Skip to main content

Advertisement

SpringerLink
Log in

Generative adversarial networks in dental imaging: a systematic review

  • Review Article
  • Published:
Oral Radiology Aims and scope Submit manuscript

Abstract

Objectives

This systematic review on generative adversarial network (GAN) architectures for dental image analysis provides a comprehensive overview to readers regarding current GAN trends in dental imagery and potential future applications.

Methods

Electronic databases (PubMed/MEDLINE, Scopus, Embase, and Cochrane Library) were searched to identify studies involving GANs for dental image analysis. Eighteen full-text articles describing the applications of GANs in dental imagery were reviewed. Risk of bias and applicability concerns were assessed using the QUADAS-2 tool.

Results

GANs were used for various imaging modalities, including two-dimensional and three-dimensional images. In dental imaging, GANs were utilized for tasks such as artifact reduction, denoising, and super-resolution, domain transfer, image generation for augmentation, outcome prediction, and identification. The generated images were incorporated into tasks such as landmark detection, object detection and classification. Because of heterogeneity among the studies, a meta-analysis could not be conducted. Most studies (72%) had a low risk of bias in all four domains. However, only three (17%) studies had a low risk of applicability concerns.

Conclusions

This extensive analysis of GANs in dental imaging highlighted their broad application potential within the dental field. Future studies should address limitations related to the stability, repeatability, and overall interpretability of GAN architectures. By overcoming these challenges, the applicability of GANs in dentistry can be enhanced, ultimately benefiting the dental field in its use of GANs and artificial intelligence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Yi X, Walia E, Babyn P. Generative adversarial network in medical imaging: a review. Med Image Anal. 2019;58:101552.

    Article  PubMed  Google Scholar 

  2. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks. Commun ACM. 2020;63:139–44.

    Article  Google Scholar 

  3. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. Imagenet: a large-scale hierarchical image database. In: Proceedings of the 2009 IEEE conference on computer vision and pattern recognition. IEEE; 2009.

  4. Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, et al. Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In: Proceedings of the proceedings of the AAAI conference on artificial intelligence; 2019.

  5. Johnson AE, Pollard TJ, Shen L, Lehman L-WH, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3:1–9.

  6. Sorin V, Barash Y, Konen E, Klang E. Creating artificial images for radiology applications using generative adversarial networks (GANs)—a systematic review. Acad Radiol. 2020;27:1175–85.

    Article  PubMed  Google Scholar 

  7. Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2015;2015:349.

    Google Scholar 

  8. Schardt C, Adams MB, Owens T, Keitz S, Fontelo P. Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Med Inform Decis Mak. 2007;7:1–6.

    Article  Google Scholar 

  9. Hegazy MAA, Cho MH, Lee SY. Half-scan artifact correction using generative adversarial network for dental CT. Comput Biol Med. 2021;132:104313.

    Article  PubMed  Google Scholar 

  10. Hu Z, Jiang C, Sun F, Zhang Q, Ge Y, Yang Y, et al. Artifact correction in low-dose dental CT imaging using Wasserstein generative adversarial networks. Med Phys. 2019;46:1686–96.

    Article  PubMed  Google Scholar 

  11. Jiang C, Zhang Q, Ge Y, Liang D, Yang Y, Liu X, et al. Wasserstein generative adversarial networks for motion artifact removal in dental CT imaging. In: Proceedings of the progress in biomedical optics and imaging—proceedings of SPIE; 2019.

  12. Koike Y, Anetai Y, Takegawa H, Ohira S, Nakamura S, Tanigawa N. Deep learning-based metal artifact reduction using cycle-consistent adversarial network for intensity-modulated head and neck radiation therapy treatment planning. Physica Med. 2020;78:8–14.

    Article  Google Scholar 

  13. Park HS, Jeon K, Lee SH, Seo JK. Unpaired-paired learning for shading correction in cone-beam computed tomography. IEEE Access. 2022;10:26140–8.

    Article  Google Scholar 

  14. Khaleghi G, Hosntalab M, Sadeghi M, Reiazi R, Mahdavi SR. Metal artifact reduction in computed tomography images based on developed generative adversarial neural network. Inf Med Unlock. 2021;24:100573.

    Article  Google Scholar 

  15. Hegazy MAA, Cho MH, Lee SY. Image denoising by transfer learning of generative adversarial network for dental CT. Biomed Phys Eng Express. 2020;6:055024.

    Article  PubMed  Google Scholar 

  16. Moran MBH, Faria MDB, Giraldi GA, Bastos LF, Conci A. Using super-resolution generative adversarial network models and transfer learning to obtain high resolution digital periapical radiographs. Comput Biol Med. 2021;129:104139.

    Article  PubMed  Google Scholar 

  17. Huang Y, Fan F, Syben C, Roser P, Mill L, Maier A. Cephalogram synthesis and landmark detection in dental cone-beam CT systems. Med Image Anal. 2021;70:102028.

    Article  PubMed  Google Scholar 

  18. Lee C, Ha EG, Choi YJ, Jeon KJ, Han SS. 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. 2022;52:393–8.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Kim M, Kim S, Kim M, Bae HJ, Park JW, Kim N. Realistic high-resolution lateral cephalometric radiography generated by progressive growing generative adversarial network and quality evaluations. Sci Rep. 2021;11:12563.

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  20. 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. 2021;11:18517.

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  21. Krishnamoorthy VK, Baskaran S. Optimized adversarial network with faster residual deep learning for osteoarthritis classification in panoramic radiography. Int J Intell Eng Syst. 2022;15:191–200.

    Google Scholar 

  22. Kearney VP, Yansane A-IM, Brandon RG, Vaderhobli R, Lin G-H, Hekmatian H, et al. A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level. J Dent. 2022;123:104211.

