Gender Estimation with Parameters Obtained From the Upper Dental Arcade by Using Machine Learning Algorithms and Artificial Neural Networks

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Upper dental arcade, cone-beamed computed tomography, estimation of gender, machine learning algorithms, artificial neural networks


Objective: The aim of this study is to estimate gender with parameters obtained from the upper dental arcade by using machine learning algorithms and artificial neural networks.

Methods: The study was conducted on cone-beamed computed tomography images of 176 individuals between the ages of 18 and 55 who did not have any pathologies or surgical interventions in their upper dental arcade. The images obtained were transferred to RadiAnt DICOM Viewer program in Digital Imaging and Communications in Medicine format and all images were brought to orthogonal plane by applying 3D Curved Multiplanar Reconstruction. Length and curvature length measurements were performed on these images brought to orthogonal plane. The data obtained were used in machine learning algorithms (ML) and artificial neural networks input and rates of gender estimation were shown.

Results: In the study, an accuracy ratio of 0.86 was found with ML models linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) algorithm and an accuracy ratio of 0.86 was found with random forest (RF) algorithm. It was found with SHAP analyser of RF algorithm that the parameter of width at the level of 3rd molar teeth contributed the most to gender. An accuracy rate of 0.92 was found as a result of training for 500 times with multilayer perceptron classifier (MLCP), which is an artificial neural network (ANN) model.

Conclusion: As a result of our study, it was found that the parameters obtained from the upper dental arcade showed a high accuracy in estimation of gender. In this context, we believe that the present study will make important contributions to forensic sciences.


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Intasuwan P, Taranop V, Mahakkanukrauh P. (2022) A Comparative Study of Visual Assessment Between Dry Bone, 2-Dimensional Photograph, and Deep Learning Methods in Sex Classification on the Auricular Area of the Os Coxae in a Thai Population. Int J Morphol. 40(1).

Glenn JK, Goldman J. (1976) Task delegation to physician extenders--some comparisons. Am J Public Health. 66(1):64-6.

Kaplan A, Alaittin E (2020) Anatomi 1. Cilt, 7. Baskı. Güneş Medical Publising, Ankara

Skrzat J, Holiat D, Walocha J. 2003 A morphometrical study of the human palatine sutures. Folia Morphol. 62(2):123-7.

Alanazi AA, Almutair AM, Alhubayshi A, Almalki A, Naqvi ZA, Alassaf A, et al. (2022) Morphometric Analysis of Permanent Canines: Preliminary Findings on Odontometric Sex Dimorphism. Int. J. Environ. Res. Public Health. 19(4):2109.

Hu KS, Koh KS, Han SH, Shin KJ, Kim HJ. (2006) Sex determination using nonmetric characteristics of the mandible in Koreans. JFS. 51(6):1376-82.

Sherfudhin H, Abdullah M, Khan N. (1996) A cross‐sectional study of canine dimorphism in establishing sex identity: comparison of two statistical methods. J Oral Rehabil. 23(9):627-31.

Aljayousi M, Al-Khateeb S, Badran S, Alhaija E. (2021) Maxillary and mandibular dental arch forms in a Jordanian population with normal occlusion. BMC Oral Health. 21(1):1-8.

Linder-Aronson S. (1970) Adenoids. Their effect on mode of breathing and nasal airflow and their relationship to characteristics of the facial skeleton and the denition. A biometric, rhino-manometric and cephalometro-radiographic study on children with and without adenoids. Acta Otolaryngol Suppl. 265:1-132.

Omar H, Alhajrasi M, Felemban N, Hassan A. (2018) Dental arch dimensions, form and tooth size ratio among a Saudi sample. Saudi Med J. 39(1):86.

Hasanreisoglu U, Berksun S, Aras K, Arslan I. (2005) An analysis of maxillary anterior teeth: facial and dental proportions. J Prosthet Dent. 94(6):530-8.

Mankapure PK, Barpande SR, Bhavthankar JD. (2017) Evaluation of sexual dimorphism in arch depth and palatal depth in 500 young adults of Marathwada region, India. J Forensic Dent Sci. 9(3):153.

