Sex estimation on panoramic dental radiographs: A methodological approach

  • Ana Beatriz Hougaz UFBA
  • David Lima UFBA
  • Bernardo Peters UFBA
  • Patricia Cury UFBA
  • Luciano Oliveira UFBA

Resumo


Estimating sex using tooth radiographs requires knowledge of a comprehensive spectrum of maxillar anatomy, which ultimately demands specialization on the anatomical structures in the oral cavity. In this paper, we propose a more effective methodological study than others present in the literature for the problem of automatic sex estimation. Our methodology uses the largest publicly available data set in the literature, raises statistical significance in the performance assessment, and explains which part of the images influences the classification. Our findings showed that although EfficientNetV2-Large reached an average F1-score of 91,43% +- 0,67, an EfficientNet-B0 could be more beneficial with a very close F1-score and a much lighter architecture.

Referências

Abdul, N. S., Alhazani, L., Alruwail, R., Aldres, S., and Asil, S. (2019). Awareness of forensic odontology among undergraduate, graduate, and postgraduate dental students in riyadh, saudi arabia: A knowledge-, attitude-, and practice-based study. Journal of forensic dental sciences, (1):35–41.

Anderson, D. and Thompson, G. (1973). Interrelationships and sex differences of dental and skeletal measurements. Journal of Dental Research, 52:8–431.

Badran, D. H., Othman, D. A., Thnaibat, H. W., and M., A. W. (2015). Predictive accuracy of mandibular ramus flexure as a morphologic indicator of sex dimorphism in jordanians. International Journal of Morphology, 33(4):1248–1254.

Dayal, P. (1998). Textbook of Forensic Odontology. Hyderabad Paras Medical Publishers, 1 edition.

Govindaraj, S., Jayanandan, M., Vishnu Priya, V., Thirumal, R., and Shamsudeen, S. (2018). Knowledge and attitude among senior dental students on forensic dentistry: A survey. World Journal of Dentistry, 9:91–187.

Ilić, I., Vodanović, M., and Subašić, M. (2019). Gender estimation from panoramic dental x-ray images using deep convolutional networks. IEEE, pages 1–5.

Ke, W., Fan, F., Liao, P., Lai, Y., Wu, Q., Du, W., Chen, H., Deng, Z., and Zhang, Y. (2020). Biological gender estimation from panoramic dental x-ray images based on multiple feature fusion model. Sensing and Imaging (2020), 21:1–11.

Milošević, D., Vodanović, M., Galić, I., and Subašić, M. (2019). Estimating biological gender from panoramic dental x-ray images. International Symposium on Image and Signal Processing and Analysis, ISPA, 2019-September:105–110.

Milošević, D., Vodanović, M., Galić, I., and Subašić, M. (2021). Automated sex assessment of individual adult tooth x-ray images. International Symposium on Image and Signal Processing and Analysis (ISPA), pages 72–77.

Milošević, D., Vodanović, M., Galić, I., and Subašić, M. (2022). A comprehensive exploration of neural networks for forensic analysis of adult single tooth x-ray images. IEEE, pages 70980–71002.

Nagare, S. P., Chaudhari, R. S.and Birangane, R. S., and Parkarwar, P. C. (2018). Sex determination in forensic identification, a review. Journal of forensic dental sciences, https://doi.org/10.4103/jfo.jfds_55_17, 10(2):61–66.

Rajee, M. and Mythili, C. (2021). Gender classification on digital dental x-ray images using deep convolutional neural network. Biomedical Signal Processing and Control, 69:102–139.

Saini, V., Srivastava, R., Rai, R. K., Shamal, S. N., Singh, T. B., and Tripathi, S. K. (2011). Mandibular ramus: an indicator for sex in fragmentary mandible. Journal of forensic sciences, 56:6–13.

Sharma, A., Shokeen, S., Arora, R., and Dhaginakatti, S. (2015). Survey on knowledge, attitude and practice of forensic odontology among private dental practitioners in ghaziabad city, india. Journal of Dental Specialities, 3:7–43.

Sherfudhin, H., Abdullah, M., and Khan, N. (1996). A cross-sectional study of canine dimorphism in establishing sex identity: Comparison of two statistical methods. Journal of Oral Rehabilitation, 23:31–627.

Shivani, B., Arshroop, K., Karanprakash, S., Mahjeet, S., Navgeet, P., and Chitra, A. (2017). Perception of forensic odontology and its practice among local dentists of an institution. Journal of Forensic Research, 8:1–4.

Tan, M. and Le, Q. V. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. International Conference on Machine Learning, pages 6105–6114.

Tan, M. and Le, Q. V. (2021). Efficientnetv2: Smaller models and faster training. International Conference on Machine Learning, pages 10096–10106.

Williams, B. and Rogers, T. (2006). Evaluatiing the accuracy and precision of cranial morphological traits for sex determination. Journal of forensic sciences, 4:35–729.
Publicado
27/06/2023
HOUGAZ, Ana Beatriz; LIMA, David; PETERS, Bernardo; CURY, Patricia; OLIVEIRA, Luciano. Sex estimation on panoramic dental radiographs: A methodological approach. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 115-125. ISSN 2763-8952. DOI: https://doi.org/10.5753/sbcas.2023.229563.

##plugins.generic.recommendByAuthor.heading##