de Brito DHS, dos Santos TGFT, Costa SGA, dos Santos AS, da Silva ILB, da Silva NRC, Fernandes BJT, Niederman R, Mota CCBO, da Silveira MMF, Heimer MV, Rosenblatt A. Accuracy in diagnosing caries in young permanent molars using interproximal radiographic imaging and validation by artificial intelligence. J Clin Exp Dent. 2025;17(6):e648-55.

doi:10.4317/jced.62396

___

References

1. Pitts NB, Baez RJ, Diaz-Guillory C, Donly KJ, Feldens CA, Twetman S, et al. Early childhood caries: IAPD Bangkok declaration. Journal of dentistry for children (Chicago, Ill.) 2019;86(2):72. PMid:31395110

2. Keenan JR, Keenan AV. Accuracy of dental radiographs for caries detection. Evidence-based dentistry. 2016;17(2):43-43. https://doi.org/10.1038/sj.ebd.6401166 PMid:27339235

3. Braga MM, Mendes FM, Ekstrand KR. Detection activity assessment and diagnosis of dental caries lesions. Dent Clin North Am. 2010;54(3):479-93. https://doi.org/10.1016/j.cden.2010.03.006 PMid:20630191

4. Novaes TF, Matos R, Raggio DP, Braga MM, Mendes FM. Children's discomfort in assessments using different methods for approximal caries detection. Braz Oral Res. 2012;26(2):93-9. https://doi.org/10.1590/S1806-83242012000200002 PMid:22473342

5. Getova B, Pavlevska M, Carceva-Salja S. Prevalence of first permanent molar caries among children at age 12 in Republic of Macedonia. Research Journal of Pharmaceutical, Biological and Chemical Sciences. 2018;ISSN: 0975-8585.

6. Temur KT, Önsüren AS. Investigating permanent first molars of a Turkish pediatric sample in the eastern Mediterranean region: A radiographic study. Journal of Experimental and Clinical Medicine. 2022;39(3):611-15. https://doi.org/10.52142/omujecm.39.3.3

7. Botelho K, Carvalho L, Maciel R, Franca CD, Colares V. Clinical condition of the first permanent molars: of children aged 6 to 8 years. Clinical-Scientific Dentistry. 2011;10(2):167-171.

8. Gondim RS, Abreu LG. Study of the accuracy of fluorescence induction devices and the radiographic method for the diagnosis of caries lesions in primary teeth: a systematic review and meta-analysis. Archives in Dentistr 2022;58:63-86.

9. Soares GG, Souza PR, de Carvalho Purger FP, de Vasconcellos AB, Ribeiro AA. Caries detection methods. Brazilian Journal of Dentistry. 2012;69(1): 84.

10. Purger F, Oliveira P, Vasconcellos A. Relative importance of radiographs in diagnosing primary molars proximal caries. Journal Dental Research. 2011;90.

11. World Health Organization. Oral health surveys: basic methods. World Health Organization. 2013.

12. Martins AMEBL, Haikal DSA, Santos Neto PE, et al. Calibration of examiners epidemiological survey of oral health status of populations in Montes Claros, MG. Unimontes Cient. 2012;14(1):43-56.

13. Rodrigues ACC, Quispe RA, Capelozza ALA, Rubira CMF, Buaes AMG, da Silva Santos PS. The importance of calibration for radiographic evaluation of dental anomalies in cancer patients. IC Cesumar. 2017;19(2):171-77. https://doi.org/10.17765/1518-1243.2017v19n2p171-177

14. Slimani A, Terrer E, Manton DJ, Tassery H. Carious lesion detection technologies: Factual clinical approaches. British Dental Journal. 2020;229(7):432-442. https://doi.org/10.1038/s41415-020-2116-3 PMid:33037363

15. Kaur J, Singh W. Tools, techniques, datasets and application areas for object detection in an image: a review. Multimedia Tools and Applications. Springer. 2022;81(27):38297-38351. https://doi.org/10.1007/s11042-022-13153-y PMid:35493415 PMCid:PMC9033309

