Research Article
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Year 2022, Volume: 8 Issue: 2, 22 - 30, 30.12.2022
https://doi.org/10.22531/muglajsci.1108397

Abstract

Project Number

2210034

References

  • Staudt CB, Kiliaridis S. “Different skeletal types underlying Class-III malocclusion in a random population.” Am J Orthod Dentofacial Orthop, 136(5), 715-721, 2009.
  • Oltramari-Navarro PV, de Almeida RR, Conti AC, Navarro Rde L, de Almeida MR, Fernandes LS. “Early treatment protocol for skeletal Class-III malocclusion.” Braz Dent J. ,24(2), 167-173, 2013.
  • Al-Khalifa, Hussein. (2014). “Orthopedic Correction of Class-III Malocclusions during Mixed Dentition.” Open Journal of Stomatology. 04(07), 372-380,2014
  • Mandall N, Cousley R, DiBiase A, Dyer F, Littlewood S, Mattick R, Nute SJ, Doherty B, Stivaros N, McDowall R, Shargill I, Worthington HV. “Early Class-III protraction facemask treatment reduces the need for orthognathic surgery: a multi-centre, two-arm parallel randomized, controlled trial.” J Orthod., 43(3), 164-175, 2016.
  • Sharma JN. “Epidemiology of malocclusions and assessment of orthodontic treatment need for the population of eastern Nepal.” World J Orthod., 10(4), 311- 316, 2009.
  • X. Xu et al., "Advances in Smartphone-Based Point-of-Care Diagnostics," in Proceedings of the IEEE, vol. 103, no. 2, pp. 236-247, Feb. 2015, doi: 10.1109/JPROC.2014.2378776.
  • Digital around the world - datareportal – global digital insights. DataReportal. (n.d.). Retrieved July 25, 2022, from https://datareportal.com/global-digital-overview
  • Mobile Health Industry Trends and forecast 2021. Artezio. (n.d.). Retrieved July 24, 2022, from https://www.artezio.com/pressroom/blog/mobile-industry-forecast/
  • Gupta G, Vaid NR. “The World of Orthodontic apps.” APOS Trends Orthod, 7(2), 73, 2017.
  • Development, C. S. (n.d.). Dental4Windows. Download.com. Retrieved July 24, 2022, from https://download.cnet.com/Dental4Windows/3000-2129_4-76472046.html
  • Baheti, M.J., Toshniwal, N. “Orthodontic apps at fingertips.”, Progress in Orthodontic, 15(1), 36, 2014.
  • Phimentum. (n.d.). Retrieved July 23, 2022, from https://www.phimentum.com/
  • Demircan, G.S., Kılıç, B., Önal-Süzek, T. (2021). “Early Diagnosis and Prediction of Skeletal Class-III Malocclusion from Profile Photos Using Artificial Intelligence.” In: Jarm, T., Cvetkoska, A., Mahnič-Kalamiza, S., Miklavcic, D. (eds) 8th European Medical and Biological Engineering Conference. EMBEC 2020. IFMBE Proceedings, 80, 434-448, 2020.
  • Basciftci,F.A.,Uysal,T.,Buyukerkmen,A.“Determinati on of Holdaway soft tissue norms in Anatolian Turkish adults” Am J Orthod Dentofacial Orthop, 123(4),395-400, 2003.
  • 1adrianb. (n.d.). 1adrianb/face-alignment: 2D and 3D face alignment library build using pytorch. GitHub. Retrieved July 24, 2022, from https://github.com/1adrianb/face-alignment.