  23. Park YS, Choi JH, Kim Y, Choi SH, Lee JH, Kim KH, et al. Deep learning-based prediction of the 3D postorthodontic facial changes. J Dent Res. 2022;101:1372–9.

    Article  PubMed  CAS  Google Scholar 

  24. Yong TH, Yang S, Lee SJ, Park C, Kim JE, Huh KH, et al. QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study. Sci Rep. 2021;11:15083.

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  25. Li Y, Wang J, Liang W, Xue H, He Z, Lv J, et al. CR-GAN: automatic craniofacial reconstruction for personal identification. Pattern Recogn. 2022;124:108400.

    Article  Google Scholar 

  26. Jeong JJ, Tariq A, Adejumo T, Trivedi H, Gichoya JW, Banerjee I. Systematic review of generative adversarial networks (GANs) for medical image classification and segmentation. J Digit Imaging. 2022;35:137–52.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. Preprint arXiv:1511.06434; 2015.

  28. Mao X, Li Q, Xie H, Lau RY, Wang Z, Paul Smolley S. Least squares generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision; 2017.

  29. Weng L. From GAN to WGAN. arXiv Preprint arXiv:1904.08994; 2019.

  30. Karras T, Aila T, Laine S, Lehtinen J. Progressive growing of GANS for improved quality, stability, and variation. arXiv Preprint arXiv:1710.10196; 2017.

  31. Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T. Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; 2020.

  32. Mirza M, Osindero S. Conditional generative adversarial nets. arXiv Preprint arXiv:1411.1784; 2014.

  33. You A, Kim JK, Ryu IH, Yoo TK. Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey. Eye Vis. 2022;9:1–19.

    Article  Google Scholar 

  34. Isola P, Zhu J-Y, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017.

  35. Sood R, Topiwala B, Choutagunta K, Sood R, Rusu M. An application of generative adversarial networks for super resolution medical imaging. In: Proceedings of the 2018 17th IEEE international conference on machine learning and applications (ICMLA); 2018. IEEE.

  36. Zhu J-Y, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision; 2017.

  37. Ding Z, Jiang S, Zhao J. Take a close look at mode collapse and vanishing gradient in GAN. In: Proceedings of the 2022 IEEE 2nd international conference on electronic technology, communication and information (ICETCI); 2022. IEEE.

  38. Demir U, Unal G. Patch-based image inpainting with generative adversarial networks. arXiv Preprint arXiv:1803.07422; 2018.

  39. Wang C, Yang G, Papanastasiou G, Tsaftaris SA, Newby DE, Gray C, et al. DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis. Inf Fusion. 2021;67:147–60.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Xie H, Lei H, Zeng X, He Y, Chen G, Elazab A, et al. AMD-GAN: Attention encoder and multi-branch structure based generative adversarial networks for fundus disease detection from scanning laser ophthalmoscopy images. Neural Netw. 2020;132:477–90.

    Article  PubMed  Google Scholar 

  41. Johnson DH. Signal-to-noise ratio. Scholarpedia. 2006;1:2088.

    Article  ADS  Google Scholar 

  42. Hore A, Ziou D. Image quality metrics: PSNR vs. SSIM. In: Proceedings of the 2010 20th international conference on pattern recognition. IEEE; 2010.

  43. Chai T, Draxler RR. Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci Model Develop. 2014;7:1247–50.

    Article  ADS  Google Scholar 

  44. da Costa-Luis CO, Reader AJ. Micro-networks for robust MR-guided low count PET imaging. IEEE Trans Radiat Plasma Med Sci. 2020;5:202–12.

    Article  PubMed  Google Scholar 

  45. Xun S, Li D, Zhu H, Chen M, Wang J, Li J, et al. Generative adversarial networks in medical image segmentation: a review. Comput Biol Med. 2022;140:105063.

    Article  PubMed  Google Scholar 

  46. Sounderajah V, Ashrafian H, Aggarwal R, De Fauw J, Denniston AK, Greaves F, et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: the STARD-AI Steering Group. Nat Med. 2020;26:807–8.

    Article  PubMed  CAS  Google Scholar 

  47. Mongan J, Moy L, Kahn CE Jr. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Soc North Ame. 2020;2020:e200029.

  48. Schwendicke F, Singh T, Lee J-H, Gaudin R, Chaurasia A, Wiegand T, et al. Artificial intelligence in dental research: checklist for authors, reviewers, readers. J Dent. 2021;107:103610.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank Ryan Chastain-Gross, Ph.D., from Liwen Bianji (Edanz) (www.liwenbianji.cn/) for editing the English text of a draft of this manuscript.

Funding

Not applicable.

Author information

Author notes

  1. Sujin Yang and Kee-Deog Kim have contributed equally to this work.

Authors and Affiliations

  1. Department of Advanced General Dentistry, College of Dentistry, Yonsei University, Seoul, Korea

    Sujin Yang & Kee-Deog Kim

  2. Department of Oral and Maxillofacial Radiology, School of Dentistry, Aichi Gakuin University, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan

    Eiichiro Ariji & Yoshitaka Kise

Authors
  1. Sujin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kee-Deog Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eiichiro Ariji
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yoshitaka Kise
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SY and KK were responsible for review conceptualization, literature search, data analysis, and drafting and critical revision of the manuscript. YK and EA performed the literature search, data analysis, and critical revision of the manuscript.

Corresponding author

Correspondence to Yoshitaka Kise.

Ethics declarations

Conflict of interest

None.

Ethics approval

Not applicable.

Informed consent

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, S., Kim, KD., Ariji, E. et al. Generative adversarial networks in dental imaging: a systematic review. Oral Radiol 40, 93–108 (2024). https://doi.org/10.1007/s11282-023-00719-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11282-023-00719-1

Keywords

Search

Navigation