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All issues Volume 723 (2026) E3S Web Conf., 723 (2026) 01002 References
Open Access
Issue
E3S Web Conf.
Volume 723, 2026
2026 International Conference on Artificial Intelligence in Energy and Infrastructure (AIEI 2026)
Article Number 01002
Number of page(s) 6
Section Artificial Intelligence, Machine Learning & Data Analytics
DOI https://doi.org/10.1051/e3sconf/202672301002
Published online 08 July 2026
  1. P. Achararit et al., “Impacted lower third molar classification and difficulty index assessment: Comparisons among dental students, general practitioners and deep learning model assistance,” BMC Oral Health, vol. 25, no. 1, Art. no. 152, Jan. 2025. [Google Scholar]
  2. Y. Balel and K. Sağtaş, “Deep learning-based approach to third molar impaction analysis with clinical classifications,” Sci. Rep., vol. 15, no. 1, Art. no. 23688, Jul. 2025. [Google Scholar]
  3. T. Chindanuruks et al., “Development and validation of a deep learning algorithm for the classification of the level of surgical difficulty in impacted mandibular third molar surgery,” Int. J. Oral Maxillofac. Surg., vol. 54, no. 5, pp. 452–460, May 2025. [Google Scholar]
  4. J. H. Yoo et al., “Deep learning based prediction of extraction difficulty for mandibular third molars,” Sci. Rep., vol. 11, Art. no. 1954, 2021. [Google Scholar]
  5. A. Danjo et al., “Limitations of panoramic radiographs in predicting mandibular wisdom tooth extraction and the potential of deep learning models to overcome them,” Sci. Rep., vol. 14, no. 1, Art. no. 30806, Dec. 2024. [Google Scholar]
  6. A. C. Tricco et al., “PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation,” Ann. Intern. Med., vol. 169, no. 7, pp. 467–473, 2018. [Google Scholar]
  7. C. Kang et al., “A data-driven method for surgeon-specific difficulty assessment in third molar extraction,” Front. Med. (Lausanne), vol. 7, Art. no. 1654727, Nov. 2025. [Google Scholar]
  8. F. Khorshidi, R. Esmaeilyfard, and M. Paknahad, “Enhancing predictive analytics in mandibular third molar extraction using artificial intelligence: A CBCT-based study,” Saudi Dent. J., vol. 36, no. 12, pp. 1582–1587, Dec. 2024. [Google Scholar]
  9. W. Li et al., “Transfer learning-based super-resolution in panoramic models for predicting mandibular third molar extraction difficulty: A multi-center study,” Medical Data Mining, 2023. [Google Scholar]
  10. V. Trachoo et al., “Deep learning for predicting the difficulty level of removing the impacted mandibular third molar,” Int. Dent. J., vol. 75, no. 1, pp. 144–150, Feb. 2025. [Google Scholar]
  11. D. Kwon, J. Ahn, C. S. Kim, D. O. Kang, and J. Y. Paeng, “A deep learning model based on concatenation approach to predict the time to extract a mandibular third molar tooth,” BMC Oral Health, vol. 22, no. 1, Art. no. 571, Dec. 2022. [Google Scholar]
  12. S. Akdoğan, M. U. Öziç, and M. Tassoker, “Development of an AI-supported clinical tool for assessing mandibular third molar tooth extraction difficulty using panoramic radiographs and YOLO11 sub-models,” Diagnostics, vol. 15, no. 4, Art. no. 462, 2025. [Google Scholar]
  13. J. Mongan, L. Moy, and C. E. Kahn Jr., “Checklist for artificial intelligence in medical imaging (CLAIM): A guide for authors and reviewers,” Radiology, vol. 295, no. 1, pp. 202–208, 2020. [Google Scholar]
  14. R. F. Wolff et al., “PROBAST: A tool to assess risk of bias and applicability of prediction model studies,” BMJ, vol. 364, Art. no. l1327, 2019. [Google Scholar]
  15. J. Lee, J. Park, S. Y. Moon, and K. Lee, “Automated prediction of extraction difficulty and inferior alveolar nerve injury for mandibular third molar using a deep neural network,” Appl. Sci., vol. 12, no. 1, Art. no. 475, 2022. [Google Scholar]

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