Vertucci, F. J. Root canal morphology and its relationship to endodontic procedures. Endod. Top. 10, 3–29 (2005).
Torabinejad, M., Kutsenko, D., Machnick, T. K., Ismail, A. & Newton, C. W. Levels of evidence for the outcome of nonsurgical endodontic treatment. J. Endod. 31, 637–646 (2005).
Ball, R. L., Barbizam, J. V. & Cohenca, N. Intraoperative endodontic applications of cone-beam computed tomography. J. Endod. 39, 548–557 (2013).
Schwendicke, F. A., Samek, W. & Krois, J. Artificial intelligence in dentistry: Chances and challenges. J. Dent. Res. 99, 769–774 (2020).
Singh, N. K. & Raza, K. Progress in deep learning-based dental and maxillofacial image analysis: A systematic review. Expert Syst. Appl. 199, 116968 (2022).
Corbella, S., Srinivas, S. & Cabitza, F. Applications of deep learning in dentistry. Oral Med. Oral Pathol. Oral Radiol. 132, 225–238 (2021).
Jeon, S.-J. et al. Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs. Dentomaxillofac. Radiol. 50, 20200513 (2021).
Sherwood, A. A. et al. A deep learning approach to segment and classify C-shaped canal morphologies in mandibular second molars using cone-beam computed tomography. J. Endod. 47, 1907–1916 (2021).
Yang, S. et al. Development and validation of a visually explainable deep learning model for classification of C-shaped canals of the mandibular second molars in periapical and panoramic dental radiographs. J. Endod. 48, 914–921 (2022).
Barbedo, J. G. A. Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Comput. Electron. Agric. 153, 46–53 (2018).
Chen, Y. et al. Generative adversarial networks in medical image augmentation: A review. Comput. Biol. Med. 144, 105382 (2022).
Xun, S. et al. Generative adversarial networks in medical image segmentation: A review. Comput. Biol. Med. 140, 105063 (2022).
Kaji, S. & Kida, S. Overview of image-to-image translation by use of deep neural networks: Denoising, super-resolution, modality conversion, and reconstruction in medical imaging. Radiol. Phys. Technol. 12, 235–248 (2019).
Ahmad, W., Ali, H., Shah, Z. & Azmat, S. A new generative adversarial network for medical images super resolution. Sci. Rep. 12, 9533 (2022).
Zhang, F. et al. PregGAN: A prognosis prediction model for breast cancer based on conditional generative adversarial networks. Comput. Methods. Programs. Biomed. 224, 107026 (2022).
Elazab, A. et al. GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images. Neural Netw. 132, 321–332 (2020).
Goodfellow, I. et al. Generative adversarial networks. Commun. ACM 63, 139–144 (2020).
Karras, T., Laine, S. & Aila, T. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 4401–4410 (2019).
Karras, T. et al. Training generative adversarial networks with limited data. Adv. Neural Inf. Process. Syst. 33, 12104–12114 (2020).
Viazovetskyi, Y., Ivashkin, V. & Kashin, E. Stylegan2 distillation for feed-forward image manipulation. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXII, Vol. 16 170–186 (Springer, 2020).
Woodland, M. et al. Evaluating the performance of StyleGAN2-ADA on medical images. In Simulation and Synthesis in Medical Imaging: 7th International Workshop, SASHIMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings 142–153 (Springer, 2022).
Hegazy, M. A. A., Cho, M. H. & Lee, S. Y. Image denoising by transfer learning of generative adversarial network for dental CT. Biomed. Phys. Eng. Express. 6, 055024. https://doi.org/10.1088/2057-1976/abb068 (2020).
Hegazy, M. A. A., Cho, M. H. & Lee, S. Y. Half-scan artifact correction using generative adversarial network for dental CT. Comput. Biol. Med. 132, 104313. https://doi.org/10.1016/j.compbiomed.2021.104313 (2021).
Hu, Z. et al. Artifact correction in low-dose dental CT imaging using Wasserstein generative adversarial networks. Med. Phys. 46, 1686–1696. https://doi.org/10.1002/mp.13415 (2019).
Jiang, C. et al. Wasserstein generative adversarial networks for motion artifact removal in dental CT imaging. In Progress in Biomedical Optics and Imaging—Proceedings of SPIE. SPIE. Vol. 10948 (2019).
Khaleghi, G., Hosntalab, M., Sadeghi, M., Reiazi, R. & Mahdavi, S. R. Metal artifact reduction in computed tomography images based on developed generative adversarial neural network. Inform. Med. Unlocked. 24, 100573. https://doi.org/10.1016/j.imu.2021.100573 (2021).
Koike, Y. et al. Deep learning-based metal artifact reduction using cycle-consistent adversarial network for intensity-modulated head and neck radiation therapy treatment planning. Phys. Med. 78, 8–14. https://doi.org/10.1016/j.ejmp.2020.08.018 (2020).
Moran, M. B. H., Faria, M. D. B., Giraldi, G. A., Bastos, L. F. & Conci, A. Using super-resolution generative adversarial network models and transfer learning to obtain high resolution digital periapical radiographs. Comput. Biol. Med. 129, 104139. https://doi.org/10.1016/j.compbiomed.2020.104139 (2021).
