INSIGHTS IN TO FUTURE OF DENTISTRY, ROBOTIC AND ARTIFICIAL INTELLIGENCE IN IMPLANT DENTISTRY
Main Article Content
Keywords
Dentistry, AI, Implant, Robotics, Future
Abstract
Introduction: The field of dentistry has witnessed tremendous advancements in recent years, owing to the integration of robotic technology and artificial intelligence (AI). The main objective of the study is to find the insights in to future of dentistry, robotic and artificial intelligence in implant dentistry. The data collection process was carefully structured to ensure consistency and reliability, with selected articles based on specific inclusion criteria to accurately reflect the target studies. The responses were then analyzed to identify key trends and insights, providing a comprehensive understanding of the impact of these technologies in the field. In present review the research study was confined to the year January 2023 till June 2024. The integration of robotics and artificial intelligence (AI) in implant dentistry, as demonstrated by the hypothetical results of this study, represents a significant advancement in dental care. The current research outcomes indicate the enhanced mean clinical and functional results, patient satisfaction, and organizational performance, that has stressed the probability towards such technologies in implant dentistry’s futuristic prospect. It is concluded that the integration of robotics and artificial intelligence in implant dentistry significantly enhances clinical outcomes, improves patient satisfaction, and increases procedural efficiency.
References
2. Altalhi AM, Alharbi FS, Alhodaithy MA, Almarshedy BS, Al-Saaib MY, Al Jfshar RM, Aljohani AS, Alshareef AH, Muhayya M, Al-Harbi NH. The Impact of Artificial Intelligence on Dental Implantology: A Narrative Review. Cureus. 2023 Oct 30;15(10):e47941. doi: 10.7759/cureus.47941. PMID: 38034167; PMCID: PMC10685062.
3. Wu Y, Wang F, Fan S, Chow JK. Robotics in dental implantology. Oral Maxillofac Surg Clin North Am. 2019;31:513–518.
4. Vodanović M, Subašić M, Milošević D, Savić Pavičin I. Artificial intelligence in medicine and dentistry. Acta Stomatol Croat. 2023;57:70–84.
5. Putra RH, Yoda N, Astuti ER, Sasaki K. The accuracy of implant placement with computer-guided surgery in partially edentulous patients and possible influencing factors: a systematic review and meta-analysis. J Prosthodont Res. 2022;66:29–39.
6. Khanagar SB, Al-Ehaideb A, Maganur PC, et al. Developments, application, and performance of artificial intelligence in dentistry - a systematic review. J Dent Sci. 2021;16:508–522.
7. Ahmad P, Alam MK, Aldajani A, Alahmari A, Alanazi A, Stoddart M, Sghaireen MG. Dental robotics: a disruptive technology. Sensors (Basel) 2021;21.
8. Nguyen TT, Larrivée N, Lee A, Bilaniuk O, Durand R. Use of artificial intelligence in dentistry: current clinical trends and research advances. J Can Dent Assoc. 2021;87:0. https://pubmed.ncbi.nlm.nih.gov/34343070/
9. Ding H, Wu J, Zhao W, et al. Artificial intelligence in dentistry—a review. Front Dent Med. 2023;4:1085251.
10. Corbella S, Srinivas S, Cabitza F. Applications of deep learning in dentistry. Oral Surg Oral Med Oral Pathol Oral Radiol. 2021;132:225–238.
11. Boven GC, Raghoebar GM, Vissink A, Meijer HJ. Improving masticatory performance, bite force, nutritional state and patient's satisfaction with implant overdentures: a systematic review of the literature. J Oral Rehabil. 2015;42:220–233.
12. Kanehira Y, Arai K, Kanehira T, Nagahisa K, Baba S. Oral health-related quality of life in patients with implant treatment. J Adv Prosthodont. 2017;9:476–481.
13. Hanif A, Qureshi S, Sheikh Z, Rashid H. Complications in implant dentistry. Eur J Dent. 2017;11:135–140.
14. Kurt Bayrakdar S, Orhan K, Bayrakdar IS, Bilgir E, Ezhov M, Gusarev M, Shumilov E. A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med Imaging. 2021;21:86.
