Use of Artificial Intelligence for Caries Detection on Dental Radiographs
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Abstract
Background: Dental caries, being among the most frequent oral issues, calls for careful attention. Early checks help. Without them, things can slowly get worse, sometimes before anyone even notices. Standard ways of evaluating these cases still depend heavily on clinical skill, which isn’t always consistent. Mistakes in reading the signs aren’t rare. Now, with advancements in Artificial Intelligence, the field is shifting. It’s not just about detection anymore. AI enters the scene with promise bringing shifts in how diagnosis happens, care is planned, and cases are followed. Even the utilization of the time gets changed in clinics.
Aim: To spot interproximal caries in periapical x-rays using artificial intelligence (AI). Also, to compare the efficacy of AI findings against the readings done by radiologists.
Materials and Methods: A dataset of 400 periapical images was selected, representing 900 posterior teeth with suspected interproximal caries. A convolutional neural network (CNN) was trained using labelled periapical images for caries detection.
Results: The statistical analysis confirmed that the AI-model achieves statistically significant and clinically excellent performance in interproximal caries detection (McNemar’s test (χ² = 4.447, p-value=0.035)), and with near-perfect Cohen’s Kappa = 0.916. Model accuracy stood at 95.8%, with statistical comparison to radiologists showing no significant difference (p=0.089) and particularly notable results in advanced caries cases. The accuracy of the AI model was 95.8 percent and matched with the radiologists’ findings, with a p-value of 0.089. The results were especially strong in dealing with advanced caries cases. The AUC curve reached 0.957, and with a sensitivity of 97.4 percent.
Conclusion: AI showed solid accuracy and dependable results in finding dental caries on periapical images. Its performance closely reflected as that of experienced dental professionals’.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License.
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1. Selwitz RH, Ismail AI, Pitts NB. Dental caries. Lancet 2007, 369, 51-59. https://doi.org/10.1016/S0140-6736(07)60031-2
2. Young DA, Nový BB, Zeller GG, Hale R, Hart TC, Truelove EL, Ekstrand KR, Featherstone JD, Fontana M, Ismail A, Kuehne J. The American Dental Association caries classification system for clinical practice: a report of the American Dental Association Council on Scientific Affairs. J Am Dent Assoc. 2015;146(2):79-86. https://doi.org/10.1016/j.adaj.2014.11.018
3. GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018; 392:1789-1858. https://doi.org/10.1016/S0140-6736(18)32279-7
4. Rathee M, Sapra A. Dental Caries. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2024.
5. Carey CM. Focus on fluorides: Update on the use of fluoride for the prevention of dental caries. J Evid Based Dent Pract. 2014; 14:95-102. https://doi.org/10.1016/j.jebdp.2014.02.004
6. Featherstone JD. Remineralization, the natural caries repair process-The need for new approaches. Adv Dent Res. 2009; 21: 4-7. https://doi.org/10.1177/0895937409335590
7. Spaveras A, Karkazi F, Antoniadou M. Caries detection with laser fluorescence devices. Limitations of their use. Stoma Educ J. 2017;4:46-53.
8. Zandona AF, Zero DT. Diagnostic tools for early caries detection. J Am Dent Assoc. 2006; 137: 1675-1684. https://doi.org/10.14219/jada.archive.2006.0113
9. Pontes LR, Lara JS, Novaes TF, Freitas JG, Gimenez T, Moro BL, Maia HC, Imparato JC, Braga MM, Raggio DP, Mendes FM. Negligible therapeutic impact, false-positives, overdiagnosis and lead-time are the reasons why radiographs bring more harm than benefits in the caries diagnosis of preschool children. BMC Oral Health. 2021;21:1-7. https://doi.org/10.1186/s12903-021-01528-w
10. Pretty IA. Caries detection and diagnosis: Novel technologies. J Dent. 2006;34:727-739. https://doi.org/10.1016/j.jdent.2006.06.001
11. Pontes LR, Novaes TF, Moro BL, Braga MM, Mendes FM. Clinical performance of fluorescence-based methods for detection of occlusal caries lesions in primary teeth. Braz Oral Res. 2017;31:e91. https://doi.org/10.1590/1807-3107bor-2017.vol31.0091
12. Morita INH, Nonoyama K, Robinson C. DIAGNOdent values of occlusal surface in the first permanent molar in vivo (abstract 45)-49th ORCA Congress. Caries Res. 2002; 36:188. https://doi.org/10.1159/000059333
13. Sheehy EC, Brailsford SR, Kidd EA, Beighton D, Zoitopoulos L. Comparison between visual examination and a laser fluorescence system for in vivo diagnosis of occlusal caries. Caries Res. 2001;35(6):421-6. https://doi.org/10.1159/000047485
14. Cochrane Oral Health Group, Walsh T, Macey R, Riley P, Glenny AM, Schwendicke F, Worthington HV, Clarkson JE, Ricketts D, Su TL, Sengupta A. Imaging modalities to inform the detection and diagnosis of early caries. Cochrane Database of Systematic Reviews. 1996;2021(12). CD014545. https://doi.org/10.1002/14651858.CD014545
15. Hilton TJ, Ferracane JL, Broome JC, Santos JD, Summitt JB. Fundamentals of Operative Dentistry: A Contemporary Approach. 4th Edition, Quintessence Publishing, 2013.