COMPARATIVE ANALYSIS OF THREE MACHINE LEARNING MODELS FOR EARLY PREDICTION OF SKELETAL CLASS-III MALOCCLUSION FROM PROFILE PHOTOS

Year 2022, Volume: 8 Issue: 2, 22 - 30, 30.12.2022
https://doi.org/10.22531/muglajsci.1108397

Abstract

The pre-adolescent growth period is the best time for the skeletal Class-III malocclusion treatment. Diagnosis and treatment during this period continue to be a complex orthodontic problem. Class-III malocclusion is complicated to treat with braces frequently requiring surgical intervention after a pubertal growth spurt. In addition, delayed recognition of the problem will yield significant functional, aesthetic, and psychological concerns. This study presents the first fully automated machine learning method to accurately diagnose Class-III malocclusion applied across mobile images, to the best of our knowledge. For this purpose, we comparatively evaluated three machine learning approaches: a deep learning algorithm, a machine learning algorithm, and a rule-based algorithm. We collected a novel profile image data set for this analysis along with their formal diagnosis from 435 orthodontics patients. The most successful method among the three was the machine learning method, with an accuracy of %76.

Supporting Institution

TÜBİTAK 1512 BİGG

Project Number

2210034

Thanks

We want to thank Gül Sude Demircan, who developed the previous prototype, and Tülay Sevinç, who assisted in collecting the patient images and the consent forms.

References

  • Staudt CB, Kiliaridis S. “Different skeletal types underlying Class-III malocclusion in a random population.” Am J Orthod Dentofacial Orthop, 136(5), 715-721, 2009.
  • Oltramari-Navarro PV, de Almeida RR, Conti AC, Navarro Rde L, de Almeida MR, Fernandes LS. “Early treatment protocol for skeletal Class-III malocclusion.” Braz Dent J. ,24(2), 167-173, 2013.
  • Al-Khalifa, Hussein. (2014). “Orthopedic Correction of Class-III Malocclusions during Mixed Dentition.” Open Journal of Stomatology. 04(07), 372-380,2014
  • Mandall N, Cousley R, DiBiase A, Dyer F, Littlewood S, Mattick R, Nute SJ, Doherty B, Stivaros N, McDowall R, Shargill I, Worthington HV. “Early Class-III protraction facemask treatment reduces the need for orthognathic surgery: a multi-centre, two-arm parallel randomized, controlled trial.” J Orthod., 43(3), 164-175, 2016.
  • Sharma JN. “Epidemiology of malocclusions and assessment of orthodontic treatment need for the population of eastern Nepal.” World J Orthod., 10(4), 311- 316, 2009.
  • X. Xu et al., "Advances in Smartphone-Based Point-of-Care Diagnostics," in Proceedings of the IEEE, vol. 103, no. 2, pp. 236-247, Feb. 2015, doi: 10.1109/JPROC.2014.2378776.
  • Digital around the world - datareportal – global digital insights. DataReportal. (n.d.). Retrieved July 25, 2022, from https://datareportal.com/global-digital-overview
  • Mobile Health Industry Trends and forecast 2021. Artezio. (n.d.). Retrieved July 24, 2022, from https://www.artezio.com/pressroom/blog/mobile-industry-forecast/
  • Gupta G, Vaid NR. “The World of Orthodontic apps.” APOS Trends Orthod, 7(2), 73, 2017.
  • Development, C. S. (n.d.). Dental4Windows. Download.com. Retrieved July 24, 2022, from https://download.cnet.com/Dental4Windows/3000-2129_4-76472046.html
  • Baheti, M.J., Toshniwal, N. “Orthodontic apps at fingertips.”, Progress in Orthodontic, 15(1), 36, 2014.
  • Phimentum. (n.d.). Retrieved July 23, 2022, from https://www.phimentum.com/
  • Demircan, G.S., Kılıç, B., Önal-Süzek, T. (2021). “Early Diagnosis and Prediction of Skeletal Class-III Malocclusion from Profile Photos Using Artificial Intelligence.” In: Jarm, T., Cvetkoska, A., Mahnič-Kalamiza, S., Miklavcic, D. (eds) 8th European Medical and Biological Engineering Conference. EMBEC 2020. IFMBE Proceedings, 80, 434-448, 2020.
  • Basciftci,F.A.,Uysal,T.,Buyukerkmen,A.“Determinati on of Holdaway soft tissue norms in Anatolian Turkish adults” Am J Orthod Dentofacial Orthop, 123(4),395-400, 2003.
  • 1adrianb. (n.d.). 1adrianb/face-alignment: 2D and 3D face alignment library build using pytorch. GitHub. Retrieved July 24, 2022, from https://github.com/1adrianb/face-alignment.
There are 15 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Journals
Authors

