Artificial intelligence and oral photography: an approach to the epidemiology of dental caries

Authors

  • Luiz Roberto Augusto Noro Federal University of Rio Grande do Norte image/svg+xml
  • Maria Cristina Manzanares Céspedes University of Barcelona image/svg+xml

DOI:

https://doi.org/10.11606/s15188787.2025059006910

Keywords:

Dental Caries, Artificial Intelligence, Epidemiology, Diagnosis, Dental Photography

Abstract

OBJECTIVE: Dental caries is an important public health issue due to its high prevalence around the world, its impact on people’s quality of life, and the existence of effective methods to control and prevent it. This study aims to find the state of the art regarding the diagnosis of caries with the use of artificial intelligence in the scientific literature, which could represent future advances in its use in oral health epidemiology. METHODS: A scoping review was carried out based on a search strategy on the main health databases that found 1,439 articles by descriptors or keywords related to caries, diagnosis, and artificial intelligence. RESULTS: After analysis, 17 scientific articles composed the final sample. Of these, 94.1% are quite recent, published from 2020 onward. Although the articles were based on a clinical perspective, their objectives, results, and conclusions signal the possible effectiveness of artificial intelligence as a strategic tool for epidemiology. Frontal, lateral, and occlusal photographs served to diagnose caries on all sides of the teeth. CONCLUSION: It is essential to invest in alternatives related to artificial intelligence and oral photography to replace traditional epidemiological surveys, which would enable the full development of oral health surveillance.

 

References

1. Daly B, Watt R, Batchelor P, Treasure E. Essential Dental Public Health [Internet]. New York: Oxford University Press, 2020 [citado 2025 jun 14]. Disponível em: https://doi.org/10.1093/oso/9780199679379.001.0001

2. World Health Organization. Global oral health status report: towards universal health coverage for oral health by 2030 [Internet]. Geneva: WHO; 2022 [citado 2025 jun 14]. Disponível em: https://www.who.int/publications/i/item/9789240061484

3. Campêlo MCC, Lins RML, Alves GF, Costa JCS, Santos Júnior VE. Avaliação do impacto da dor de dente, da cárie não tratada e suas consequências na qualidade de vida de crianças brasileiras. RFO UPF 2020;25(1):88-95. https://doi.org/10.5335/rfo.v25i1.10236

4. Gonçalves MJN, Wanderley FGC, Silva RA, Almeida TF. Absenteísmo por causa odontológica: uma revisão de literatura relacionada à ausência no trabalho e a saúde bucal do trabalhador. RFO UPF 2015;20(2):264-70. https://doi.org/10.5335/rfo.v20i2.4466

5. Roncalli AG, Noro LRA, Cury JA, Zilbovicius C, Pinheiro HHC, Ely HC, et al. Fluoretação da água no Brasil: distribuição regional e acurácia das informações sobre vigilância em municípios com mais de 50 mil habitantes. Cad Saude Publica. 2019;35(6):1-12. https://doi.org/10.1590/0102-311x00157918

6. Rouquayrol MZ, Moysés G, Santana EWP, Gondim APS. Epidemiologia, história natural, determinação social, prevenção de doenças e promoção da saúde. In: Rouquayrol MZ, Silva MGC. Epidemiologia e Saúde. 8. ed. Rio de Janeiro: Medbook; 2018.

7. Ministério da Saúde (BR). Divisão Nacional de Saúde Bucal. Levantamento Epidemiológico em Saúde Bucal: Brasil, zona urbana. Brasília: Ministério da Saúde; 1986.

8. Ministério da Saúde (BR). Projeto SB Brasil 2003: condições de saúde bucal da população brasileira 2002-2003: resultados principais. Brasília: Ministério da Saúde; 2004.

9. Roncalli AG. Projeto SB Brasil 2010: elemento estratégico na construção de um modelo de vigilância em saúde bucal. Cad Saude Publica 2010;26(3):428-9. https://doi.org/10.1590/S0102-311X2010000300001

10. Albano D, Galiano V, Basile M, Di Luca F, Gitto S, Messina C, et al. Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review. BMC Oral Health. 2024;24:274. https://doi.org/10.1186/s12903-024-04046-7

11. Anil S, Porwal P, Porwal A. Transforming dental caries diagnosis through artificial intelligencebased techniques. Cureus. 2023;15(7):e41694. https://doi.org/10.7759/cureus.41694

12. Nogueira SAN, Bastos LF, Costa ICC. Riscos Ocupacionais em Odontologia: Revisão da Literatura. UNOPAR Cient Cienc Biol Saude. 2010;12(3):11-20.