16. Khan HA, Haider MA, Ansari HA, Ishaq H, Kiyani A, Sohail K, Muhammad M, Khurram SA. Automated feature detection in dental periapical radiographs by using deep learning. Oral Surg Oral Med Oral Pathol Oral Radiol. 2021;131(6):711-720. https://doi.org/10.1016/j.oooo.2020.08.024
17. Langlais RP, Skoczylas LJ, Prihoda TJ, Langland OE, Schiff T. Interpretation of bitewing radiographs: application of the kappa statistic to determine rater agreements. Oral Surg Oral Med Oral Pathol. 1987;64(6):751-6. https://doi.org/10.1016/0030-4220(87)90181-2
18. Bailit HL, Reisine ST, Damuth RL, Richards NP. The validity of the radiographic method in the pretreatment review of dental claims. J Pub Health Dent. 1980;40(1):26-38. https://doi.org/10.1111/j.1752-7325.1980.tb01846.x
19. Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofac Radiol. 2020;49(1):20190107. https://doi.org/10.1259/dmfr.20190107
20. Mazurowski MA. Artificial intelligence in radiology: Some ethical considerations for radiologists and algorithm developers. Acad Radiol. 2020; 27:127–129. https://doi.org/10.1016/j.acra.2019.04.024
21. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJ. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500-10. https://doi.org/10.1038/s41568-018-0016-5
22. Hwang JJ, Jung YH, Cho BH, Heo MS. An overview of deep learning in the field of dentistry. Imaging Sci Dent. 2019;49(1):1. https://doi.org/10.5624/isd.2019.49.1.1
23. Bayrakdar IS, Orhan K, Akarsu S, Çelik Ö, Atasoy S, Pekince A, Yasa Y, Bilgir E, Sağlam H, Aslan AF, Odabaş A. Deep-learning approach for caries detection and segmentation on dental bitewing radiographs. Oral Radiol. 2022;38:468-479. https://doi.org/10.1007/s11282-021-00577-9
24. Li Z, Liu F, Yang W, Peng S, Zhou J. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst. 2021;33(12):6999-7019. https://doi.org/10.1109/TNNLS.2021.3084827
25. Casalegno F, Newton T, Daher R, Abdelaziz M, Lodi-Rizzini A, Schürmann F, Krejci I, Markram H. Caries detection with near-infrared transillumination using deep learning. J Dent Res. 2019;98(11):1227-33. https://doi.org/10.1177/0022034519871884
26. Gomez J. Detection and diagnosis of the early caries lesion. BMC Oral Health 2015; 15 (Suppl. S1), S3. https://doi.org/10.1186/1472-6831-15-S1-S3
27. Chen H, Li H, Zhao Y, Zhao J, Wang Y. Dental disease detection on periapical radiographs based on deep convolutional neural networks. Int J Comput Assist Radiol Surg. 2021;16:649-661. https://doi.org/10.1007/s11548-021-02319-y
28. Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018;77:106–111. https://doi.org/10.1016/j.jdent.2018.07.015
29. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. InProceedings of the IEEE conference on computer vision and pattern recognition 2016 (pp. 770-778). https://doi.org/10.1109/CVPR.2016.90
30. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9(7):676-82. https://doi.org/10.1038/nmeth.2019
31. Gómez-de-Mariscal, E., García-López-de-Haro, C., Ouyang, W. et al. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. Nat Methods. 2021;18:1192–1195. https://doi.org/10.1038/s41592-021-01262-9
32. Kishimoto T, Goto T, Matsuda T, Iwawaki Y, Ichikawa T. Application of artificial intelligence in the dental field: A literature review. J Prosthodon Res. 2022;66(1):19-28. https://doi.org/10.2186/jpr.JPR_D_20_00139
33. 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
34. Schwendicke F, Golla T, Dreher M, Krois J. Convolutional neural networks for dental image diagnostics: A scoping review. J Dent. 2019;91:103226. https://doi.org/10.1016/j.jdent.2019.103226
35. Rao RS, Shivanna DB, Lakshminarayana S, Mahadevpur KS, Alhazmi YA, Bakri MM, 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. J Personal Med. 2022;12(8):1220. https://doi.org/10.3390/jpm12081220
36. Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T et al., Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol. 2019;35:301-7. https://doi.org/10.1007/s11282-018-0363-7
37. Celik ME. Deep learning based detection tool for impacted mandibular third molar teeth. Diagnostics 2022;12:942. https://doi.org/10.3390/diagnostics12040942
38. Chuo Y, Lin WM, Chen TY, Chan ML, Chang YS, Lin YR, et al., A high-accuracy detection system: Based on transfer learning for apical lesions on periapical radiograph. Bioengineering. 2022;9(12):777. https://doi.org/10.3390/bioengineering9120777
