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Deep-learning approach for caries detection and segmentation on dental bitewing radiographs

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A Letter to the Editor to this article was published on 11 February 2023

Abstract

Objectives

The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer.

Methods

A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively.

Results

The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists.

Conclusion

CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry.

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References

  1. Frencken JE, Sharma P, Stenhouse L, Green D, Laverty D, Dietrich T. Global epidemiology of dental caries and severe periodontitis—a comprehensive review. J Clin Periodontol. 2017;44(Suppl 18):S94-s105.

    Article  PubMed  Google Scholar 

  2. Stookey GK, Jackson RD, Zandona AG, Analoui M. Dental caries diagnosis. Dent Clin North Am. 1999;43:665-77 vi.

    Article  PubMed  Google Scholar 

  3. Hellén-Halme K, Petersson GH. Influence of education level and experience on detection of approximal caries in digital dental radiographs. An in vitro study. Swed Dent J. 2010;34:63–9.

    PubMed  Google Scholar 

  4. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18:500–10.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Leite AF, Van Gerven A, Willems H, Beznik T, Lahoud P, Gaêta-Araujo H, et al. Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs. Clin Oral Invest. 2020. https://doi.org/10.1007/s00784-020-03544-6.

    Article  Google Scholar 

  6. 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;49:20190107.

    Article  PubMed  Google Scholar 

  7. Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, et al. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Sci Rep. 2019;9:3840.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kılıc MC, Bayrakdar IS, Çelik Ö, Bilgir E, Orhan K, Aydın OB, et al. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofac Radiol. 2021;50:20200172.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Yasa Y, Çelik Ö, Bayrakdar IS, Pekince A, Orhan K, Akarsu S, et al. An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs. Acta Odontol Scand. 2020. https://doi.org/10.1080/00016357.2020.1840624.

    Article  PubMed  Google Scholar 

  10. Lee JH, Han SS, Kim YH, Lee C, Kim I. Application of a fully deep convolutional neural network to the automation of tooth segmentation on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;129:635–42.

    Article  PubMed  Google Scholar 

  11. Kim J, Lee HS, Song IS, Jung KH. DeNTNet: Deep Neural Transfer Network for the detection of periodontal bone loss using panoramic dental radiographs. Sci Rep. 2019;9:17615.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep learning for the radiographic detection of periodontal bone loss. Sci Rep. 2019;9:8495.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Bayrakdar SK, Çelik Ö, Bayrakdar İŞ, Orhan K, Bilgir E, Odabas A, et al. Success of artificial intelligence system in determining alveolar bone loss from dental panoramic radiography images. Cumhuriyet Dent. J. 2020;23:318–24.

  14. Kositbowornchai S, Siriteptawee S, Plermkamon S, Bureerat S, Chetchotsak D. An artificial neural network for detection of simulated dental caries. Int J Comput Assist Radiol Surg. 2006;1:91–6.

    Article  Google Scholar 

  15. 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;77:106–11.

    Article  PubMed  Google Scholar 

  16. 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.

    Article  PubMed  Google Scholar 

  17. Casalegno F, Newton T, Daher R, Abdelaziz M, Lodi-Rizzini A, Schürmann F, et al. Caries detection with near-infrared transillumination using deep learning. J Dent Res. 2019;98:1227–33.

    Article  PubMed  Google Scholar 

  18. Prados-Privado M, García Villalón J, Martínez-Martínez CH, Ivorra C, Prados-Frutos JC. Dental caries diagnosis and detection using neural networks: a systematic review. J Clin Med. 2020;9:3579.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Schwendicke F, Elhennawy K, Paris S, Friebertshäuser P, Krois J. Deep learning for caries lesion detection in near-infrared light transillumination images: a pilot study. J Dent. 2020;92:103260.

    Article  PubMed  Google Scholar 

  20. Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, et al. Deep learning for the radiographic detection of apical lesions. J Endod. 2019;45:917-22.e5.

    Article  PubMed  Google Scholar 

  21. Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J. 2020;53:680–9.

    Article  PubMed  Google Scholar 

  22. Endres MG, Hillen F, Salloumis M, Sedaghat AR, Niehues SM, Quatela O, et al. Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics (Basel). 2020;10:430.

    Article  PubMed  Google Scholar 

  23. Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, et al. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofac Radiol. 2019;48:20180218.

    Article  PubMed  Google Scholar 

  24. Jeon SJ, Yun JP, Yeom HG, Shin WS, Lee JH, Jeong SH, et al. Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs. Dentomaxillofac Radiol. 2021;50:20200513.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Fukuda M, Inamoto K, Shibata N, Ariji Y, Yanashita Y, Kutsuna S, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol. 2020;36:337–43.