Eckl, M. et al. Evaluation of a cycle-generative adversarial network-based cone-beam CT to synthetic CT conversion algorithm for adaptive radiation therapy. Phys. Med. 80, 308–316. https://doi.org/10.1016/j.ejmp.2020.11.007 (2020).
Huang, Y. et al. Cephalogram synthesis and landmark detection in dental cone-beam CT systems. Med. Image. Anal. 70, 102028. https://doi.org/10.1016/j.media.2021.102028 (2021).
Lee, C., Ha, E. G., Choi, Y. J., Jeon, K. J. & Han, S. S. Synthesis of T2-weighted images from proton density images using a generative adversarial network in a temporomandibular joint magnetic resonance imaging protocol. Imaging Sci. Dent. 52, 393–398. https://doi.org/10.5624/isd.20220125 (2022).
Chau, R. C. W., Hsung, R. T., McGrath, C., Pow, E. H. N. & Lam, W. Y. H. Accuracy of artificial intelligence-designed single-molar dental prostheses: A feasibility study. J. Prosthet. Dent. https://doi.org/10.1016/j.prosdent.2022.12.004 (2023).
Chen, Q. et al. Hierarchical CNN-based occlusal surface morphology analysis for classifying posterior tooth type using augmented images from 3D dental surface models. Comput. Methods Programs Biomed. 208, 106295. https://doi.org/10.1016/j.cmpb.2021.106295 (2021).
Ding, H. et al. Morphology and mechanical performance of dental crown designed by 3D-DCGAN. Dent. Mater. https://doi.org/10.1016/j.dental.2023.02.001 (2023).
Hwang, J.-J., Azernikov, S., Efros, A. A. & Yu, S. X. Learning beyond human expertise with generative models for dental restorations. arXiv preprint arXiv:1804.00064 (2018).
Tian, S. et al. A dual discriminator adversarial learning approach for dental occlusal surface reconstruction. J. Healthc. Eng. 2022, 1933617. https://doi.org/10.1155/2022/1933617 (2022).
Tian, S. et al. DCPR-GAN: Dental crown prosthesis restoration using two-stage generative adversarial networks. IEEE J. Biomed. Health Inf. 26, 151–160. https://doi.org/10.1109/jbhi.2021.3119394 (2022).
Tian, S. et al. Efficient computer-aided design of dental inlay restoration: A deep adversarial framework. IEEE Trans. Med. Imaging. 40, 2415–2427. https://doi.org/10.1109/tmi.2021.3077334 (2021).
Yuan, F. et al. Personalized design technique for the dental occlusal surface based on conditional generative adversarial networks. Int. J. Numer. Method Biomed. Eng. 36, e3321. https://doi.org/10.1002/cnm.3321 (2020).
Kokomoto, K., Okawa, R., Nakano, K. & Nozaki, K. Intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists. Sci. Rep. 11, 18517. https://doi.org/10.1038/s41598-021-98043-3 (2021).
Kim, M. et al. Realistic high-resolution lateral cephalometric radiography generated by progressive growing generative adversarial network and quality evaluations. Sci. Rep. 11, 12563. https://doi.org/10.1038/s41598-021-91965-y (2021).
Karras, T. et al. Alias-free generative adversarial networks. Adv. Neural Inf. Process. Syst. 34, 852–863 (2021).
Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning 6105–6114 (PMLR, 2019).
Manning, S. Root canal anatomy of mandibular second molars: Part II C-shaped canals. Int Endod J. 23, 40–45 (1990).
Fetty, L. et al. Latent space manipulation for high-resolution medical image synthesis via the StyleGAN. Z. Med. Phys. 30, 305–314 (2020).
Tronchin, L., Sicilia, R., Cordelli, E., Ramella, S. & Soda, P. Evaluating GANs in medical imaging. In Deep Generative Models, and Data Augmentation, Labelling, and Imperfections: First Workshop, DGM4MICCAI 2021, and First Workshop, DALI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings, Vol. 1 112–121 (Springer, 2021).
Jung, E., Luna, M. & Park, S. H. Conditional GAN with an attention-based generator and a 3D discriminator for 3D medical image generation. In Medical Image Computing and Computer Assisted Intervention—MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI, Vol. 24 318–328 (Springer, 2021).
Chen, J., Wei, J. & Li, R. TarGAN: Target-aware generative adversarial networks for multi-modality medical image translation. In Medical Image Computing and Computer Assisted Intervention—MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI, Vol. 24 24–33 (Springer, 2021).
Li, C. et al. Interpretable generative adversarial networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36 1280–1288 (2022).
Montenegro, H., Silva, W. & Cardoso, J. S. Privacy-preserving generative adversarial network for case-based explainability in medical image analysis. IEEE Access 9, 148037–148047 (2021).
You, A., Kim, J. K., Ryu, I. H. & Yoo, T. K. Application of generative adversarial networks (GAN) for ophthalmology image domains: A survey. Eye. Vis. 9, 1–19 (2022).
