Teeth, Tech, and AI: A Revolutionizing approach in Dentistry
Keywords:
AI, Digital dentistry, oral health awareness, surveillance, Communication, Dental imaging
Abstract
Abstract
Introduction
Digital dentistry equipped with AI based integrated platforms enabled dentists to provide accurate diagnosis, prompt treatment, increased efficiency of evidence-based healthcare service provision.
Objectives
- To explore the role of AI in dentistry with emphasis on public health aspects as oral health awareness, tobacco cessation counselling and tele Dentistry
- to reconnoitre the role of AI in dental healthcare services and public health surveillance.
Methodology
Electronic search in various databases with the keywords AI, Digital dentistry, oral health awareness, surveillance, Communication, Dental imaging, were performed such as PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). 30 full-text articles were selected and systematically analysed and the relevant data were extracted.
Result and Conclusion
AI-driven platform enhanced oral health awareness, services and facilitated automated evidence synthesis in the field of dentistry making it affordable, accessible and available to the public. Integration of AI and digital dentistry holds the promise of revolutionizing the way we approach oral health care.
References
1. 1.Puja C Yavagal, Manjusha Umbranikar, Gazala Sulthana DS. Applications of digital technology in dental public health: A narrative review. Int J Appl Dent Sci 2021;7(4):162-165. DOI: https://doi.org/10.22271/oral.2021.v7.i4c.1366
2. Varshitha A. Prevalence of oral cancer in India. J Pharmaceut Sci Res. 2015;7:845–848
3. Moghimi S., Talebi M., Parisay I. Design and implementation of a hybrid genetic algorithm and artificial neural network system for predicting the sizes of unerupted canines and premolars. Eur J Orthod. 2011;34:480–486.
4.Al Haidan A., Abu-Hammad O., Dar-Odeh N. Comput Math Methods Med. 2014:1–7. 2014.
5. How Artificial Intelligence Is Driving Changes in Radiology, By Laura Cowen,February10, 2023.https://www.insideprecisionmedicine.com/news-and-features/how-artificial-intelligence-is-driving-changes-in-radiology/#
6. Bush J. How AI is taking the scut work out of health care. Harv Bus Rev. 2018 https://hbr.org/2018/03/how-ai-is-taking-the-scut-work-out-of-health-care
7. Endres MG, Hillen F, Salloumis M, et al. Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics (Basel) 2020;10:430.
8. Ekert T, Krois J, Meinhold L, et al. Deep learning for the radiographic detection of apical lesions. J Endod 2019;45:917–922.e5.
9. Setzer FC, Shi KJ, Zhang Z, et al. Artificial intelligence for the computer-aided detection of
periapical lesions in cone-beam computed tomographic images. J Endod 2020;46:987–93.
10. Christodoulou A, Mikrogeorgis G, Vouzara T, et al. A new methodology for the measurement of the root canal curvature and its 3D modification after instrumentation. Acta Odontol Scand 2018;76:488–92.
11. Liu J, Zhang C, Shan Z. Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives. Healthcare (Basel). 2023 Oct 18;11(20):2760. doi: 10.3390/healthcare11202760. PMID: 37893833; PMCID: PMC10606213.
12.Wu T.H., Lian C., Lee S., Pastewait M., Piers C., Liu J., Wang F., Wang L., Chiu C.Y., Wang W., et al. Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans. IEEE Trans. Med. Imaging. 2022;41:3158–3166. doi: 10.1109/TMI.2022.3180343.
13. Woodsend B., Koufoudaki E., Lin P., McIntyre G., El-Angbawi A., Aziz A., Shaw W., Semb G., Reesu G.V., Mossey P.A. Development of intra-oral automated landmark recognition (ALR) for dental and occlusal outcome measurements. Eur. J. Orthod. 2022;44:43–50. doi: 10.1093/ejo/cjab012.
14. Kim H, Kim CS, Lee JM, Lee JJ, Lee J, Kim JS, Choi SH. Prediction of Fishman's skeletal maturity indicators using artificial intelligence. Sci Rep. 2023 Apr 11;13(1):5870. doi: 10.1038/s41598-023-33058-6. PMID: 37041244; PMCID: PMC10090071.
15. Kök H., Acilar A.M., İzgi M.S. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Prog. Orthod. 2019;20:41. doi: 10.1186/s40510-019-0295-8.
16. Tanikawa C., Yamashiro T. Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients. Sci. Rep. 2021;11:15853.
17.Park J.H., Kim Y.-J., Kim J., Kim J., Kim I.-H., Kim N., Vaid N.R., Kook Y.-A. Use of artificial intelligence to predict outcomes of nonextraction treatment of Class II malocclusions.
