Araştırma Makalesi

Machine Learning-Based Tooth Color Assessment Using Color Moments to Prevent Metamerism in Different Clinical Lights

Cilt: 15 Sayı: 1 1 Mart 2025
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Machine Learning-Based Tooth Color Assessment Using Color Moments to Prevent Metamerism in Different Clinical Lights

Öz

Choosing the right shade in prosthodontic treatment is of great importance in terms of achieving a natural aesthetic appearance and increasing the patient's satisfaction with the treatment. However, this process is affected by many technical and environmental factors. In particular, variable light sources in clinical and laboratory environments cause the problem of metamerism, which leads to misleading results in color perception. This study proposes a method that reduces the effect of metamerism by detecting color under different light conditions, eliminates the subjectivity of traditional color matching methods and offers an alternative to costly measurement devices. The 29 color samples from the Vita 3D Master shade guide were imaged five times each in four different clinical light conditions. Feature extraction was performed using color moments in RGB, LAB and HSV color spaces. Experimental studies were carried out with different machine learning algorithms on the datasets created with these data. As a result, 100% accuracy was obtained for the classification of four clinical light conditions, 85% for the light-independent classification of 29 Vita colors, 100% under white light, 97% under natural light, 92% under flash light and 94% under yellow light. These findings demonstrated that the limitations of traditional or costly color selection processes can be overcome and metamerism can be reduced by machine learning techniques.

Anahtar Kelimeler

Proje Numarası

Bu çalışma, 123E597 proje kodu kapsamında Türkiye Bilimsel ve Teknik Araştırma Kurumu (TÜBİTAK) tarafından desteklenmektedir.

Kaynakça

  1. Abraham, G., Kurian, N., Wadhwa, S., & Varghese, K. G. (2023). A smartphone application with a gray card for clinical shade selection: A technique. The Journal of Prosthetic Dentistry. https://doi.org/10.1016/j.prosdent.2023.01.016
  2. Basavanna, R. S., Gohil, C., & Shivanna, V. (2013). Shade selection. International Journal of Oral Health Sciences, 3(1), 26-31. https://doi.org/10.4103/2231-6027.122097
  3. Bernauer, S. A., Zitzmann, N. U., & Joda, T. (2021). The use and performance of artificial intelligence in prosthodontics: A systematic review. Sensors, 21(19), 6628. https://doi.org/10.3390/s21196628
  4. Borse, S., & Chaware, S. H. (2020). Tooth shade analysis and selection in prosthodontics: A systematic review and meta-analysis. The Journal of Indian Prosthodontic Society, 20(2), 131-140. https://doi.org/10.4103/jips.jips_399_19
  5. Fayed, A. E. M., Mohamed, H. A., & Othman, H. I. (2022). A Comparison between visual shade matching and digital shade analysis system using K-NN algorithm. Al-Azhar Journal of Dental Science, 25(2), 133-141. https://doi.org/10.21608/ajdsm.2021.85035.1211
  6. Grischke, J., Johannsmeier, L., Eich, L., Griga, L., & Haddadin, S. (2020). Dentronics: Towards robotics and artificial intelligence in dentistry. Dental Materials, 36(6), 765-778. https://doi.org/10.1016/j.dental.2020.03.021
  7. Hu, J. C., Wang, C. H., & Kuhns, D. (2016). New Algorithm in Shade Matching. Journal of Cosmetic Dentistry, 32(1).
  8. Jarad, F. D., Russell, M. D., & Moss, B. W. (2005). The use of digital imaging for colour matching and communication in restorative dentistry. British Dental Journal, 199(1), 43-49. https://doi.org/10.1038/sj.bdj.4812559

Ayrıntılar

Birincil Dil

İngilizce

Konular

Bilgisayar Yazılımı

Bölüm

Araştırma Makalesi

Erken Görünüm Tarihi

20 Şubat 2025

Yayımlanma Tarihi

1 Mart 2025

Gönderilme Tarihi

2 Aralık 2024

Kabul Tarihi

9 Ocak 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 15 Sayı: 1

Kaynak Göster

APA
Efitli, E., Karcıoğlu, A. A., Şimşek, E., Özdoğan, A., Karataş, F., & Şenocak, T. (2025). Machine Learning-Based Tooth Color Assessment Using Color Moments to Prevent Metamerism in Different Clinical Lights. Journal of the Institute of Science and Technology, 15(1), 71-82. https://doi.org/10.21597/jist.1594829
AMA
1.Efitli E, Karcıoğlu AA, Şimşek E, Özdoğan A, Karataş F, Şenocak T. Machine Learning-Based Tooth Color Assessment Using Color Moments to Prevent Metamerism in Different Clinical Lights. Iğdır Üniv. Fen Bil Enst. Der. 2025;15(1):71-82. doi:10.21597/jist.1594829
Chicago
Efitli, Esra, Abdullah Ammar Karcıoğlu, Emrah Şimşek, Alper Özdoğan, Furkan Karataş, ve Tuba Şenocak. 2025. “Machine Learning-Based Tooth Color Assessment Using Color Moments to Prevent Metamerism in Different Clinical Lights”. Journal of the Institute of Science and Technology 15 (1): 71-82. https://doi.org/10.21597/jist.1594829.
EndNote
Efitli E, Karcıoğlu AA, Şimşek E, Özdoğan A, Karataş F, Şenocak T (01 Mart 2025) Machine Learning-Based Tooth Color Assessment Using Color Moments to Prevent Metamerism in Different Clinical Lights. Journal of the Institute of Science and Technology 15 1 71–82.
IEEE
[1]E. Efitli, A. A. Karcıoğlu, E. Şimşek, A. Özdoğan, F. Karataş, ve T. Şenocak, “Machine Learning-Based Tooth Color Assessment Using Color Moments to Prevent Metamerism in Different Clinical Lights”, Iğdır Üniv. Fen Bil Enst. Der., c. 15, sy 1, ss. 71–82, Mar. 2025, doi: 10.21597/jist.1594829.
ISNAD
Efitli, Esra - Karcıoğlu, Abdullah Ammar - Şimşek, Emrah - Özdoğan, Alper - Karataş, Furkan - Şenocak, Tuba. “Machine Learning-Based Tooth Color Assessment Using Color Moments to Prevent Metamerism in Different Clinical Lights”. Journal of the Institute of Science and Technology 15/1 (01 Mart 2025): 71-82. https://doi.org/10.21597/jist.1594829.
JAMA
1.Efitli E, Karcıoğlu AA, Şimşek E, Özdoğan A, Karataş F, Şenocak T. Machine Learning-Based Tooth Color Assessment Using Color Moments to Prevent Metamerism in Different Clinical Lights. Iğdır Üniv. Fen Bil Enst. Der. 2025;15:71–82.
MLA
Efitli, Esra, vd. “Machine Learning-Based Tooth Color Assessment Using Color Moments to Prevent Metamerism in Different Clinical Lights”. Journal of the Institute of Science and Technology, c. 15, sy 1, Mart 2025, ss. 71-82, doi:10.21597/jist.1594829.
Vancouver
1.Esra Efitli, Abdullah Ammar Karcıoğlu, Emrah Şimşek, Alper Özdoğan, Furkan Karataş, Tuba Şenocak. Machine Learning-Based Tooth Color Assessment Using Color Moments to Prevent Metamerism in Different Clinical Lights. Iğdır Üniv. Fen Bil Enst. Der. 01 Mart 2025;15(1):71-82. doi:10.21597/jist.1594829

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