Skip to main content

Advertisement

Log in

Dental disease detection on periapical radiographs based on deep convolutional neural networks

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Objectives

It is with a great prospect to develop an auxiliary diagnosis system for dental periapical radiographs based on deep convolutional neural networks (CNNs), and the indications and performances should be investigated. The aim of this study is to train CNNs for lesion detections on dental periapical radiographs, to evaluate performances across disease categories, severity levels, and train strategies.

Methods

Deep CNNs with region proposal techniques were constructed for disease detections on clinical dental periapical radiographs, including decay, periapical periodontitis, and periodontitis, leveled as mild, moderate, and severe. Four strategies were carried out to train corresponding networks with all disease and level categories (baseline), all disease categories (Net A), each disease category (Net B), and each level category (Net C) and validated by a fivefold cross-validation method afterward. Metrics, including intersection over union (IoU), precision, recall, and average precision (AP), were compared across diseases, severity levels, and train strategies by analysis of variance.

Results

Lesions were detected with precision and recall generally between 0.5 and 0.6 on each kind of disease. The influence of train strategy, disease category, and severity level were all statistically significant on performances (P < .001). Decay and periapical periodontitis lesions were detected with precision, recall, and AP values less than 0.25 for mild level, while 0.2–0.3 for moderate level and 0.5–0.6 for severe level. Net A performed similar to baseline (P > 0.05 for IoU, precision, and recall), while Net B and Net C performed slightly better than baseline under certain circumstances (P < 0.05), but Net C failed to predict mild decay.

Conclusions

The deep CNNs are able to detect diseases on clinical dental periapical radiographs. This study reveals that the CNNs prefer to detect lesions with severe levels, and it is better to train the CNNs with customized strategy for each disease.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. American Dental Association Council on Scientific Affairs (2006) The use of dental radiographs: update and recommendations. J Am Dent Assoc 137(9):1304–1312

    Article  Google Scholar 

  2. Rohlin M, Kullendorff B, Ahlqwist M, Henrikson CO, Hollender L, Stenström B (1989) Comparison between panoramic and periapical radiography in the diagnosis of periapical bone lesions. Dentomaxillofac Radiol 18(4):151–155

    Article  CAS  Google Scholar 

  3. Douglass CW, Valachovic RW, Wijesinha A, Chauncey HH, Kapur KK, Mcneil BJ (1986) Clinical efficacy of dental radiography in the detection of dental caries and periodontal diseases. Oral Surg Oral Med Oral Pathol 62(3):330–339

    Article  CAS  Google Scholar 

  4. Gupta A, Devi P, Srivastava R, Jyoti B (2014) Intra oral periapical radiography-basics yet intrigue: a review. Bangladesh J Dent Res Educ 4(2):83–87

    Article  Google Scholar 

  5. Kaffe I, Gratt BM (1988) Variations in the radiographic interpretation of the periapical dental region. J Endod 14(7):330–335

    Article  CAS  Google Scholar 

  6. Valachovic RW, Douglass CW, Berkey CS, Mcneil BJ, Chauncey HH (1986) Examiner reliability in dental radiography. J Dent Res 65(3):432–436

    Article  CAS  Google Scholar 

  7. Sirotheau Corrêa Pontes F, Paiva Fonseca F, Souza De Jesus A, Garcia Alves AC, Marques Araújo L, Silva Do Nascimento L, Rebelo Pontes HA (2014) Nonendodontic lesions misdiagnosed as apical periodontitis lesions: series of case reports and review of literature. J Endod 40(1):16–27

    Article  Google Scholar 

  8. Jain AK, Chen H (2004) Matching of dental X-ray images for human identification. Pattern Recogn 37(7):1519–1532

    Article  Google Scholar 

  9. Shah S, Abaza A, Ross A, Ammar H (2006) Automatic tooth segmentation using active contour without edges. In: IEEE biometric consortium conference, biometrics symposium: special session on research, pp 1–6

  10. Nomir O, Abdel-Mottaleb M (2005) A system for human identification from X-ray dental radiographs. Pattern Recogn 38(8):1295–1305

    Article  Google Scholar 

  11. Zhou J, Abdel-Mottaleb M (2005) A content-based system for human identification based on bitewing dental X-ray images. Pattern Recogn 38(11):2132–2142

    Article  Google Scholar 

  12. Razali MRM, Ismail W, Ahmad NS, Bahari M, Zaki ZM, Radman A (2017) An adaptive thresholding method for segmenting dental X-ray images. J Telecommun Electron Comput Eng (JTEC) 9(4):1–5

    Google Scholar 

  13. Lin PL, Lai YH, Huang PW (2010) An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information. Pattern Recogn 43(4):1380–1392

    Article  Google Scholar 

  14. Li S, Fevens T, Krzyżak A, Jin C, Li S (2007) Semi-automatic computer aided lesion detection in dental X-rays using variational level set. Pattern Recogn 40(10):2861–2873

    Article  Google Scholar 

  15. Rad AEAR (2018) Automatic computer-aided caries detection from dental x-ray images using intelligent level set. Multimed Tools Appl 77(21):28843–28862