18. Susic I., TravarM, Susic M. The application of CAD/CAM technology in Dentistry. IOP Conf Series: Mater Sci. 2016;200 doi: 10.1088/1757-899X/200/1/012020. 2017 Engineering.
19. Development of an artificial intelligence model to identify a dental implant from a radiograph. Hadj Saïd M, Le Roux MK, Catherine JH, Lan R. Int J Oral Maxillofac Implants. 2020;36:1077–1082.
20. Zeng W, Chen G, Ju R, Yin H, Tian W, Tang W. The combined application of database and three-dimensional image registration technology in the restoration of total nose defect. J Craniofacial Surg. 2018;29:484–487. doi: 10.1097/SCS.0000000000004500.
21. Tariq S, Gupta N, Gupta P, Sharma A. Artificial Intelligence in Public Health Dentistry. Int Healthc Res J. 2021;5(9):RV1-RV5. https://doi.org/10.26440/IHRJ/0509.12489
22.Hogan R, Goodwin M, Boothman N, Iafolla T, Pretty IA. Further opportunities for digital imaging in dental epidemiology. Journal of Dentistry 2018;74: S2-9.
23.Su N, Lagerweij MD, van der Heijden GJ. Assessment of predictive performance of caries risk assessment models based on a systematic review and meta-analysis. Journal of Dentistry 2021;110:103664.
24. Eshwar S, Ankola AV, Kumar A, Hebbal M. Evaluation of periodontal risk assessment model among adults aged 30-60 years attending KLE Dental College, Belgaum: A hospital-based study. J Indian Soc Periodontol 2010;14(3):173-7.
25. Bhattacharya S, Hossain MM. National digital health blueprint of India: A need for implementation research. J Appl Sci Clin Pract 2020;1:21-2.
26. Mannocci A, Bontempi C, Giraldi G, Chiaradia G, de Waure C, Sferrazza A et al. EpiInfo as a research and teaching tool in epidemiology and statistics: strengths and weaknesses. Ig Sanita Pubbl. 2012;68(1):85-96. Italian.
27. A smartphone-based health care chatbot to promote self-management of chronic pain (SELMA): pilot randomized controlled trial. Hauser-Ulrich S, Künzli H, Meier-Peterhans D, Kowatsch T. JMIR Mhealth Uhealth. 2020;8:0.
28. Beginnings of artificial intelligence in medicine (AIM): computational artifice assisting scientific inquiry and clinical art - with reflections on present AIM challenges. Kulikowski CA. Yearb Med Inform. 2019;28:249–256.
29. Medical ethics considerations on artificial intelligence. Keskinbora KH. J Clin Neurosci. 2019;64:277–282.
30. Artificial intelligence in dentistry: current applications and future perspectives. Chen YW, Stanley K, Att W. Quintessence Int. 2020;51:248–257.
2. Varshitha A. Prevalence of oral cancer in India. J Pharmaceut Sci Res. 2015;7:845–848
3. Moghimi S., Talebi M., Parisay I. Design and implementation of a hybrid genetic algorithm and artificial neural network system for predicting the sizes of unerupted canines and premolars. Eur J Orthod. 2011;34:480–486.
4.Al Haidan A., Abu-Hammad O., Dar-Odeh N. Comput Math Methods Med. 2014:1–7. 2014.
5. How Artificial Intelligence Is Driving Changes in Radiology, By Laura Cowen,February10, 2023.https://www.insideprecisionmedicine.com/news-and-features/how-artificial-intelligence-is-driving-changes-in-radiology/#
6. Bush J. How AI is taking the scut work out of health care. Harv Bus Rev. 2018 https://hbr.org/2018/03/how-ai-is-taking-the-scut-work-out-of-health-care
7. Endres MG, Hillen F, Salloumis M, et al. Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics (Basel) 2020;10:430.
8. Ekert T, Krois J, Meinhold L, et al. Deep learning for the radiographic detection of apical lesions. J Endod 2019;45:917–922.e5.
9. Setzer FC, Shi KJ, Zhang Z, et al. Artificial intelligence for the computer-aided detection of
periapical lesions in cone-beam computed tomographic images. J Endod 2020;46:987–93.
10. Christodoulou A, Mikrogeorgis G, Vouzara T, et al. A new methodology for the measurement of the root canal curvature and its 3D modification after instrumentation. Acta Odontol Scand 2018;76:488–92.
11. Liu J, Zhang C, Shan Z. Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives. Healthcare (Basel). 2023 Oct 18;11(20):2760. doi: 10.3390/healthcare11202760. PMID: 37893833; PMCID: PMC10606213.
