Analysis of frontiers and hotspots of artificial intelligence applied in stomatology_Chinese Journal of Evidence-Based Medicine

Authors：

TANG Li ^1,2 ,  XU Xin ^1,2 , LIAO Ga ^1,3

1. State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases West China Hospital of Stomatology, Sichuan University, Chengdu 610041, P. R. China;
2. Department of Cariology and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, P. R. China;
3. Medical Big Data Center, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

XU Xin, Email: liaoga@hotmail.com

Keywords：

Artificial intelligence; Stomatology; Robotic surgery; Visualization; Bibliometric

DOI：

10.7507/1672-2531.202202103

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To analyze the research status and summarize research hotspots and development trends of research on artificial intelligence in stomatology. Methods Data retrieved from the Web of Science Core Collection database from inception to 2021 were analyzed by CiteSpace software. Results The number of publications about artificial intelligence in stomatology was rising. The United States ranked first in terms of publications and cooperation capabilities. Apart from comprehensive stomatology journals, the literature was mainly published by specialist journals of oral and maxillofacial surgery, orthodontic and dental radiology. Oral head and neck tumors were the frontier field of artificial intelligence research in stomatology. Artificial intelligence, including deep learning and neural networks, showed the tremendous potential medical value and economic value in assisting in the diagnosis and treatment decisions of oral diseases. Conclusion The research of artificial intelligence in stomatology has rapidly increased, which is conducive to the development of stomatology in the direction of digitalization, intelligence, and individuation.

Citation： TANG Li, XU Xin, LIAO Ga. Analysis of frontiers and hotspots of artificial intelligence applied in stomatology. Chinese Journal of Evidence-Based Medicine, 2022, 22(6): 684-691. doi: 10.7507/1672-2531.202202103 Copy

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16.	Bernabe E, Marcenes W, Hernandez CR, et al. Global, regional, and national levels and trends in burden of oral conditions from 1990 to 2017: a systematic analysis for the global burden of disease 2017 study. J Dent Res, 2020, 99(4): 362-373.
17.	Reymus M, Liebermann A, Diegritz C. Virtual reality: an effective tool for teaching root canal anatomy to undergraduate dental students - a preliminary study. Int Endod J, 2020, 53(11): 1581-1587.
18.	Abe S, Noguchi N, Matsuka Y, et al. Educational effects using a robot patient simulation system for development of clinical attitude. Eur J Dent Educ, 2018, 22(3): e327-e336.
19.	Xia C, Dong X, Li H, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J, 2022, 135(5): 584-590.
20.	Hararah MK, Stokes WA, Jones BL, et al. Nomogram for preoperative prediction of nodal extracapsular extension or positive surgical margins in oropharyngeal squamous cell carcinoma. Oral Oncol, 2018, 83: 73-80.
21.	Bur AM, Holcomb A, Goodwin S, et al. Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma. Oral Oncol, 2019, 92: 20-25.
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23.	Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM, 2017, 60(6): 84-90.
24.	Gondivkar SM, Gadbail AR, Gondivkar RS, et al. Nutrition and oral health. Dis Mon, 2019, 65(6): 147-154.
25.	Sampaio-Maia B, Caldas IM, Pereira ML, et al. The oral microbiome in health and its implication in oral and systemic diseases. Adv Appl Microbiol, 2016, 97: 171-210.
26.	Hosseinpoor AR, Itani L, Petersen PE. Socio-economic inequality in oral healthcare coverage: results from the world health survey. J Dent Res, 2012, 91(3): 275-281.
27.	Mohamed M, Ferguson JD, Venugopal A, et al. An artificial intelligence based referral application to optimize orthodontic referrals in a public oral healthcare system. Semin Orthod, 2021, 27(2): 157-163.
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30.	Loftus TJ, Tighe PJ, Filiberto AC, et al. Artificial intelligence and surgical decision-making. JAMA Surg, 2020, 155(2): 148-158.

