Skip to main content

Advertisement

SpringerLink
Log in

A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System

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

Abstract

This study aims to provide an effective solution for the autonomous identification of dental implant brands through a deep learning-based computer diagnostic system. It also seeks to ascertain the system’s potential in clinical practices and to offer a strategic framework for improving diagnosis and treatment processes in implantology. This study employed a total of 28 different deep learning models, including 18 convolutional neural network (CNN) models (VGG, ResNet, DenseNet, EfficientNet, RegNet, ConvNeXt) and 10 vision transformer models (Swin and Vision Transformer). The dataset comprises 1258 panoramic radiographs from patients who received implant treatments at Erciyes University Faculty of Dentistry between 2012 and 2023. It is utilized for the training and evaluation process of deep learning models and consists of prototypes from six different implant systems provided by six manufacturers. The deep learning-based dental implant system provided high classification accuracy for different dental implant brands using deep learning models. Furthermore, among all the architectures evaluated, the small model of the ConvNeXt architecture achieved an impressive accuracy rate of 94.2%, demonstrating a high level of classification success.This study emphasizes the effectiveness of deep learning-based systems in achieving high classification accuracy in dental implant types. These findings pave the way for integrating advanced deep learning tools into clinical practice, promising significant improvements in patient care and treatment outcomes.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data Availability

Data is available on request from the authors.

References

  1. H.W. Elani, J.R. Starr, J.D. Da Silva, G.O. Gallucci, Trends in Dental Implant Use in the U.S., 1999–2016, and Projections to 2026, J Dent Res 97 (2018) 1424. https://doi.org/10.1177/0022034518792567.

  2. M.A. Saghiri, P. Freag, A. Fakhrzadeh, A.M. Saghiri, J. Eid, Current technology for identifying dental implants: a narrative review, Bull Natl Res Cent 45 (2021) 7. https://doi.org/10.1186/s42269-020-00471-0.

    Article  Google Scholar 

  3. T.G.T.M. T Takahashi K Nozaki, Identification of dental implants using deep learning—pilot study, (2020).

  4. S.-N.J. Jae-Hong Lee, Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study., 99 (2020).

  5. S.-N.J.J.-H. Lee, Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study., 99 (2020).

  6. H.J. Kong, Classification of dental implant systems using cloud-based deep learning algorithm: an experimental study, Journal of Yeungnam Medical Science 40 (2023) S29–S36. https://doi.org/10.12701/JYMS.2023.00465.

  7. S. Sukegawa, K. Yoshii, T. Hara, K. Yamashita, K. Nakano, N. Yamamoto, H. Nagatsuka, Y. Furuki, Deep Neural Networks for Dental Implant System Classification, Biomolecules 2020, Vol. 10, Page 984 10 (2020) 984. https://doi.org/10.3390/BIOM10070984.

  8. M. Alaftekin, I. Pacal, K. Cicek, Real-time sign language recognition based on YOLO algorithm, Neural Comput Appl (2024). https://doi.org/10.1007/s00521-024-09503-6.

    Article  Google Scholar 

  9. J.H. Lee, Y.T. Kim, J. Bin Lee, S.N. Jeong, A Performance Comparison between Automated Deep Learning and Dental Professionals in Classification of Dental Implant Systems from Dental Imaging: A Multi-Center Study, Diagnostics 2020, Vol. 10, Page 910 10 (2020) 910. https://doi.org/10.3390/DIAGNOSTICS10110910.

  10. A. Jokstad, U. Braegger, J.B. Brunski, A.B. Carr, I. Naert, A. Wennerberg, Quality of dental implants, Int Dent J 53 (2003) 409–443. https://doi.org/10.1111/J.1875-595X.2003.TB00918.X.

    Article  PubMed  Google Scholar 

  11. I. Pacal, Enhancing crop productivity and sustainability through disease identification in maize leaves: Exploiting a large dataset with an advanced vision transformer model, Expert Syst Appl 238 (2024) 122099. https://doi.org/10.1016/J.ESWA.2023.122099.

    Article  Google Scholar 

  12. I. Pacal, MaxCerVixT: A novel lightweight vision transformer-based Approach for precise cervical cancer detection, Knowl Based Syst 289 (2024) 111482. https://doi.org/10.1016/j.knosys.2024.111482.

    Article  Google Scholar 

  13. A. Karaman, D. Karaboga, I. Pacal, B. Akay, A. Basturk, U. Nalbantoglu, S. Coskun, O. Sahin, Hyper-parameter optimization of deep learning architectures using artificial bee colony (ABC) algorithm for high performance real-time automatic colorectal cancer (CRC) polyp detection, Applied Intelligence 53 (2023) 15603–15620. https://doi.org/10.1007/s10489-022-04299-1.