    Article  PubMed  Google Scholar 

  26. Vinayahalingam S, Xi T, Bergé S, Maal T, de Jong G. Automated detection of third molars and mandibular nerve by deep learning. Sci Rep. 2019;9:9007.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Kuwada C, Ariji Y, Fukuda M, Kise Y, Fujita H, Katsumata A, et al. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130:464–9.

    Article  PubMed  Google Scholar 

  28. Fukuda M, Ariji Y, Kise Y, Nozawa M, Kuwada C, Funakoshi T, et al. Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130:336–43.

    Article  PubMed  Google Scholar 

  29. Orhan K, Bilgir E, Bayrakdar IS, Ezhov M, Gusarev M, Shumilov E. Evaluation of artificial intelligence for detecting impacted third molars on cone-beam computed tomography scans. J Stomatol Oral Maxillofac Surg. 2020. https://doi.org/10.1016/j.jormas.2020.12.006.

    Article  PubMed  Google Scholar 

  30. Ariji Y, Yanashita Y, Kutsuna S, Muramatsu C, Fukuda M, Kise Y, et al. Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique. Oral Surg Oral Med Oral Pathol Oral Radiol. 2019;128:424–30.

    Article  PubMed  Google Scholar 

  31. Yang H, Jo E, Kim HJ, Cha IH, Jung YS, Nam W, et al. Deep learning for automated detection of cyst and tumors of the jaw in panoramic radiographs. J Clin Med. 2020;9:1839.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kwon O, Yong TH, Kang SR, Kim JE, Huh KH, Heo MS, et al. Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network. Dentomaxillofac Radiol. 2020;49:20200185.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Lee JH, Kim DH, Jeong SN. Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network. Oral Dis. 2020;26:152–8.

    Article  PubMed  Google Scholar 

  34. Watanabe H, Ariji Y, Fukuda M, Kuwada C, Kise Y, Nozawa M, et al. Deep learning object detection of maxillary cyst-like lesions on panoramic radiographs: preliminary study. Oral Radiol. 2020. https://doi.org/10.1007/s11282-020-00485-4.

    Article  PubMed  Google Scholar 

  35. Lee JH, Jeong SN. Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: a pilot study. Medicine (Baltimore). 2020;99: e20787.

    Article  PubMed  Google Scholar 

  36. Sukegawa S, Yoshii K, Hara T, Yamashita K, Nakano K, Yamamoto N, et al. Deep neural networks for dental implant system classification. Biomolecules. 2020;10:984.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Lee KS, Jung SK, Ryu JJ, Shin SW, Choi J. Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs. J Clin Med. 2020;9:392.

    Article  PubMed  PubMed Central  Google Scholar 

  38. 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.

    Article  PubMed  Google Scholar 

  39. Kuwana R, Ariji Y, Fukuda M, Kise Y, Nozawa M, Kuwada C, et al. Performance of deep learning object detection technology in the detection and diagnosis of maxillary sinus lesions on panoramic radiographs. Dentomaxillofac Radiol. 2021;50:20200171.

    PubMed  Google Scholar 

  40. Kim H, Shim E, Park J, Kim YJ, Lee U, Kim Y. Web-based fully automated cephalometric analysis by deep learning. Comput Methods Programs Biomed. 2020;194:105513.

    Article  PubMed  Google Scholar 

  41. Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J. Artificial intelligence in orthodontics: evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. J Orofac Orthop. 2020;81:52–68.

    Article  PubMed  Google Scholar 

  42. Sin Ç, Akkaya N, Aksoy S, Orhan K, Öz U. A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images. Orthod Craniofac Res. 2021. https://doi.org/10.1111/ocr.12480.

    Article  PubMed  Google Scholar 

  43. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint https://arxiv.org/abs/1409.1556. 2014.

  44. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention: Springer; 2015. p. 234–41.

  45. Schneiderman A, Elbaum M, Shultz T, Keem S, Greenebaum M, Driller J. Assessment of dental caries with Digital Imaging Fiber-Optic TransIllumination (DIFOTI): in vitro study. Caries Res. 1997;31:103–10.

    Article  PubMed  Google Scholar 

  46. Macey R, Walsh T, Riley P, Hogan R, Glenny AM, Worthington HV, et al. Transillumination and optical coherence tomography for the detection and diagnosis of enamel caries. Cochrane Database Syst Rev. 2021;1:Cd013855.

