Research Article
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Year 2022, Volume 2, Issue 2, 51 - 58, 01.10.2022

Abstract

References

  • Çetli, E., & Özkoçak, V. (2018). Use of recorded personal data in forensic sciences. Avrasya Sanat ve Medeniyet Dergisi, 10, 1-12.
  • Akay, G., Atak, N., & Güngör, K. (2018). Adli dişhekimliğinde dişler kullanılarak yapılan yaş tayini yöntemleri. Ege Üniversitesi Diş Hekimliği Fakültesi Dergisi, 39(2), 73-82.
  • Demirel, T., & Bodrumlu, E. H. Sürme Anomalileri. Uluslararası Diş Hekimliği Bilimleri Dergisi, (3), 141-146.
  • Massler, M., Schour, I., & Poncher, H. G. (1941). Developmental pattern of the child as reflected in the calcification pattern of the teeth. American Journal of Diseases of Children, 62(1), 33-67.
  • Panchbhai, A. S. (2011). Dental radiographic indicators, a key to age estimation. Dentomaxillofacial Radiology, 40(4), 199-212.
  • Demirjian, A., Goldstein, H., & Tanner, J. M. (1973). A new system of dental age assessment. Human biology, 211-227.
  • Wallraff, S., Vesal, S., Syben, C., Lutz, R., & Maier, A. (2021). Age estimation on panoramic dental X-ray images using deep learning. In Bildverarbeitung für die Medizin 2021 (pp. 186-191). Springer Vieweg, Wiesbaden.
  • Štern, D., Kainz, P., Payer, C., & Urschler, M. (2017). Multi-factorial age estimation from skeletal and dental MRI volumes. In International workshop on machine learning in medical imaging (pp. 61-69). Springer, Cham.
  • Kahaki, S. M., Nordin, M., Ahmad, N. S., Arzoky, M., & Ismail, W. (2020). Deep convolutional neural network designed for age assessment based on orthopantomography data. Neural Computing and Applications, 32(13), 9357-9368.
  • Matsuda, S., Miyamoto, T., Yoshimura, H., & Hasegawa, T. (2020). Personal identification with orthopantomography using simple convolutional neural networks: a preliminary study. Scientific reports, 10(1), 1-7.
  • Farhadian, M., Salemi, F., Saati, S., & Nafisi, N. (2019). Dental age estimation using the pulp-to-tooth ratio in canines by neural networks. Imaging science in dentistry, 49(1), 19-26.
  • Banjšak, L., Milošević, D., & Subašić, M. (2020). Implementation of artificial intelligence in chronological age estimation from orthopantomographic X-ray images of archaeological skull remains. Bulletin of the International Association for Paleodontology, 14(2), 122-129.
  • Milošević, D., Vodanović, M., Galić, I., & Subašić, M. (2022). Automated estimation of chronological age from panoramic dental X-ray images using deep learning. Expert Systems with Applications, 189, 116038.
  • Zaborowicz, M., Zaborowicz, K., Biedziak, B., & Garbowski, T. (2022). 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, 22(2), 637.
  • Lee, Y. H., Won, J. H., Auh, Q., & Noh, Y. K. (2022). Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms. Scientific Reports, 12(1), 1-14.
  • Özkan, İ. N. İ. K., & Ülker, E. (2017). Derin öğrenme ve görüntü analizinde kullanılan derin öğrenme modelleri. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3), 85-104.

Age Detection by Deep Learning from Dental Panoramic Radiographs

Year 2022, Volume 2, Issue 2, 51 - 58, 01.10.2022

Abstract

The use of deep learning approaches is growing day by day in the solution of various real-world problems in engineering science. Health sciences problems are also one of the areas in that deep learning is frequently applied. Especially in digital forensics cases and anthropology, when determining the identification of living individuals or corpses, the most important specification is to state the age of the person. At the stage of determining the age, the analysis of bone or tooth development of people is two of the most trustworthy methods. Moreover, there is a distinctive interrelation between the eruption of permanent and primary teeth and the chronological age of the individual. In this study, a deep learning approach is suggested as an alternative to age determination using traditional methods. A total of 627 dental orthopantomographic images gathered from individuals between the ages of 2 and 21 were employed in this study. The data set consists of two different classes, individuals under the age of 13 and individuals aged 13 and over, who have completed the eruption of their permanent number 7 teeth. Firstly, feature extraction was operated on the data by using the Convolutional Neural Network (CNN) architecture, which is one of the deep learning approaches. Afterward the feature extraction phase, the system was completed using four different classifiers. 70% of the dataset is allocated for training while in the rest is reserved for testing. The results achieved using various evaluation metrics are presented in detail with a complexity matrix, tables, and graphs. In this study, 84% accuracy, 85% F-score, and 76% sensitivity values were reached using the Alexnet architecture and k-nearest neighbor (k-NN) algorithm. It is forecasted that the proposed system will ensure age determination in less time and abate the cost compared to traditional age determination methods. Besides, the study will both support dentists in the clinical environment and can be used in education.