1. Simmons AB, Chappell SG. Artificial-intelligence - definition and practice. IEEE J Ocean Eng, 1988, 13(2): 14-42.
2. He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med, 2019, 25(1): 30-36.
3. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism, 2017, 69S: S36-S40.
4. Tran BX, Vu GT, Ha GH, et al. Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. J Clin Med, 2019, 8(3): 360.
5. Chen CM. Citespace Ⅱ: Detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Tec, 2006, 57(3): 359-377.
6. Sims-Williams JH, Brown ID, Matthewman A, et al. A computer-controlled expert system for orthodontic advice. Br Dent J, 1987, 163(5): 161-166.
7. Chen CM. Science mapping: A systematic review of the literature. J Data Info Sci, 2017, 2(2): 1-40.
8. 胡越, 罗东阳, 花奎, 等. 关于深度学习的综述与讨论. 智能系统学报, 2019, 14(1): 1-19.
9. Casalegno F, Newton T, Daher R, et al. Caries detection with near-infrared transillumination using deep learning. J Dent Res, 2019, 98(11): 1227-1233.
10. Orhan K, Bayrakdar IS, Ezhov M, et al. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J, 2020, 53(5): 680-689.
11. Lee JH, Kim DH, Jeong SN, et al. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci, 2018, 48(2): 114-123.
12. Warin K, Limprasert W, Suebnukarn S, et al. Automatic classification and detection of oral cancer in photographic images using deep learning algorithms. J Oral Pathol Med, 2021, 50(9): 911-918.
13. Shoukri B, Prieto JC, Ruellas A, et al. Minimally invasive approach for diagnosing tmj osteoarthritis. J Dent Res, 2019, 98(10): 1103-1111.
14. Retrouvey JM. The role of ai and machine learning in contemporary orthodontics. Apos Trends Orthod, 2021, 11(1): 74-80.
15. 于文俊, 席庆. 达芬奇机器人在口腔颌面外科中的应用. 中华老年口腔医学杂志, 2021, 19(6): 360-364.
16. Bernabe E, Marcenes W, Hernandez CR, et al. Global, regional, and national levels and trends in burden of oral conditions from 1990 to 2017: a systematic analysis for the global burden of disease 2017 study. J Dent Res, 2020, 99(4): 362-373.
17. Reymus M, Liebermann A, Diegritz C. Virtual reality: an effective tool for teaching root canal anatomy to undergraduate dental students - a preliminary study. Int Endod J, 2020, 53(11): 1581-1587.
18. Abe S, Noguchi N, Matsuka Y, et al. Educational effects using a robot patient simulation system for development of clinical attitude. Eur J Dent Educ, 2018, 22(3): e327-e336.
19. Xia C, Dong X, Li H, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J, 2022, 135(5): 584-590.
20. Hararah MK, Stokes WA, Jones BL, et al. Nomogram for preoperative prediction of nodal extracapsular extension or positive surgical margins in oropharyngeal squamous cell carcinoma. Oral Oncol, 2018, 83: 73-80.
21. Bur AM, Holcomb A, Goodwin S, et al. Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma. Oral Oncol, 2019, 92: 20-25.
22. Haenlein M, Kaplan A. A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif Manage Rev, 2019, 61(4): 5-14.
23. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM, 2017, 60(6): 84-90.
24. Gondivkar SM, Gadbail AR, Gondivkar RS, et al. Nutrition and oral health. Dis Mon, 2019, 65(6): 147-154.
25. Sampaio-Maia B, Caldas IM, Pereira ML, et al. The oral microbiome in health and its implication in oral and systemic diseases. Adv Appl Microbiol, 2016, 97: 171-210.
26. Hosseinpoor AR, Itani L, Petersen PE. Socio-economic inequality in oral healthcare coverage: results from the world health survey. J Dent Res, 2012, 91(3): 275-281.
27. Mohamed M, Ferguson JD, Venugopal A, et al. An artificial intelligence based referral application to optimize orthodontic referrals in a public oral healthcare system. Semin Orthod, 2021, 27(2): 157-163.
28. Sun TQ, Medaglia R. Mapping the challenges of artificial intelligence in the public sector: evidence from public healthcare. Gov Inf Q, 2019, 36(2): 368-383.
29. Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res, 2020, 99(7): 769-774.
30. Loftus TJ, Tighe PJ, Filiberto AC, et al. Artificial intelligence and surgical decision-making. JAMA Surg, 2020, 155(2): 148-158.

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