Opportunities for artificial intelligence and telemedicine in implantology

封面

如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅或者付费存取

详细

Artificial Intelligence (AI) has been making significant strides in various fields, including healthcare. One such area is dental implantology. AI can assist in accurate diagnosis, treatment planning, in the execution of the procedure, and predict implant success based on various factors like bone density, implant site, patient's medical history, etc.

Despite the promising potential, the application of AI in dental implantology is still in its nascent stages. Research in this area of medicine is limited, but there has been an increase in recent years. This trend is related to the possibility of improving patient outcomes, including shorter treatment times, prevention of complications and improved quality of care in general.

全文:

受限制的访问

作者简介

P. Seliverstov

S.M. Kirov Military Medical Academy

Email: dr-brudyan@mail.ru
ORCID iD: 0000-0001-5623-4226

Candidate of Medical Sciences, Associate Professor 

俄罗斯联邦, Saint Petersburg

G. Brudyan

Voskresensk Dental Polyclinic

编辑信件的主要联系方式.
Email: dr-brudyan@mail.ru
俄罗斯联邦, Voskresensk

参考

  1. Sikri A., Sikri J., Gupta R. (2023). Artificial Intelligence in Prosthodontics and Oral Implantology – A Narrative Review. Glob Acad J Dent Oral Health. 2023; 5 (2): 13–9. doi: 10.36348/gajdoh.2023.v05i02.001
  2. Jacobs R., Salmon B., Codari M. et al. Cone beam computed tomography in implant dentistry: recommendations for clinical use. BMC Oral Health. 2018; 18 (1): 88. doi: 10.1186/s12903-018-0523-5
  3. Ivanov D.V., Dol A.V., Smirnov D.A. Optimization of dental implant treatment. Russian Open Medical Journal. 2016; 5: e0102. doi: 10.15275/rusomj.2016.0102
  4. Chen S., Wang L., Li G. et al. Machine learning in orthodontics: Introducing a 3D auto-segmentation and auto-landmark finder of CBCT images to assess maxillary constriction in unilateral impacted canine patients. Angle Orthod. 2020; 90 (1): 77–84. doi: 10.2319/012919-59.1
  5. Chen Y., Du H., Yun Zh. et al. Automatic Segmentation of Individual Tooth in Dental CBCT Images From Tooth Surface Map by a Multi-Task FCN. IEEE Access. 2020; 8: 97296–309. doi: 10.1109/ACCESS.2020.2991799
  6. Kurt Bayrakdar S., Orhan K., Bayrakdar I.S. et al. A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med Imaging. 2021; 21 (1): 86. doi: 10.1186/s12880-021-00618-z
  7. Yang X. et al. Two-Stream Regression Network for Dental Implant Position Prediction. arXiv:2305.10044 [cs.CV]. doi: 10.48550/arXiv.2305.10044 URL: https://arxiv.org/pdf/2305.10044.pdf
  8. Селиверстов П.В., Безручко Д.С., Васин А.В. и др. Телемедицинский дистанционный многопрофильный анкетный скрининг как инструмент раннего выявления хронических неинфекционных заболеваний. Медицинский совет. 2023; 6: 311–2 [Seliverstov P.V., Bezruchko D.S., Vasin A.V. et al. Telemedicine remote multidisciplinary questionnaire screening as a tool for early detection of chronic non-communicable diseases. Medical Council. 2023; 6: 311–21 (in Russ.)]. doi: 10.21518/ms2023-070

补充文件

附件文件
动作
1. JATS XML

版权所有 © Russkiy Vrach Publishing House, 2023
##common.cookie##