    Article  Google Scholar 

  14. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015. https://doi.org/10.1109/CVPR.2015.7298594.

    Article  Google Scholar 

  15. M. Shafiq, Z. Gu, Deep Residual Learning for Image Recognition: A Survey, Applied Sciences (Switzerland) 12 (2022). https://doi.org/10.3390/app12188972.

  16. D. Deporter, A.A. Khoshkhounejad, N. Khoshkhounejad, M. Ketabi, A new classification of peri implant gaps based on gap location (A case series of 210 immediate implants), Dent Res J (Isfahan) 18 (2021) 29. https://doi.org/10.4103/1735-3327.313124.

    Article  PubMed  Google Scholar 

  17. I. Pacal, D. Karaboga, A robust real-time deep learning based automatic polyp detection system, Comput Biol Med 134 (2021) 104519. https://doi.org/10.1016/J.COMPBIOMED.2021.104519.

    Article  PubMed  Google Scholar 

  18. I. Pacal, S. Kılıcarslan, Deep learning-based approaches for robust classification of cervical cancer, Neural Comput Appl 35 (2023) 18813–18828. https://doi.org/10.1007/S00521-023-08757-W/METRICS.

    Article  Google Scholar 

  19. A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, J. Dean, A guide to deep learning in healthcare, Nat Med 25 (2019) 24–29. https://doi.org/10.1038/s41591-018-0316-z.

    Article  CAS  PubMed  Google Scholar 

  20. I. Pacal, A. Karaman, D. Karaboga, B. Akay, A. Basturk, U. Nalbantoglu, S. Coskun, An efficient real-time colonic polyp detection with YOLO algorithms trained by using negative samples and large datasets, Comput Biol Med 141 (2022) 105031. https://doi.org/10.1016/J.COMPBIOMED.2021.105031.

    Article  PubMed  Google Scholar 

  21. S. Shamshirband, M. Fathi, A. Dehzangi, A.T. Chronopoulos, H. Alinejad-Rokny, A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues, J Biomed Inform 113 (2021) 103627. https://doi.org/10.1016/j.jbi.2020.103627.

    Article  PubMed  Google Scholar 

  22. M. Alhanjouri, M. A. H. Lubbad, M. Z. Alkurdi, Robust Speaker Identification using Denoised Wave Atom and GMM, Int J Comput Appl 67 (2013) 17–23. https://doi.org/10.5120/11391-6687.

    Article  Google Scholar 

  23. A. Karaman, I. Pacal, A. Basturk, B. Akay, U. Nalbantoglu, S. Coskun, O. Sahin, D. Karaboga, Robust real-time polyp detection system design based on YOLO algorithms by optimizing activation functions and hyper-parameters with artificial bee colony (ABC), Expert Syst Appl 221 (2023) 119741. https://doi.org/10.1016/J.ESWA.2023.119741.

    Article  Google Scholar 

  24. M. Lubbad, D. Karaboga, A. Basturk, B. Akay, U. Nalbantoglu, I. Pacal, Machine learning applications in detection and diagnosis of urology cancers: a systematic literature review, Neural Comput Appl (2024) 1–25. https://doi.org/10.1007/S00521-023-09375-2/METRICS.

  25. I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative Adversarial Networks, Sci Robot 3 (2014) 2672–2680. https://arxiv.org/abs/1406.2661v1 (accessed February 6, 2024).

  26. K. O’Shea, R. Nash, An Introduction to Convolutional Neural Networks, Int J Res Appl Sci Eng Technol 10 (2015) 943–947. https://doi.org/10.22214/ijraset.2022.47789.

  27. Z. Li, F. Liu, W. Yang, S. Peng, J. Zhou, A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects, IEEE Trans Neural Netw Learn Syst 33 (2022) 6999–7019. https://doi.org/10.1109/TNNLS.2021.3084827.

    Article  PubMed  Google Scholar 

  28. Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86 (1998). https://doi.org/10.1109/5.726791.

  29. A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, Commun ACM 60 (2017). https://doi.org/10.1145/3065386.

  30. I. Pacal, D. Karaboga, A. Basturk, B. Akay, U. Nalbantoglu, A comprehensive review of deep learning in colon cancer, Comput Biol Med 126 (2020) 104003. https://doi.org/10.1016/J.COMPBIOMED.2020.104003.

    Article  PubMed  Google Scholar 

  31. K. He, G. Gkioxari, P. Dollár, R. Girshick, Mask R-CNN, IEEE Trans Pattern Anal Mach Intell 42 (2020). https://doi.org/10.1109/TPAMI.2018.2844175.

  32. M. Tan, Q. V Le, EfficientNet: Rethinking model scaling for convolutional neural networks, in: 36th International Conference on Machine Learning, ICML 2019, 2019.

