Opportunities for artificial intelligence and telemedicine in implantology

Мұқаба

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Тек жазылушылар үшін

Аннотация

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.

Толық мәтін

##article.viewOnOriginalSite##

Авторлар туралы

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 

Ресей, Saint Petersburg

G. Brudyan

Voskresensk Dental Polyclinic

Хат алмасуға жауапты Автор.
Email: dr-brudyan@mail.ru
Ресей, Voskresensk

Әдебиет тізімі

  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

Осы сайт cookie-файлдарды пайдаланады

Біздің сайтты пайдалануды жалғастыра отырып, сіз сайттың дұрыс жұмыс істеуін қамтамасыз ететін cookie файлдарын өңдеуге келісім бересіз.< / br>< / br>cookie файлдары туралы< / a>