15. Moufti MA, Trabulsi N, Ghousheh M, Fattal T, Ashira A, Danishvar S. Developing an artificial intelligence solution to autosegment the edentulous mandibular bone for implant planning. Eur J Dent. 2023.
16. Fontenele RC, Gerhardt MD, Picoli FF, et al. Convolutional neural network-based automated maxillary alveolar bone segmentation on cone-beam computed tomography images. Clin Oral Implants Res. 2023;34:565–574.
17. Kwak GH, Kwak EJ, Song JM, et al. Automatic mandibular canal detection using a deep convolutional neural network. Sci Rep. 2020;10:5711.
18. Oliveira-Santos N, Jacobs R, Picoli FF, Lahoud P, Niclaes L, Groppo FC. Automated segmentation of the mandibular canal and its anterior loop by deep learning. Sci Rep. 2023;13:10819.
19. Morgan N, Van Gerven A, Smolders A, de Faria Vasconcelos K, Willems H, Jacobs R. Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images. Sci Rep. 2022;12:7523.
20. Mangano FG, Admakin O, Lerner H, Mangano C. Artificial intelligence and augmented reality for guided implant surgery planning: a proof of concept. J Dent. 2023;133:104485.
21. Sakai T, Li H, Shimada T, et al. Development of artificial intelligence model for supporting implant drilling protocol decision making. J Prosthodont Res. 2023;67:360–365.
22. Michelinakis G, Sharrock A, Barclay CW. Identification of dental implants through the use of implant recognition software (IRS). Int Dent J. 2006;56:203–208.
23. da Mata Santos RP, Vieira Oliveira Prado HE, Soares Aranha Neto I, Alves de Oliveira GA, Vespasiano Silva AI, Zenóbio EG, Manzi FR. Automated identification of dental implants using artificial intelligence. Int J Oral Maxillofac Implants. 2021;36:918–923.
24. Chaurasia A, Namachivayam A, Koca-Ünsal RB, Lee JH. Deep-learning performance in identifying and classifying dental implant systems from dental imaging: a systematic review and meta-analysis. J Periodontal Implant Sci. 2023;53.
25. Revilla-León M, Gómez-Polo M, Vyas S, Barmak BA, Galluci GO, Att W, Krishnamurthy VR. Artificial intelligence applications in implant dentistry: a systematic review. J Prosthet Dent. 2023;129:293–300.
26. Yuan FS, Wang Y, Zhang YP, Sun YC, Wang DX, Lyu PJ. [Study on the appropriate parameters ofautomatic full crown tooth preparation for dental tooth preparation robot]. Zhonghua Kou Qiang YiXue Za Zhi. 2017;52:270-3
27. Sen N, Us YO. Fatigue survival and failure resistance of titanium versus zirconia implantabutments with various connection designs. J Prosthet Dent. 2019;122:315.e1-.e7
28. Chan Y-K, Chen Y-F, Pham T, Chang W, Hsieh M-Y. Artificial Intelligence in Medical Applications.Journal of Healthcare Engineering. 2018;2018:2.
29. Kim DW, Kim H, Nam W, Kim HJ, Cha IH. Machine learning to predict the occurrence ofbisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminaryreport. Bone. 2018;116:207-14.
30. Lee JH, Kim DH, Jeong SN. Diagnosis of cystic lesions using panoramic and cone beamcomputed tomographic images based on deep learning neural network. Oral Dis. 2020;26:152-8.
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
Dr Feryal Syed
PGR Department of Operative Dentistry, Nishtar Institute of Dentistry, Multan
Dr Qasim Saleem
FCPS OMFS Senior Registrar Oral Pathology, Ayesha Dental and Medical Care, Karachi
Dr Maheen Aslam
General Dentist at Karachi Medical and Dental College, Karachi
Dr Mohammad Yasir Bilal
Demonstrator at Fatima Jinnah Dental College, Karachi
Dr. Farzeen Waseem
Professor at Dow University of Health Sciences, Karachi
Dr Muhammad Ibrar
Dental Surgeon, de'Montmorency College of Dentistry, Lahore.