    Article  Google Scholar 

  16. Said EH, Nassar DEM, Fahmy G, Ammar HH (2006) Teeth segmentation in digitized dental X-ray films using mathematical morphology. IEEE Trans Inf Forensics Secur 1(2):178–189

    Article  Google Scholar 

  17. Nomir O, Abdel-Mottaleb M (2007) Human identification from dental X-ray images based on the shape and appearance of the teeth. IEEE Trans Inf Forensics Secur 2(2):188–197

    Article  Google Scholar 

  18. Nomir O, Abdel-Mottaleb M (2008) Hierarchical contour matching for dental X-ray radiographs. Pattern Recogn 41(1):130–138

    Article  Google Scholar 

  19. Lin P, Lai Y, Huang P (2012) Dental biometrics: human identification based on teeth and dental works in bitewing radiographs. Pattern Recogn 45(3):934–946

    Article  Google Scholar 

  20. Rad AE, Shafry M, Rahim M, Norouzi A (2013) Digital dental X-Ray image segmentation and feature extraction. Telkomnika Indones J Electr Eng 11(6):3109–3114

    Google Scholar 

  21. Lin PL, Huang PY, Huang PW, Hsu HC, Chen CC (2014) Teeth segmentation of dental periapical radiographs based on local singularity analysis. Comput Methods Programs Biomed 113(2):433–445

    Article  CAS  Google Scholar 

  22. Son LH, Tuan TM (2016) A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation. Expert Syst Appl 46(Supplement C):380–393

    Article  Google Scholar 

  23. Tuan TM et al (2017) Dental segmentation from X-ray images using semi-supervised fuzzy clustering with spatial constraints. Eng Appl Artif Intell 59:186–195

    Article  Google Scholar 

  24. Ali M, Khan M, Tung NT et al (2018) Segmentation of dental X-ray images in medical imaging using neutrosophic orthogonal matrices. Expert Syst Appl 91:434–441

    Article  Google Scholar 

  25. Li S, Fevens T, Krzyżak A, Li S (2006) Automatic clinical image segmentation using pathological modeling, PCA and SVM. Eng Appl Artif Intell 19(4):403–410

    Article  Google Scholar 

  26. Yu Y, Li Y, Li Y, Wang J, Lin D, Ye W (2006) Tooth decay diagnosis using back propagation neural network. In: IEEE international conference on machine learning and cybernetics, pp 3956–3959

  27. El-Bakry HM, Mastorakis N (2008) An effective method for detecting dental diseases by using fast neural networks. WSEAS Trans Biol Biomed 11:293–301

    Google Scholar 

  28. Tumbelaka B, Oscandar F, Baihaki F, Sitam S, Rukmo M (2014) Identification of pulpitis at dental X-ray periapical radiography based on edge detection, texture description and artificial neural networks. Saudi Endod J 4(3):115–121

    Article  Google Scholar 

  29. Srivastava MM, Kumar P, Pradhan L, Varadarajan S (2017) Detection of tooth caries in bitewing radiographs using deep learning. arXiv preprint arXiv:1711.07312.

  30. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  31. Al Kheraif AA, Wahba AA, Fouad H (2019) Detection of dental diseases from radiographic 2d dental image using hybrid graph-cut technique and convolutional neural network. Measurement 146:333–342

    Article  Google Scholar 

  32. Lee J, Kim D, Jeong S, Choi S (2018) Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 77:106–111

    Article  Google Scholar 

  33. Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F (2019) Deep learning for the radiographic detection of apical lesions. J Endod 45(7):917–922

    Article  Google Scholar 

  34. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587

  35. Girshick R (2015) Fast r-cnn. In: Proceedings IEEE international conference on computer vision, pp 1440–1448

  36. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99

  37. Laishram A, Thongam K (2020) Detection and classification of dental pathologies using faster-RCNN in orthopantomogram radiography image. Int Conf Signal Process Integr Netw (SPIN) 7:423–428

    Google Scholar 

  38. Huang J, Rathod V, Sun C, Zhu M, Korattikara A, Fathi A, Fischer I, Wojna Z, Song Y, Guadarrama S et al (2017) Speed/accuracy trade-offs for modern convolutional object detectors. In: Proceedings IEEE conference on computer vision and pattern recognition, pp 7310–7311

  39. Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2010) The Pascal visual object classes (VOC) challenge. Int J Comput Vision 88(2):303–338

    Article  Google Scholar 

  40. Davis J, Goadrich M (2006) The Relationship between precision–recall and ROC curves. In: Proceedings 23rd international conference on machine learning, pp 233–240

Download references

Funding

This study was funded by the National Natural Science Foundation of China (No. 51705006), Program for New Clinical Techniques and Therapies of Peking University School and Hospital of Stomatology (No. PKUSSNCT-19A08), and open fund of Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials (KF2020-04).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Hu Chen or Yong Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

For this type of study, formal consent is not required.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, H., Li, H., Zhao, Y. et al. Dental disease detection on periapical radiographs based on deep convolutional neural networks. Int J CARS 16, 649–661 (2021). https://doi.org/10.1007/s11548-021-02319-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-021-02319-y

Keywords

Navigation