13. Mohammad-Rahimi H, Motamedian SR, Rohban MH, Krois J, Uribe SE, Mahmoudinia E, et al. Deep learning for caries detection: a systematic review. J Dent. 2022;122:104115. https://doi.org/10.1016/j.jdent.2022.104115

14. Qari AH, Hadi M, Alaidarous A, Aboalreesh A, Alqahtani M, Bamaga IK, et al. The accuracy of asynchronous tele-screening for detecting dental caries in patient-captured mobile photos: a pilot study. Saudi Dent J. 2024;36(1):105-11. https://doi.org/10.1016/j.sdentj.2023.10.006

15. Peters MDJ, Godfrey C, McInerney P, Munn Z, Trico AC, Khalil H. Chapter 11: Scoping reviews. In: Aromataris E, Munn Z, editors. JBI Manual for Evidence Synthesis. Adelaide: Joanna Brigs Institute; 2020. p. 407-52.

16. Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169:467-73. https://doi.org/10.7326/M18-0850

17. Xiao J, Luo JB, Ly-Mapes O, Wu TT, Dye T, Al Jallad N, et al. Assessing a smartphone app (AICaries) that uses artificial intelligence to detect dental caries in children and provides interactive oral health education: protocol for a design and usability testing study. JMIR Res Protoc. 2021;10(10):e32921. https://doi.org/10.2196/32921

18. Duong DL, Kabir MH, Kuo RF. Automated caries detection with smartphone color photography using machine learning. Health Inform J. 2021;27(2). https://doi.org/10.1177/14604582211027744

19. Thanh MTG, Toan NV, Ngoc VTN, Tra NT, Giap CN, Nguyen DM. Deep learning application in dental caries detection using intraoral photos taken by smartphones. Appl Sci-Basel. 2022;12(11):5504. https://doi.org/10.3390/app12115504

20. Estai M, Kanagasingam Y, Mehdizadeh M, Vignarajan J, Norman R, Huang B, et al. Mobile photographic screening for dental caries in children diagnostic performance compared to unaided visual dental examination. J Public Health Dent. 2022;82:166-75. https://doi.org/10.1111/jphd.12443

21. Al-Jallad N, Ly-Mapes O, Hao P, Ruan J, Ramesh A, Luo J, et al. Artificial Intelligence-powered smartphone application ALCaries improves at home dental caries screening in children: moderated and unmoderated usability test. PLOS Digit Health. 2022;1(6):e0000046. https://doi.org/10.1371/journal.pdig.0000046.

22. Ding B, Zhang Z, Liang Y, Wang W, Hao S, Meng Z, et al. Detection of dental caries in oral photographs taken by mobile phones based on the YOLOv3 algorithm. Ann Transl Med. 2021;9(21):1622. https://doi.org/10.21037/atm-21-4805

23. Zhang X, Liang Y, Li W, Liu C, Gu D, Sun WB, et al. Development and evaluation of deep learning for screening dental caries from oral photographs. Oral Dis. 2022;28(1):173-81. https://doi.org/10.1111/odi.13735

24. Tareq A, Faisal MI, Islam MS, Rafa NS, Chowdhury T, Ahmed S, et al. Visual diagnostics of dental caries through deep learning of non-standardised photographs using a hybrid yolo ensemble and transfer learning model. Int J Environ Res Public Health. 2023;20(7):5351. https://doi.org/10.3390/ijerph20075351

25. Berdouses ED, Koutsouri GD, Tripoliti EE, Matsopoulos GK, Oulis CJ, Fotiadis DI. A computer-aided automated methodology for the detection and classification of occlusal caries from photographic color images. Comput Biol Med. 2015;62:119-35. https://doi.org/10.1016/j.compbiomed.2015.04.016

26. Askar H, Krois J, Rohrer C, Mertens S, Elhennawy K, Ottolenghi L, et al. Detecting white spot lesions on dental photography using deep learning: a pilot study. J Dent. 2021;107:103615. https://doi.org/10.1016/j.jdent.2021.103615