    PubMed  Google Scholar 

  47. Macey R, Walsh T, Riley P, Glenny AM, Worthington HV, Clarkson JE, et al. Electrical conductance for the detection of dental caries. Cochrane Database Syst Rev. 2021;3:Cd014547.

    PubMed  Google Scholar 

  48. Takahashi N, Lee C, Da Silva JD, Ohyama H, Roppongi M, Kihara H, et al. A comparison of diagnosis of early stage interproximal caries with bitewing radiographs and periapical images using consensus reference. Dentomaxillofac Radiol. 2019;48:20170450.

    Article  PubMed  Google Scholar 

  49. Geetha V, Aprameya K, Hinduja DM. Dental caries diagnosis in digital radiographs using back-propagation neural network. Health Inf Sci Syst. 2020;8:1–14.

    Article  Google Scholar 

  50. Devito KL, de Souza BF, 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;106:879–84.

    Article  Google Scholar 

  51. Khan HA, Haider MA, Ansari HA, Ishaq H, Kiyani A, Sohail K, et al. Automated feature detection in dental periapical radiographs using deep learning. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;131:711–20.

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

This work has been supported by Eskisehir Osmangazi University Scientific Research Projects Coordination Unit under Grant number 202045E06.

Author information

Authors and Affiliations

  1. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey

    Ibrahim Sevki Bayrakdar, Elif Bilgir & Hande Sağlam

  2. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey

    Kaan Orhan

  3. Department of Restorative Dentistry, Faculty of Dentistry, Ordu University, Ordu, Turkey

    Samet Atasoy

  4. Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey

    Serdar Akarsu, Özer Çelik, Ahmet Faruk Aslan & Alper Odabaş

  5. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Karabuk University, Karabuk, Turkey

    Adem Pekince

  6. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ordu University, Ordu, Turkey

    Yasin Yasa

  7. Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey

    Ibrahim Sevki Bayrakdar

  8. Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey

    Kaan Orhan & Özer Çelik

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  1. Ibrahim Sevki Bayrakdar
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Correspondence to Ibrahim Sevki Bayrakdar.

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Appendix A

Appendix A

Eskisehir Osmangazi University Faculty of Dentistry Dental-Artificial Intelligence (AI) Laboratory has advanced technology computer equipment’s including Dell PowerEdge T640 Calculation Server (Intel Xeon Gold 5218 2.3G, 16C/32 T, 10.4GT/s, 22 M Cache, Turbo, HT (125 W) DDR4‐2666, 32 GB RDIMM, 3200MT/s, Dual Rank, PERC H330 + RAID Controller, 480 GB SSD SATA Read Intensive 6Gbps 512 2.5in Hot‐plug AG Drive), PowerEdge T640 GPU Calculation Server (Intel Xeon Gold 5218 2.3G, 16C/32 T, 10.4GT/s, 22 M Cache, Turbo, HT (125 W) DDR4‐2666 2, 32 GB RDIMM, 3200MT/s, Dual Rank, PERC H330 + RAID Controller, 480 GB SSD SATA Read Intensive 6Gbps 512 2.5in Hot‐plug AG Drive, NVIDIA Tesla V100 16G Passive GPU), PowerEdge R540 Storage Server (Intel Xeon Silver 4208 2.1G, 8C/16 T, 9.6GT/s, 11 M Cache, Turbo, HT (85 W) DDR4‐2400, 16 GB RDIMM, 3200MT/s, Dual Rank, PERC H730P + RAID Controller, 2 Gb NV Cache, Adapter, Low Profile, 8 TB 7.2 K RPM SATA 6Gbps 512e 3.5in Hot‐plug Hard Drive, 240 GB SSD SATA Mixed Use 6Gbps 512e 2.5in Hot plug, 3.5in HYB CARR S4610 Drive), Precision 3640 Tower CTO BASE workstation (Intel(R) Xeon(R) W‐1250P (6 Core, 12 M cache, base 4.1 GHz, up to 4.8 GHz) DDR4‐2666, 64 GB DDR4 (4 X16GB) 2666 MHz UDIMM ECC Memory, 256 GB SSD SATA, Nvidia Quadro P620, 2 GB), Dell EMC Network Switch (N1148T‐ON, L2, 48 ports RJ45 1GbE, 4 ports SFP + 10GbE, Stacking).

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Bayrakdar, I.S., Orhan, K., Akarsu, S. et al. Deep-learning approach for caries detection and segmentation on dental bitewing radiographs. Oral Radiol 38, 468–479 (2022). https://doi.org/10.1007/s11282-021-00577-9

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