  33. A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, (2017).

  34. J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, T. Chen, Recent advances in convolutional neural networks, Pattern Recognit 77 (2018) 354–377. https://doi.org/10.1016/J.PATCOG.2017.10.013.

    Article  Google Scholar 

  35. I. Leblebicioglu, M. Lubbad, O. M. D. Yilmaz, K. Kilic, D. Karaboga, A. Basturk, ... & I. Pacal. A robust deep learning model for the classification of dental implant brands, Journal of Stomatology, Oral and Maxillofacial Surgery (2024) 101818

  36. S. Albawi, T.A. Mohammed, S. Al-Zawi, Understanding of a convolutional neural network, Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017 2018-January (2017) 1–6. https://doi.org/10.1109/ICENGTECHNOL.2017.8308186.

  37. R. Yamashita, M. Nishio, R.K.G. Do, K. Togashi, Convolutional neural networks: an overview and application in radiology, Insights Imaging 9 (2018) 611–629. https://doi.org/10.1007/S13244-018-0639-9/FIGURES/15.

    Article  Google Scholar 

  38. J. Heaton, Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning, Genet Program Evolvable Mach 19 (2018) 305–307. https://doi.org/10.1007/s10710-017-9314-z.

    Article  Google Scholar 

  39. Y. Lecun, Y. Bengio, G. Hinton, Deep learning, Nature 521 (2015) 436–444. https://doi.org/10.1038/NATURE14539.

    Article  CAS  PubMed  Google Scholar 

  40. M.E. Klontzas, G. Kalarakis, E. Koltsakis, T. Papathomas, A.H. Karantanas, A. Tzortzakakis, Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset, Insights Imaging 15 (2024) 1–11. https://doi.org/10.1186/S13244-023-01601-8/FIGURES/5.

    Article  Google Scholar 

  41. H. Habibi Aghdam, E. Jahani Heravi, Guide to Convolutional Neural Networks, Guide to Convolutional Neural Networks (2017). https://doi.org/10.1007/978-3-319-57550-6.

  42. N. Kalchbrenner, E. Grefenstette, P. Blunsom, A Convolutional Neural Network for Modelling Sentences, 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference 1 (2014) 655–665. https://doi.org/10.3115/v1/p14-1062.

  43. N. Ketkar, J. Moolayil, Convolutional Neural Networks, Deep Learning with Python (2021) 197–242. https://doi.org/10.1007/978-1-4842-5364-9_6.

  44. N. Ketkar, J. Moolayil, Deep learning with python: Learn Best Practices of Deep Learning Models with PyTorch, Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch (2021) 1–306. https://doi.org/10.1007/978-1-4842-5364-9.

  45. P. Ramachandran, B. Zoph, Q. V Le Google Brain, Searching for Activation Functions, 6th International Conference on Learning Representations, ICLR 2018 - Workshop Track Proceedings (2017). https://arxiv.org/abs/1710.05941v2 (accessed February 15, 2024).

  46. Z. Yang, Z. Yang, Activation Function: Cell Recognition Based on YoLov5s/m, Journal of Computer and Communications 9 (2021) 1–16. https://doi.org/10.4236/JCC.2021.912001.

    Article  Google Scholar 

  47. M.D. Zeiler, R. Fergus, Visualizing and understanding convolutional networks, in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014. https://doi.org/10.1007/978-3-319-10590-1_53.

    Article  Google Scholar 

  48. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A.C. Berg, L. Fei-Fei, ImageNet Large Scale Visual Recognition Challenge, Int J Comput Vis 115 (2015). https://doi.org/10.1007/s11263-015-0816-y.

  49. K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, in: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 2015.

  50. G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017. https://doi.org/10.1109/CVPR.2017.243.

  51. S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, IEEE Trans Pattern Anal Mach Intell 39 (2017). https://doi.org/10.1109/TPAMI.2016.2577031.

  52. J.-S.S.Y.-H.J.B.-H.C.J.J.H.J.-E.K.N.-E. Nam, Transfer Learning via Deep Neural Networks for Implant Fixture System Classification Using Periapical Radiographs., 9 (2020).

  53. J. Xu, Y. Pan, X. Pan, S. Hoi, Z. Yi, Z. Xu, RegNet: Self-Regulated Network for Image Classification, IEEE Trans Neural Netw Learn Syst 34 (2023). https://doi.org/10.1109/TNNLS.2022.3158966.

  54. Z. Liu, H. Mao, C.Y. Wu, C. Feichtenhofer, T. Darrell, S. Xie, A ConvNet for the 2020s, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2022. https://doi.org/10.1109/CVPR52688.2022.01167.