References

  • Çetli, E., & Özkoçak, V. (2018). Use of recorded personal data in forensic sciences. Avrasya Sanat ve Medeniyet Dergisi, 10, 1-12.
  • Akay, G., Atak, N., & Güngör, K. (2018). Adli dişhekimliğinde dişler kullanılarak yapılan yaş tayini yöntemleri. Ege Üniversitesi Diş Hekimliği Fakültesi Dergisi, 39(2), 73-82.
  • Demirel, T., & Bodrumlu, E. H. Sürme Anomalileri. Uluslararası Diş Hekimliği Bilimleri Dergisi, (3), 141-146.
  • Massler, M., Schour, I., & Poncher, H. G. (1941). Developmental pattern of the child as reflected in the calcification pattern of the teeth. American Journal of Diseases of Children, 62(1), 33-67.
  • Panchbhai, A. S. (2011). Dental radiographic indicators, a key to age estimation. Dentomaxillofacial Radiology, 40(4), 199-212.
  • Demirjian, A., Goldstein, H., & Tanner, J. M. (1973). A new system of dental age assessment. Human biology, 211-227.
  • Wallraff, S., Vesal, S., Syben, C., Lutz, R., & Maier, A. (2021). Age estimation on panoramic dental X-ray images using deep learning. In Bildverarbeitung für die Medizin 2021 (pp. 186-191). Springer Vieweg, Wiesbaden.
  • Štern, D., Kainz, P., Payer, C., & Urschler, M. (2017). Multi-factorial age estimation from skeletal and dental MRI volumes. In International workshop on machine learning in medical imaging (pp. 61-69). Springer, Cham.
  • Kahaki, S. M., Nordin, M., Ahmad, N. S., Arzoky, M., & Ismail, W. (2020). Deep convolutional neural network designed for age assessment based on orthopantomography data. Neural Computing and Applications, 32(13), 9357-9368.
  • Matsuda, S., Miyamoto, T., Yoshimura, H., & Hasegawa, T. (2020). Personal identification with orthopantomography using simple convolutional neural networks: a preliminary study. Scientific reports, 10(1), 1-7.
  • Farhadian, M., Salemi, F., Saati, S., & Nafisi, N. (2019). Dental age estimation using the pulp-to-tooth ratio in canines by neural networks. Imaging science in dentistry, 49(1), 19-26.
  • Banjšak, L., Milošević, D., & Subašić, M. (2020). Implementation of artificial intelligence in chronological age estimation from orthopantomographic X-ray images of archaeological skull remains. Bulletin of the International Association for Paleodontology, 14(2), 122-129.
  • Milošević, D., Vodanović, M., Galić, I., & Subašić, M. (2022). Automated estimation of chronological age from panoramic dental X-ray images using deep learning. Expert Systems with Applications, 189, 116038.
  • Zaborowicz, M., Zaborowicz, K., Biedziak, B., & Garbowski, T. (2022). 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, 22(2), 637.
  • Lee, Y. H., Won, J. H., Auh, Q., & Noh, Y. K. (2022). Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms. Scientific Reports, 12(1), 1-14.
  • Özkan, İ. N. İ. K., & Ülker, E. (2017). Derin öğrenme ve görüntü analizinde kullanılan derin öğrenme modelleri. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3), 85-104.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Merve PARLAK BAYDOĞAN> (Primary Author)
FIRAT UNIVERSITY
0000-0002-2114-0139
Türkiye


Sümeyye COŞGUN BAYBARS>
FIRAT ÜNİVERSİTESİ
0000-0002-4166-3754
Türkiye


Seda ARSLAN TUNCER>
FIRAT ÜNİVERSİTESİ
0000-0001-6472-8306
Türkiye

Publication Date October 1, 2022
Published in Issue Year 2022, Volume 2, Issue 2

Cite

APA Parlak Baydoğan, M. , Coşgun Baybars, S. & Arslan Tuncer, S. (2022). Age Detection by Deep Learning from Dental Panoramic Radiographs . Artificial Intelligence Theory and Applications , 2 (2) , 51-58 . Retrieved from https://dergipark.org.tr/en/pub/aita/issue/72862/1179521