in human skeletal remains: current concepts and future directions. Leg Med (Tokyo) 2010;12:1-7. https://doi.org/10.1016/j.legalmed.2009.09.001