    Article  Google Scholar 

  55. Y. Zhang, J. Wang, J.M. Gorriz, S. Wang, Deep Learning and Vision Transformer for Medical Image Analysis, J Imaging 9 (2023) 147. https://doi.org/10.3390/JIMAGING9070147.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Y.E. Almalki, M. Zaffar, M. Irfan, M.A. Abbas, M. Khalid, K.S. Quraishi, T. Ali, F. Alshehri, S.K. Alduraibi, A.A. Asiri, M.A.A. Basha, A. Alduraibi, M.K. Saeed, S. Rahman, A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients, Intelligent Automation & Soft Computing 35 (2023) 163–180. https://doi.org/10.32604/IASC.2023.025580.

  57. Y. Li, S. Rao, J.R.A. Solares, A. Hassaine, R. Ramakrishnan, D. Canoy, Y. Zhu, K. Rahimi, G. Salimi-Khorshidi, BEHRT: Transformer for Electronic Health Records, Scientific Reports 2020 10:1 10 (2020) 1–12. https://doi.org/10.1038/s41598-020-62922-y.

  58. T.G.T.M.T.T.K. Nozaki, Identification of dental implants using deep learning—pilot study, (2020).

  59. S. Sharma, R. Mehra, Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images—a Comparative Insight, J Digit Imaging 33 (2020). https://doi.org/10.1007/s10278-019-00307-y.

Download references

Acknowledgements

The computational experiments were conducted utilizing the resources available at the Artificial Intelligence and Big Data Application and Research Center at Erciyes University in Turkey. Ethical clearance for this study was acquired from Erciyes University Dental Hospital under the authorization of permission decision 2021/234, dated 24.03.2021. We express our gratitude to TÜBİTAK for their support of this project, which bears project number 121E068.

Funding

This work was supported by the Scientific and Technological Research Council Of Turkey (TUBITAK) (Grant Numbers: 121E068).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Engineering Faculty, Erciyes University, 38039, Kayseri, Turkey

    Mohammed A. H. Lubbad, Dervis Karaboga, Alper Basturk, Bahriye Akay & Ozkan Ufuk Nalbantoglu

  2. Department of Prosthodontics, Dentistry Faculty, Erciyes University, Kayseri, Turkey

    Ikbal Leblebicioglu Kurtulus, Kerem Kilic, Ozden Melis Durmaz Yilmaz & Mustafa Ayata

  3. Department of Dentomaxillofacial Radiology, Dentistry Faculty, Erciyes University, Kayseri, Turkey

    Serkan Yilmaz

  4. Department of Computer Engineering, Engineering Faculty, Igdir University, Igdir, Turkey

    Ishak Pacal

  5. Artificial Intelligence and Big Data Application and Research Center, Erciyes University, Kayseri, Turkey

    Mohammed A. H. Lubbad, Dervis Karaboga, Alper Basturk, Bahriye Akay, Ozkan Ufuk Nalbantoglu & Ishak Pacal

Authors
  1. Mohammed A. H. Lubbad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ikbal Leblebicioglu Kurtulus
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dervis Karaboga
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kerem Kilic
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alper Basturk
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bahriye Akay
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ozkan Ufuk Nalbantoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ozden Melis Durmaz Yilmaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mustafa Ayata
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Serkan Yilmaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ishak Pacal
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Mohammed A. H. Lubbad: conceptualization, methodology, software, reviewing, investigation, validation, data curation, writing—review and editing; Ikbal Leblebicioglu Kurtulus: conceptualization, reviewing, investigation, validation, data curation; Dervis Karaboga: conceptualization, methodology, reviewing, supervision, validation, writing—review and editing; Kerem Kilic: conceptualization, reviewing, investigation, validation, data curation; Alper Basturk: conceptualization, methodology, reviewing, supervision, validation, writing—review and editing; Bahriye Akay: conceptualization, methodology, reviewing, supervision, validation, writing—review and editing; Ozkan Ufuk Nalbantoglu: conceptualization, methodology, reviewing, validation, writing—review and editing; Ozden Melis Durmaz Yilmaz: conceptualization, reviewing, investigation, validation, data curation; Mustafa Ayata: conceptualization, reviewing, investigation, validation, data curation; Serkan Yilmaz: conceptualization, reviewing, investigation, validation, data curation; Ishak Pacal: conceptualization, methodology, software, investigation, writing—review and editing.

Corresponding author

Correspondence to Mohammed A. H. Lubbad.

Ethics declarations

Informed Consent

There is no any patient data used in this research.

Competing Interest

The authors declare no competing interests.

Human and Animal Rights

The authors did not perform any experiments with animals for conducting this research.

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

Lubbad, M.A.H., Kurtulus, I.L., Karaboga, D. et al. A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01086-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10278-024-01086-x

Keywords

Search

Navigation