12.Wu T.H., Lian C., Lee S., Pastewait M., Piers C., Liu J., Wang F., Wang L., Chiu C.Y., Wang W., et al. Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans. IEEE Trans. Med. Imaging. 2022;41:3158–3166. doi: 10.1109/TMI.2022.3180343.
13. Woodsend B., Koufoudaki E., Lin P., McIntyre G., El-Angbawi A., Aziz A., Shaw W., Semb G., Reesu G.V., Mossey P.A. Development of intra-oral automated landmark recognition (ALR) for dental and occlusal outcome measurements. Eur. J. Orthod. 2022;44:43–50. doi: 10.1093/ejo/cjab012.
14. Kim H, Kim CS, Lee JM, Lee JJ, Lee J, Kim JS, Choi SH. Prediction of Fishman's skeletal maturity indicators using artificial intelligence. Sci Rep. 2023 Apr 11;13(1):5870. doi: 10.1038/s41598-023-33058-6. PMID: 37041244; PMCID: PMC10090071.
15. Kök H., Acilar A.M., İzgi M.S. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Prog. Orthod. 2019;20:41. doi: 10.1186/s40510-019-0295-8.
16. Tanikawa C., Yamashiro T. Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients. Sci. Rep. 2021;11:15853.
17.Park J.H., Kim Y.-J., Kim J., Kim J., Kim I.-H., Kim N., Vaid N.R., Kook Y.-A. Use of artificial intelligence to predict outcomes of nonextraction treatment of Class II malocclusions.
18. Susic I., TravarM, Susic M. The application of CAD/CAM technology in Dentistry. IOP Conf Series: Mater Sci. 2016;200 doi: 10.1088/1757-899X/200/1/012020. 2017 Engineering.
19. Development of an artificial intelligence model to identify a dental implant from a radiograph. Hadj Saïd M, Le Roux MK, Catherine JH, Lan R. Int J Oral Maxillofac Implants. 2020;36:1077–1082.
20. Zeng W, Chen G, Ju R, Yin H, Tian W, Tang W. The combined application of database and three-dimensional image registration technology in the restoration of total nose defect. J Craniofacial Surg. 2018;29:484–487. doi: 10.1097/SCS.0000000000004500.
21. Tariq S, Gupta N, Gupta P, Sharma A. Artificial Intelligence in Public Health Dentistry. Int Healthc Res J. 2021;5(9):RV1-RV5. https://doi.org/10.26440/IHRJ/0509.12489
22.Hogan R, Goodwin M, Boothman N, Iafolla T, Pretty IA. Further opportunities for digital imaging in dental epidemiology. Journal of Dentistry 2018;74: S2-9.
23.Su N, Lagerweij MD, van der Heijden GJ. Assessment of predictive performance of caries risk assessment models based on a systematic review and meta-analysis. Journal of Dentistry 2021;110:103664.
24. Eshwar S, Ankola AV, Kumar A, Hebbal M. Evaluation of periodontal risk assessment model among adults aged 30-60 years attending KLE Dental College, Belgaum: A hospital-based study. J Indian Soc Periodontol 2010;14(3):173-7.
25. Bhattacharya S, Hossain MM. National digital health blueprint of India: A need for implementation research. J Appl Sci Clin Pract 2020;1:21-2.
26. Mannocci A, Bontempi C, Giraldi G, Chiaradia G, de Waure C, Sferrazza A et al. EpiInfo as a research and teaching tool in epidemiology and statistics: strengths and weaknesses. Ig Sanita Pubbl. 2012;68(1):85-96. Italian.
27. A smartphone-based health care chatbot to promote self-management of chronic pain (SELMA): pilot randomized controlled trial. Hauser-Ulrich S, Künzli H, Meier-Peterhans D, Kowatsch T. JMIR Mhealth Uhealth. 2020;8:0.
28. Beginnings of artificial intelligence in medicine (AIM): computational artifice assisting scientific inquiry and clinical art - with reflections on present AIM challenges. Kulikowski CA. Yearb Med Inform. 2019;28:249–256.
29. Medical ethics considerations on artificial intelligence. Keskinbora KH. J Clin Neurosci. 2019;64:277–282.
30. Artificial intelligence in dentistry: current applications and future perspectives. Chen YW, Stanley K, Att W. Quintessence Int. 2020;51:248–257.
Published
2024-07-20
How to Cite
Nahid, S. (2024). Teeth, Tech, and AI: A Revolutionizing approach in Dentistry. UNIVERSITY JOURNAL OF DENTAL SCIENCES, 10(2). https://doi.org/10.21276//ujds.2024.10.2.22
Section
Review Article
