Skip to main content

Advertisement

SpringerLink
Log in

A Systematic Review on Caries Detection, Classification, and Segmentation from X-Ray Images: Methods, Datasets, Evaluation, and Open Opportunities

  • Published:
Journal of Imaging Informatics in Medicine Aims and scope Submit manuscript

Abstract

Dental caries occurs from the interaction between oral bacteria and sugars, generating acids that damage teeth over time. The importance of X-ray images for detecting oral problems is undeniable in dentistry. With technological advances, it is feasible to identify these lesions using techniques such as deep learning, machine learning, and image processing. Therefore, the survey and systematization of these methods are essential to determining the main computational approaches for identifying caries in X-ray images. In this systematic review, we investigated the primary computational methods used for classifying, detecting, and segmenting caries in X-ray images. Following the PRISMA methodology, we selected relevant studies and analyzed their methods, strengths, limitations, imaging modalities, evaluation metrics, datasets, and classification techniques. The review encompassed 42 studies retrieved from the Science Direct, IEEExplore, ACM Digital, and PubMed databases from the Computer Science and Health areas. The results indicate that 12% of the included articles utilized public datasets, with deep learning being the predominant approach, accounting for 69% of the studies. The majority of these studies (76%) focused on classifying dental caries, either in binary or multiclass classification. Panoramic imaging was the most commonly used radiographic modality, representing 29% of the cases studied. Overall, our systematic review provides a comprehensive overview of the computational methods employed in identifying caries in radiographic images and highlights trends, patterns, and challenges in this research field.

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

Access this article

Log in via an institution

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data Availability

The data in this systematic review, like the search protocol, are available upon reasonable request to the corresponding author.

Notes

  1. http://www.sciencedirect.com

  2. http://dl.acm.org

  3. https://www.ncbi.nlm.nih.gov/pubmed

  4. https://ieeexplore.ieee.org/

References

  1. Rathee, M. & Sapra, A. in Dental Caries (StatPearls Publishing, Treasure Island (FL), 2023).

    Google Scholar 

  2. Pan, Y.-C., Chan, H.-L., Kong, X., Hadjiiski, L. M. & Kripfgans, O. D. Multi-class deep learning segmentation and automated measurements in periodontal sonograms of a porcine model. Dentomaxillofacial Radiology 51, 20210363 (2022).

    Article  PubMed  Google Scholar 

  3. Rana, A. et al. Automated segmentation of gingival diseases from oral images. IEEE Healthcare Innovations 144–147 (2017).

  4. Rimi, I. F. et al. Machine learning techniques for dental disease prediction. Iran J Comput Sci 5, 187–195 (2022).

    Article  Google Scholar 

  5. Felemban, O. M., Loo, C. Y. & Ramesh, A. Accuracy of cone-beam computed tomography and extraoral bitewings compared to intraoral bitewings in detection of interproximal caries. J Contemp Dent Pract 21, 1361–1367 (2020).

    PubMed  Google Scholar 

  6. Musri, N., Christie, B., Ichwan, S. J. A. & Cahyanto, A. Deep learning convolutional neural network algorithms for the early detection and diagnosis of dental caries on periapical radiographs: A systematic review. Imaging Sci Dent 51, 237–242 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Prados-Privado, M., García Villalón, J., Martínez-Martínez, C. H., Ivorra, C. & Prados-Frutos, J. C. Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review. J Clin Med 9, 3579 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Mohammad-Rahimi, H. et al. Deep learning for caries detection: A systematic review. J Dent 122, 104115 (2022).

    Article  PubMed  Google Scholar 

  9. Reyes, L. T., Knorst, J. K., Ortiz, F. R. & Ardenghi, T. M. Machine Learning in the Diagnosis and Prognostic Prediction of Dental Caries: A Systematic Review. Caries Res 56, 161–170 (2022).

