International Journal of Oral Biology

  • 제48권4호
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  • Pages.45-49
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  • 2023
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  • 1226-7155(pISSN)
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  • 2287-6618(eISSN)
대한구강생물학회 (The Korean Academy of Oral Biology)
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The principles of artificial intelligence and its applications in dentistry

  • Yoohyun Lee (Department of Oral Microbiology, School of Dentistry, Chonnam National University) ;
  • Seung-Ho Ohk (Department of Oral Microbiology, School of Dentistry, Chonnam National University)
  • 투고 : 2023.11.30
  • 심사 : 2023.12.12
  • 발행 : 2023.12.31
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초록

Digital dentistry has witnessed significant advancements in recent years, driven by extensive research following the introduction of cutting-edge technologies such as CAD/CAM and 3D oral scanners. Until now, 2D images obtained via x-ray or CT scans were critical to detect anomalies and for decision-making. This review describes the main principles and applications of supervised, unsupervised, and reinforcement learning in medical applications. In this context, we present a diverse range of artificial intelligence networks with potential applications in dentistry, accompanied by existing results in the field.

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과제정보

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF2015R1D1A1A01057503).

참고문헌

  1. He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. IEEE Trans Pattern Anal Mach Intell 2020;42:386-97. doi: 10.1109/TPAMI.2018.2844175
  2. Cui ZM, Li CJ, Wang WP. ToothNet: automatic tooth instance segmentation and identification from cone beam CT images. Paper presented at: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2019 Jun 16-20; Long Beach, USA. Piscataway: IEEE, 2019. p. 6361-70.
  3. Sivagami S, Chitra P, Kailash GSR, Muralidharan SR. UNet architecture based dental panoramic image segmentation. Paper presented at: 2020 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET); 2020 Aug 4-6; Chennai, India. Piscataway: IEEE, 2020. p. 187-91.
  4. Chen Z, Chen S, Hu F. CTA-UNet: CNN-transformer architecture UNet for dental CBCT images segmentation. Phys Med Biol 2023;68:175042. doi: 10.1088/1361-6560/acf026
  5. Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res 2003;3:993-1022.
  6. van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res 2008;9:2579-605.
  7. Motamed S, Rogalla P, Khalvati F. Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images. Inform Med Unlocked 2021;27:100779. doi: 10.1016/j.imu.2021.100779