27. Rashid U, Javid A, Khan AR, Liu L, Ahmed A, Khalid O, et al. A hybrid mask RCNN-based tool to localize dental cavities from real-time mixed photographic images. PeerJ Comput. Sci. 2022;18(8):e888. https://doi.org/10.7717/peerj-cs.888

28. Kühnisch J, Meyer O, Hesenius M, Hickel R, Gruhn V. Caries detection on intraoral images using artificial intelligence. J Dent Res. 2022;101(2):158-65. https://doi.org/10.1177/00220345211032524

29. Park EY, Cho H, Kang S, Jeong S, Kim EK. Caries detection with tooth surface segmentation on intraoral photographic images using deep learning. BMC Oral Health 2022;22:573. doi.org/10.1186/s12903-022-02589-1.

30. Xiong YS, Zhang HY, Zhou SY, Lu MH, Huang JH, Huang QT, et al. Simultaneous detection of dental caries and fissure sealant in intraoral photos by deep learning: a pilot study. BMC Oral Health. 2024;24:553. https://doi.org/10.1186/s12903-024-04254-1

31. Engels P, Meyer O, Schonewolf J, Schlickenrieder A, Hickel R, Hesenius M, et al. Automated detection of posterior restorations in permanent teeth using artificial intelligence on intraoral photographs. J Dent. 2022;121:104124. https://doi.org/10.1016/j.jdent.2022.104124

32. Al-Sayyed R, Taqateq AM, Al-Sayyed R, Suleiman D, Shukri S, Alhenawi E, et al. Employing CNN ensemble models in classifying dental caries using oral photographs. Int J Data Netw Sci. 2023;7(4):1535-50. https://doi.org/10.5267/j.ijdns.2023.8.009

33. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc 2020;92(4):807-12. https://doi.org/10.1016/j.gie.2020.06.040

34. Khanagar SB, Alfouzan K, Awawdeh M, Alkadi L, Albalawi F, Alfadley A. application and performance of artificial intelligence technology in detection, diagnosis and prediction of dental caries (DC): a systematic review. Diagnostics (Basel). 2022;12(5):1083. https://doi.org/10.3390/diagnostics12051083

35. Moharrami M, Farmer J, Singhal S, Watson E, Glogauer M, Johnson AEW, et al. Detecting dental caries on oral photographs using artificial intelligence: a systematic review. Oral Dis. 2024;30(4):1765-83. https://doi.org/10.1111/odi.14659

36. Willemink MJ, Noël PB. The evolution of image reconstruction for CT — from filtered back projection to artificial intelligence. Eur Radiol. 2019;29:2185-95. https://doi.org/10.1007/s00330-018-5810-7

37. Radha RC, Raghavendra BS, Subhash BV, Rajan J, Narasimhadhan AV. Machine learning techniques for periodontitis and dental caries detection: a narrative review. Int J Med Inform. 2023;178:105170. https://doi.org/10.1016/j.ijmedinf.2023.105170

38. Silveira DP, Artmann E. Acurácia em métodos de relacionamento probabilístico de bases de dados em saúde: revisão sistemática. Rev Saude Publica. 2009;43(5):875-82. https://doi.org/10.1590/S0034-89102009005000060

39. Yu ASO, Nardy A, Hirano HI, Oliveira JFA de, Ribeiro N de V, Grando N. Tomada de decisão nas organizações: o que muda com a Inteligência Artificial?. Estud Av. 2024;38(111):327-48. https://doi.org/10.1590/s0103-4014.202438111.017

40. Kitsios F, Kamariotou M, Syngelakis AI, Talias MA. Recent advances of artificial intelligence in healthcare: a systematic literature review. Appl Sci-Basel. 2023;13(13):7479. https://doi.org/10.3390/app13137479

41. Celuppi IC, Mohr ETB, Felisberto M, Rodrigues TS, Hammes JF, Cunha CL, et al. Ten years of the Citizen’s Electronic Health Record e-SUS Primary Healthcare: in search of an electronic Unified Health System. Rev Saude Publica. 2024;58:23. https://doi.org/10.11606/s1518-8787.2024058005770

Published

2025-12-15

Issue

Section

Original Articles

Funding data

How to Cite

Noro, L. R. A., & Céspedes, M. C. M. (2025). Artificial intelligence and oral photography: an approach to the epidemiology of dental caries. Revista De Saúde Pública, 59, 53. https://doi.org/10.11606/s15188787.2025059006910