Selahattin Aksoy 0000-0002-1825-0097

Banu Kılıç 0000-0002-9207-4490

Tuğba Süzek 0000-0002-3243-1759

Project Number 2210034
Early Pub Date November 2, 2022
Publication Date December 30, 2022
Published in Issue Year 2022 Volume: 8 Issue: 2

Cite

APA Aksoy, S., Kılıç, B., & Süzek, T. (2022). COMPARATIVE ANALYSIS OF THREE MACHINE LEARNING MODELS FOR EARLY PREDICTION OF SKELETAL CLASS-III MALOCCLUSION FROM PROFILE PHOTOS. Mugla Journal of Science and Technology, 8(2), 22-30. https://doi.org/10.22531/muglajsci.1108397
AMA Aksoy S, Kılıç B, Süzek T. COMPARATIVE ANALYSIS OF THREE MACHINE LEARNING MODELS FOR EARLY PREDICTION OF SKELETAL CLASS-III MALOCCLUSION FROM PROFILE PHOTOS. Mugla Journal of Science and Technology. December 2022;8(2):22-30. doi:10.22531/muglajsci.1108397
Chicago Aksoy, Selahattin, Banu Kılıç, and Tuğba Süzek. “COMPARATIVE ANALYSIS OF THREE MACHINE LEARNING MODELS FOR EARLY PREDICTION OF SKELETAL CLASS-III MALOCCLUSION FROM PROFILE PHOTOS”. Mugla Journal of Science and Technology 8, no. 2 (December 2022): 22-30. https://doi.org/10.22531/muglajsci.1108397.
EndNote Aksoy S, Kılıç B, Süzek T (December 1, 2022) COMPARATIVE ANALYSIS OF THREE MACHINE LEARNING MODELS FOR EARLY PREDICTION OF SKELETAL CLASS-III MALOCCLUSION FROM PROFILE PHOTOS. Mugla Journal of Science and Technology 8 2 22–30.
IEEE S. Aksoy, B. Kılıç, and T. Süzek, “COMPARATIVE ANALYSIS OF THREE MACHINE LEARNING MODELS FOR EARLY PREDICTION OF SKELETAL CLASS-III MALOCCLUSION FROM PROFILE PHOTOS”, Mugla Journal of Science and Technology, vol. 8, no. 2, pp. 22–30, 2022, doi: 10.22531/muglajsci.1108397.
ISNAD Aksoy, Selahattin et al. “COMPARATIVE ANALYSIS OF THREE MACHINE LEARNING MODELS FOR EARLY PREDICTION OF SKELETAL CLASS-III MALOCCLUSION FROM PROFILE PHOTOS”. Mugla Journal of Science and Technology 8/2 (December 2022), 22-30. https://doi.org/10.22531/muglajsci.1108397.
JAMA Aksoy S, Kılıç B, Süzek T. COMPARATIVE ANALYSIS OF THREE MACHINE LEARNING MODELS FOR EARLY PREDICTION OF SKELETAL CLASS-III MALOCCLUSION FROM PROFILE PHOTOS. Mugla Journal of Science and Technology. 2022;8:22–30.
MLA Aksoy, Selahattin et al. “COMPARATIVE ANALYSIS OF THREE MACHINE LEARNING MODELS FOR EARLY PREDICTION OF SKELETAL CLASS-III MALOCCLUSION FROM PROFILE PHOTOS”. Mugla Journal of Science and Technology, vol. 8, no. 2, 2022, pp. 22-30, doi:10.22531/muglajsci.1108397.
Vancouver Aksoy S, Kılıç B, Süzek T. COMPARATIVE ANALYSIS OF THREE MACHINE LEARNING MODELS FOR EARLY PREDICTION OF SKELETAL CLASS-III MALOCCLUSION FROM PROFILE PHOTOS. Mugla Journal of Science and Technology. 2022;8(2):22-30.

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