39. Chen YC, Chen MY, Chen TY, Chan ML, Huang YY, Liu YL, 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
40. Falcao A, Bullón P. A review of the influence of periodontal treatment in systemic diseases. Periodontology 2000. 2019;79(1):117-28. https://doi.org/10.1111/prd.12249
41. Hung KF, Ai QYH, Wong LM, Yeung AWK, Li DTS, Leung YY. Current applications of deep learning and radiomics on CT and CBCT for maxillofacial diseases. Diagnostics 2023;13(1):110. https://doi.org/10.3390/diagnostics13010110
42. Tsoromokos N, Parinussa S, Claessen F, Moin DA, Loos BG. Estimation of alveolar bone loss in periodontitis using machine learning. Int Dent J. 2022;72(5):621–7. https://doi.org/10.1016/j.identj.2022.02.009
43. Lam DW, Chau DR. Biomimetic dental prostheses designed by artificial intelligence versus CAD software. Int Dent J. 2023;73:S32–3. https://doi.org/10.1016/j.identj.2023.07.298
44. Gao H, Xiao J, Yin Y, Liu T, Shi J. A mutually supervised graph attention network for few-shot segmentation: the perspective of fully utilizing limited samples. IEEE Trans Neural Netw Learn Syst. 2022:1–13.
45. Cao Z, Xu L, Chen DZ, Gao H, Wu J. A robust shape-aware rib fracture detection and segmentation framework with contrastive learning. IEEE Trans Multimedia 2023;25:1584–91. https://doi.org/10.1109/TMM.2023.3263074
46. Chen T, Zheng W, Hu H, Luo C, Chen J, Yuan C, et al. A corresponding region fusion framework for multi-modal cervical lesion detection. IEEE/ACM Trans Comput Biol Bioinform. 2022;21(4):959-70. https://doi.org/10.1109/TCBB.2022.3178725
47. Chen M, Luo X, Shen H, Huang Z, Peng Q, Yuan Y. A Chinese nested named entity recognition approach using sequence labeling. Int J Web Inf Syst. 2023;19(1):42–60. https://doi.org/10.1108/IJWIS-04-2023-0070
48. Payghode V, Goyal A, Bhan A, Iyer SS, Dubey AK. Object detection and activity recognition in video surveillance using neural networks. Int J Web Info Syst. 2023;19(3/4):123-38. https://doi.org/10.1108/IJWIS-01-2023-0006
49. 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
50. Vinayahalingam S, Kempers S, Limon L, Deibel D, Maal T, Hanisch M, et al. Classification of caries in third molars on panoramic radiographs using deep learning. Sci Rep 2021;11(1):12609. https://doi.org/10.1038/s41598-021-92121-2
51. Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, et al. Detecting caries lesions of different radiographic extension on bitewings using deep learning. J Dent 2020;100:103425. https://doi.org/10.1016/j.jdent.2020.103425
52. Kermany, D. S. et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172:1122-1131. https://doi.org/10.1016/j.cell.2018.02.010
53. Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. 2018;48(2):114. https://doi.org/10.5051/jpis.2018.48.2.114
54. Srivastava MM, Kumar P, Pradhan L, Varadarajan S. Detection of tooth caries in bitewing radiographs using deep learning. Workshop for ML in Health, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 2017.
55. Devito KL, de Souza Barbosa F, Felippe Filho WN. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. Endodontol. 2008;106(6):879-84. https://doi.org/10.1016/j.tripleo.2008.03.002
56. Hung M, Voss MW, Rosales MN, Li W, Su W, Xu J, et al. Application of machine learning for diagnostic prediction of root caries. Gerodontol. 2019;36(4):395-404. https://doi.org/10.1111/ger.12432
57. Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F. Deep learning for the radiographic detection of apical lesions. J Endodon. 2019;45(7):917-22. https://doi.org/10.1016/j.joen.2019.03.016
58. Pang L, Wang K, Tao Y, Zhi Q, Zhang J, Lin H. A new model for caries risk prediction in teenagers using a machine learning algorithm based on environmental and genetic factors. Front Genet. 2021;12:636867. https://doi.org/10.3389/fgene.2021.636867
59. Lee S, Oh S, Jo J, Kang S, Shin Y, Park J. Deep learning for early dental caries detection in bitewing radiographs. Sci Rep. 2021;11:16807. https://doi.org/10.1038/s41598-021-96368-7.
60. Chen X, Guo J, Ye J, Zhang M, Liang Y. Detection of proximal caries lesions on bitewing radiographs using deep learning method. Caries Res. 2022;56:455–63. https://doi.org/10.1159/000527418.