    Article  PubMed  Google Scholar 

  10. Morris, A. L. & Tadi, P. in Anatomy, Head and Neck, Teeth (StatPearls Publishing, Treasure Island (FL), 2022).

    Google Scholar 

  11. Marsh, P. D. Dental plaque as a microbial biofilm. Caries Res 38, 204–211 (2004).

    Article  CAS  PubMed  Google Scholar 

  12. Featherstone, J. D. The science and practice of caries prevention. J Am Dent Assoc 131, 887–899 (2000).

    Article  CAS  PubMed  Google Scholar 

  13. Braga, M. M., Mendes, F. M. & Ekstrand, K. R. Detection Activity Assessment and Diagnosis of Dental Caries Lesions. Dental Clinics 54, 479–493 (2010).

    PubMed  Google Scholar 

  14. White, S. C. & Pharoah, M. J. Oral radiology: Principles and interpretation (Elsevier Health Sciences, 2014).

  15. Setzer, F. C., Hinckley, N., Kohli, M. R. & Karabucak, B. A Survey of Cone-beam Computed Tomographic Use among Endodontic Practitioners in the United States. J Endod 43, 699–704 (2017).

    Article  PubMed  Google Scholar 

  16. Wang, S. & Ford, B. Imaging in Oral and Maxillofacial Surgery. Dental Clinics 65, 487–507 (2021).

    PubMed  Google Scholar 

  17. Ikeuchi, K. Computer vision: A reference guide (Springer, 2021).

  18. Guo, L. & Wenyuan, S. Salivary biomarkers for caries risk assessment. Journal of the California Dental Association 41, 107–118 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ n71 (2021).

  20. Bhan, A., Goyal, A., Harsh, Chauhan, N. & Wang, C.-W. Feature Line Profile Based Automatic Detection of Dental Caries in Bitewing Radiography. ICMETE 635–640 (2016).

  21. Naebi, M. et al. Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm. Int J Dent 2016, 3264545 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Sornam, M. & Prabhakaran, M. A new linear adaptive swarm intelligence approach using back propagation neural network for dental caries classification. ICPCSI 2698–2703 (2017).

  23. Singh, P. & Sehgal, P. Automated caries detection based on Radon transformation and DCT. ICCCNT 1–6 (2017).

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

    Article  PubMed  Google Scholar 

  25. Patil, S., Kulkarni, V. & Bhise, A. Algorithmic analysis for dental caries detection using an adaptive neural network architecture. Heliyon 5, e01579 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Datta, S., Chaki, N. & Modak, B. A Novel Technique to Detect Caries Lesion Using Isophote Concepts. IRBM 40, 174–182 (2019).

    Article  Google Scholar 

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

  28. Verma, D., Puri, S., Prabhu, S. & Smriti, K. Anomaly detection in panoramic dental x-rays using a hybrid Deep Learning and Machine Learning approach. TENCON 263–268 (2020).

  29. Lakshmi, M. M. & Chitra, P. Classification of Dental Cavities from X-ray images using Deep CNN algorithm. ICOEI 774–779 (2020).

  30. Geetha, V., Aprameya, K. S. & Hinduja, D. M. Dental caries diagnosis in digital radiographs using back-propagation neural network. Health Inf Sci Syst 8, 8 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Jusman, Y. et al. Comparison of Dental Caries Level Images Classification Performance using KNN and SVM Methods 167–172 (2021).

  32. Choudhary, A. et al. An Effective Approach for Classification of Dental Caries using Convolutional Neural Networks. SMART 204–209 (2021).

  33. Lian, L., Zhu, T., Zhu, F. & Zhu, H. Deep Learning for Caries Detection and Classification. Diagnostics (Basel) 11, 1672 (2021).

  34. Lee, S. et al. Deep learning for early dental caries detection in bitewing radiographs. Sci Rep 11, 16807 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ezhov, M. et al. Clinically applicable artificial intelligence system for dental diagnosis with CBCT. Sci Rep 11, 15006 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  36. Bayrakdar, I. S. et al. Deep-learning approach for caries detection and segmentation on dental bitewing radiographs. Oral Radiol 38, 468–479 (2022).