16. Wiley V, Lucas T. Computer vision and image processing: a paper review. International Journal of Artificial Intelligence Research. 2018;2(1):29-36. https://doi.org/10.29099/ijair.v2i1.42

17. Kishimoto T, Goto T, Matsuda T, Iwawaki Y, Ichikawa T. Application of artificial intelligence in the dental field: A literature review. Journal of Prosthodontic Research. 2022;66(1):19-28. https://doi.org/10.2186/jpr.JPR_D_20_00139 PMid:33441504

18. Suhail Y, Upadhyay M, Chhibber A, Kshitiz. Machine learning for the diagnosis of orthodontic extractions: a computational analysis using ensemble learning. Bioengineering. 2020;7(2):55. https://doi.org/10.3390/bioengineering7020055 PMid:32545428 PMCid:PMC7355468

19. Schwendicke F, Golla T, Dreher M, Krois J. Convolutional neural networks for dental image diagnostics: A scoping review. Journal of dentistry. 2019;91:103226. https://doi.org/10.1016/j.jdent.2019.103226 PMid:31704386

20. Rao RS, Shivanna DB, Lakshminarayana S, Mahadevpur KS, Alhazmi YA, Testarelli L, et al. Ensemble deep-learning-based prognostic and prediction for recurrence of sporadic odontogenic keratocysts on hematoxylin and eosin stained pathological images of incisional biopsies. Journal of Personalized Medicine 2022;12(8):1220. https://doi.org/10.3390/jpm12081220 PMid:35893314 PMCid:PMC9332803

21. Chen YC, Chen MY, Chen TY, Chan ML, Huang YY, Abu PAR, et al. Improving dental implant outcomes: CNN-based system accurately measures degree of peri-implantitis damage on periapical film. Bioengineering. 2023;10(6):640. https://doi.org/10.3390/bioengineering10060640 PMid:37370571 PMCid:PMC10294869

22. Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2016;779-788. https://doi.org/10.1109/CVPR.2016.91

23. ULTRALYTICS. yolov8. [S.l.]: GitHub, 2023. Available at: https://github.com/ultralytics/ultralytics. Accessed on: 20 de janeiro de 2024.

24. De Ullibarri Galparsoro L, Pita Fernández S. Agreement measures: the Kappa index. Primary Care Journal. 1999;6:169-171.

25. Assaf AV, Zanin L, Meneghim MDC, Pereira AC, Ambrosano GMB. Comparison between reproducibility measures for calibration in epidemiological surveys of dental caries. Public Health Journal. 2006;22:1901-1907. PMid:16917587

26. Dias da Silva PR, Martins Marques M, Steagall Jr W, Medeiros Mendes F, Lascala CA. Accuracy of direct digital radiography for detecting occlusal caries in primary teeth compared with conventional radiography and visual inspection: an in vitro study. Dentomaxillofacial Radiology. 2010;39(6):362-67. https://doi.org/10.1259/dmfr/22865872 PMid:20729186 PMCid:PMC3520238

27. Signori C, Laske M, Mendes FM, Huysmans MCD, Cenci MS, Opdam NJ. Decision-making of general practitioners on interventions at restorations based on bitewing radiographs. Journal of Dentistry. 2018;76:109-16. https://doi.org/10.1016/j.jdent.2018.07.003 PMid:30004002

28. Caceda JH, Jiang S, Calderon V, Villavicencio-Caparo E. Sensitivity and specificity of the ICDAS II system and bitewing radiographs for detecting occlusal caries using the Spectra™ caries detection system as the reference test in children. BMC Oral Health. 2023;23(1):896. https://doi.org/10.1186/s12903-023-03615-6 PMid:37986066 PMCid:PMC10662650

29. Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of dentistry. 2018;77:106-111. https://doi.org/10.1016/j.jdent.2018.07.015 PMid:30056118

30. Ragab MG, Abdulkader SJ, Muneer A, Alqushaibi A, Sumiea EH, Alhussian H, et al. A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023). IEEE Access. 2024. https://doi.org/10.1109/ACCESS.2024.3386826