Woo JK. (1949) Direction and type of the transverse palatine suture and its relation to the form of the hard palate. AJPA. 7(3):385-400.

Krogman W. (1955) The human skeleton in forensic medicine. I. Postgrad. Med. 17(2):A-48; passim.

Howells WW. (1976) Physical variation and history in Melanesia and Australia. Am J Phys Anthropol. 45(3):641-9.

Oner Z, Turan MK, Oner S, Secgin Y, Sahin B. (2019) Sex estimation using sternum part lenghts by means of artificial neural networks. FSI. 301:6-11.

Toy S, Secgin Y, Oner Z, Turan MK, Oner S, Senol D. (2022) A study on sex estimation by using machine learning algorithms with parameters obtained from computerized tomography images of the cranium. Sci Rep. 12(1):4278.

Turan MK, Oner Z, Secgin Y, Oner S. (2019) A trial on artificial neural networks in predicting sex through bone length measurements on the first and fifth phalanges and metatarsals. Comput Biol Med. 115:103490.

Secgin Y, Oner Z, Turan MK, Oner S. (2022) Gender prediction with the parameters obtained from pelvis computed tomography images and machine learning algorithms. JASI. 71(3):204.

Secgin Y, Oner Z, Turan MK, Oner S. (2021) Gender prediction with parameters obtained from pelvis computed tomography images and decision tree algorithm. Int. J. Med. Sci. 10(2):356-61.

Santosh K, Pradeep N, Goel V, Ranjan R, Pandey E, Shukla PK, et al. (2022) Machine learning techniques for human age and gender identification based on teeth X-ray images. J. Healthc. Eng. 2022.

Bishara SE, Jakobsen JR, Treder J, Nowak A. (1998) Arch length changes from 6 weeks to 45 years. The Angle Orthodontist. 68(1):69-74.<0069:ALCFWT>2.3.CO;2

Alam MK, Shahid F, Purmal K, Ahmad B, Khamis MF. (2014) Tooth size and dental arch dimension measurement through cone beam computed tomography: effect of age and gender. Res J Recent Sci ISSN. 2277:2502.

Hashim HA, Al-Ghamdi S. (2005) Tooth width and arch dimensions in normal and malocclusion samples: an odontometric study. J Contemp Dent Pract. 6(2):36-51.

Burris BG, Harris EF. (2000) Maxillary arch size and shape in American blacks and whites. The Angle Orthod. 70(4):297-302.<0297:MASASI>2.0.CO;2

Alvaran N, Roldan SI, Buschang PH. (2009) Maxillary and mandibular arch widths of Colombians. AJODO. 135(5):649-56.

G Venkat Rao GK. (2016) Sex Determination by means of Inter-Canine and Inter-Molar Width-A Study in Telangana population. Asian Pac J Health Sci. 3(4):171-5.

Al-Omari IK, Duaibis RB, Al-Bitar ZB. (2007) Application of Pont’s Index to a Jordanian population. Eur. J. Orthod. 29(6):627-31.

Azeem M, ul Haq A, Qadir S. (2018) Maxillary Inter Canine Widths: Comparison Analysis In Various Populations. TPMJ. 25(02):246-51.

Azlan A, Mardiati E, Evangelina IA (2019) A gender-based comparison of intermolar width conducted at Padjajaran University Dental Hospital, Bandung, Indonesia. Dent J (majalah Kedokteran Gigi). 52(4):168-71.

Dasgupta M, Roy BK, Bora GRH, Bharali T. (2021) Relationship between dental arch width and vertical facial morphology in multiethnic assamese adults. IJOHR. 7(1):26.

Tircoveluri S, Singh JR, Rayapudi N, Karra A, Begum M, Challa P. (2013) Correlation of masseter muscle thickness and intermolar width-an ultrasonography study. JIOH. 5(2):28.




How to Cite

Erkartal, H. Şaban, Tatlı, M., Secgin, Y., Toy, S., & Duman, B. S. (2023). Gender Estimation with Parameters Obtained From the Upper Dental Arcade by Using Machine Learning Algorithms and Artificial Neural Networks. European Journal of Therapeutics, 352–358.



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