    Article  PubMed  Google Scholar 

  37. Moran, M. et al. Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks. Sensors (Basel) 21, 5192 (2021).

  38. Mao, Y.-C. et al. Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs. Sensors 21, 4613 (2021).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  39. Vinayahalingam, S. et al. Classification of caries in third molars on panoramic radiographs using deep learning. Sci Rep 11, 12609 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Megalan Leo, L. & Kalapalatha Reddy, T. Learning compact and discriminative hybrid neural network for dental caries classification. Microprocessors and Microsystems 82, 103836 (2021).

    Article  Google Scholar 

  41. Khan, H. A. et al. Automated feature detection in dental periapical radiographs by using deep learning. Oral Surgery, Oral Medicine, Oral Pathology 131, 711–720 (2021).

    Article  Google Scholar 

  42. Evaluation of Convolutional Neural Network for Automatic Caries Detection in Digital Radiograph Panoramic on Small Dataset. ICoDSE.

  43. Imak, A. et al. Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network. IEEE Access 10, 18320–18329 (2022).

    Article  Google Scholar 

  44. Jusman, Y., Widyaningrum, A. & Puspita, S. Algorithm of Caries Level Image Classification Using Multilayer Perceptron Based Texture Features. CyberneticsCom 168–173 (2022).

  45. Jusman, Y., Widyaningrum, A., Tyassari, W., Puspita, S. & Saleh, E. Classification of Caries X-Ray Images using Multilayer Perceptron Models Based Shape Features. ICITDA 1–6 (2022).

  46. Jayasinghe, H. et al. Effectiveness of Using Radiology Images and Mask R-CNN for Stomatology. ICAC 60–65 (2022).

  47. Chen, X., Guo, J., Ye, J., Zhang, M. & Liang, Y. Detection of Proximal Caries Lesions on Bitewing Radiographs Using Deep Learning Method. Caries Res 56, 455–463 (2022).

    Article  CAS  PubMed  Google Scholar 

  48. Liu, F. et al. Recognition of Digital Dental X-ray Images Using a Convolutional Neural Network. J Digit Imaging 36, 73–79 (2023).

    Article  PubMed  Google Scholar 

  49. Taleb, A. et al. Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification. Diagnostics (Basel) 12, 1237 (2022).

  50. Panyarak, W., Suttapak, W., Wantanajittikul, K., Charuakkra, A. & Prapayasatok, S. Assessment of YOLOv3 for caries detection in bitewing radiographs based on the ICCMS™radiographic scoring system. Clin Oral Investig (2022).

  51. Kim, C., Jeong, H., Park, W. & Kim, D. Tooth-Related Disease Detection System Based on Panoramic Images and Optimization Through Automation: Development Study. JMIR Med Inform 10, e38640 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Zhu, H. et al. CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image. Neural Comput Appl 1–9 (2022).

  53. Zhu, Y. et al. Faster-RCNN based intelligent detection and localization of dental caries. Displays 74, 102201 (2022).

    Article  CAS  Google Scholar 

  54. Li, S. et al. Artificial intelligence for caries and periapical periodontitis detection. Journal of Dentistry 122, 104107 (2022).

    Article  PubMed  Google Scholar 

  55. Panyarak, W., Wantanajittikul, K., Suttapak, W., Charuakkra, A. & Prapayasatok, S. Feasibility of deep learning for dental caries classification in bitewing radiographs based on the ICCMS™radiographic scoring system. Oral Surg Oral Med Oral Pathol Oral Radiol 135, 272–281 (2023).

    Article  PubMed  Google Scholar 

  56. Ying, S., Wang, B., Zhu, H., Liu, W. & Huang, F. Caries segmentation on tooth X-ray images with a deep network. Journal of Dentistry 119, 104076 (2022).

    Article  PubMed  Google Scholar 

  57. Ramana Kumari, A., Nagaraja Rao, S. & Ramana Reddy, P. Design of hybrid dental caries segmentation and caries detection with meta-heuristic-based ResneXt-RNN. Biomedical Signal Processing and Control 78, 103961 (2022).

    Article  Google Scholar 

  58. Vimalarani, G. & Ramachandraiah, U. Automatic diagnosis and detection of dental caries in bitewing radiographs using pervasive deep gradient based LeNet classifier model. Microprocessors and Microsystems 94, 104654 (2022).

    Article  Google Scholar 

  59. Oztekin, F. et al. An Explainable Deep Learning Model to Prediction Dental Caries Using Panoramic Radiograph Images. Diagnostics 13, 226 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Dayı, B., Üzen, H., Çiçek, p. B. & Duman, U. B. A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs. Diagnostics (Basel) 13, 202 (2023).

  61. Baydar, O., Różyło-Kalinowska, I., Futyma-Gabka, K. & Saǧlam, H. The U-Net Approaches to Evaluation of Dental Bite-Wing Radiographs: An Artificial Intelligence Study. Diagnostics 13, 453 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Yu, Y. et al. Techniques and challenges of image segmentation: A review. Electronics 12 (2023).

  63. Sultana, F., Sufian, A. & Dutta, P. Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey. Knowledge-Based Systems 201-202, 106062 (2020).

    Article  Google Scholar 

  64. Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation 234–241 (2015).

  65. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition, 770–778 (2016).

  66. Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).

  67. Sanil, N., venkat, P. A. N., Rakesh, V., Mallapur, R. & Ahmed, M. R. Deep Learning Techniques for Obstacle Detection and Avoidance in Driverless Cars 1–4 (2020).

  68. Scarfe, W. C., Farman, A. G., Levin, M. D. & Gane, D. Essentials of Maxillofacial Cone Beam Computed Tomography. Alpha Omegan 103, 62–67 (2010).

    Article  PubMed  Google Scholar 

  69. Wenzel, A. Radiographic modalities for diagnosis of caries in a historical perspective: from film to machine-intelligence supported systems. Dentomaxillofacial Radiology 50, 20210010 (2021). PMID: 33661697.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Rad, A. E., Rahim, M. S., Rehman, A. & Saba, T. Digital Dental X-ray Database for Caries Screening. 3D Res. 7, 96:1–96:5 (2016).

  71. Wang, C.-W. et al. A benchmark for comparison of dental radiography analysis algorithms. Medical Image Analysis 31, 63–76 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

Download references

Funding

This work is supported in part by the Brazilian National Council for Scientific and Technological Development (CNPq grant number 307710/2022-0), in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, Brazil, and Itaú Unibanco S.A. through the PBI program of the Centro de Ciência de Dados (C2D) of Escola Politécnica at Universidade de São Paulo, and São Paulo Research Foundation (FAPESP) – National Institute of Science and Technology – Medicine Assisted by Scientific Computing (INCT-MACC) – grant 2014/50889-7.

Author information

Author notes

  1. Izabel Regina Fischer Rubira-Bullen and Fátima de Lourdes dos Santos Nunes contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Engineering and Digital Systems, University of São Paulo, Av. Prof. Luciano Gualberto 158, São Paulo, 05508-010, São Paulo, Brazil

    Luiz Guilherme Kasputis Zanini & Fátima de Lourdes dos Santos Nunes

  2. School of Dentistry at Bauru, University of São Paulo, Alameda Doutor Octávio Pinheiro Brisolla, Bauru, 17012-191, São Paulo, Brazil

    Izabel Regina Fischer Rubira-Bullen

  3. School of Arts, Sciences and Humanities, University of São Paulo, Rua Arlindo Béttio, 1000, São Paulo, 03828-000, São Paulo, Brazil

    Fátima de Lourdes dos Santos Nunes

Authors
  1. Luiz Guilherme Kasputis Zanini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Izabel Regina Fischer Rubira-Bullen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fátima de Lourdes dos Santos Nunes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luiz Guilherme Kasputis Zanini.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zanini, L.G.K., Rubira-Bullen, I.R.F. & Nunes, F.d.L.d.S. A Systematic Review on Caries Detection, Classification, and Segmentation from X-Ray Images: Methods, Datasets, Evaluation, and Open Opportunities. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01054-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10278-024-01054-5

Keywords

Search

Navigation