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Classifying Temporomandibular Disorder with Artificial Intelligent Architecture Using Magnetic Resonance Imaging

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Abstract

This study proposes a new diagnostic tool for automatically extracting discriminative features and detecting temporomandibular joint disc displacement (TMJDD) accurately with artificial intelligence. We analyzed the structural magnetic resonance imaging (MRI) images of 52 patients with TMJDD and 32 healthy controls. The data were split into training and test sets, and only the training sets were used for model construction. U-net was trained with 100 sagittal MRI images of the TMJ to detect the joint cavity between the temporal bone and the mandibular condyle, which was used as the region of interest, and classify the images into binary categories using four convolutional neural networks: InceptionResNetV2, InceptionV3, DenseNet169, and VGG16. The best models were InceptionV3 and DenseNet169; the results of InceptionV3 for recall, precision, accuracy, and F1 score were 1, 0.81, 0.85, and 0.9, respectively, and the corresponding results of DenseNet169 were 0.92, 0.86, 0.85, and 0.89, respectively. Automated detection of TMJDD from sagittal MRI images is a promising technique that involves using deep learning neural networks. It can be used to support clinicians in diagnosing patients as having TMJDD.

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References

  1. Cai, X. Y., J. M. Jin, and C. Yang. Changes in disc position, disc length, and condylar height in the temporomandibular joint with anterior disc displacement: a longitudinal retrospective magnetic resonance imaging study. J. Oral Maxillofac. Surg. 69:e340-346, 2011.

    Article  PubMed  Google Scholar 

  2. De Rossi, S. S., M. S. Greenberg, F. Liu, and A. Steinkeler. Temporomandibular disorders: evaluation and management. Med. Clin. N. Am. 98:1353–1384, 2014.

    Article  PubMed  Google Scholar 

  3. Deng, J., W. Dong, R. Socher, L. Li, L. Kai, and F.-F. Li. ImageNet: a large-scale hierarchical image database. IEEE Conf. Comput. Vis. Pattern Recogn. 2009:248–255, 2009.

    Google Scholar 

  4. Ferreira, L. A., E. Grossmann, E. Januzzi, M. V. de Paula, and A. C. Carvalho. Diagnosis of temporomandibular joint disorders: indication of imaging exams. Braz. J. Otorhinolaryngol. 82:341–352, 2016.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Frazier, J. J., and C. J. Spencer. CBCT imaging of degenerative joint disease of the temporomandibular joints. Gen. Dent. 67:17–19, 2019.

    PubMed  Google Scholar 

  6. Gauer, R. L., and M. J. Semidey. Diagnosis and treatment of temporomandibular disorders. Am. Fam. Phys. 91:378–386, 2015.

    Google Scholar 

  7. Huang, G., Z. Liu, L. V. D. Maaten, and K. Q. Weinberger. Densely connected convolutional networks. IEEE Conf. Comput. Vis. Pattern Recogn. (CVPR). 2017:2261–2269, 2017.

    Google Scholar 

  8. Kim, S. H., Z. W. Geem, and G. T. Han. Hyperparameter optimization method based on harmony search algorithm to improve performance of 1D CNN human respiration pattern recognition system. Sensors (Basel, Switzerland). 20:3697, 2020.

    Article  PubMed  Google Scholar 

  9. Kowalchuk, R. M., R. O. Kowalchuk, K. Kaplan-List, J. M. Caplash, and P. Block. Temporomandibular Joint Internal Derangement Score (TIDS): novel magnetic resonance imaging assessment score and its relation to invasive treatment in patients with clinical temporomandibular joint pathology. Heliyon.4:e00916, 2018.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Larheim, T. A., A. K. Abrahamsson, M. Kristensen, and L. Z. Arvidsson. Temporomandibular joint diagnostics using CBCT. Dentomaxillofac Radiol. 44:20140235, 2015.

    Article  CAS  PubMed  Google Scholar 

  11. Lee, K. S., H. J. Kwak, J. M. Oh, N. Jha, Y. J. Kim, W. Kim, U. B. Baik, and J. J. Ryu. Automated detection of TMJ osteoarthritis based on artificial intelligence. J. Dent. Res. 99:1363–1367, 2020.

    Article  CAS  PubMed  Google Scholar 

  12. Lopes, S. L., A. L. Costa, A. D. Cruz, L. M. Li, and S. M. de Almeida. Clinical and MRI investigation of temporomandibular joint in major depressed patients. Dento Maxillo Fac. Radiol. 41:316–322, 2012.

    Article  CAS  Google Scholar 

  13. Matsubara, R., Y. Yanagi, K. Oki, M. Hisatomi, K. C. Santos, B. O. Bamgbose, M. Fujita, S. Okada, S. Minagi, and J. Asaumi. Assessment of MRI findings and clinical symptoms in patients with temporomandibular joint disorders. Dento Maxillo Fac. Radiol. 47:20170412, 2018.

    Article  Google Scholar 

  14. Nascimento Falcão, I., M. B. C. Cal Alonso, L. H. da Silva, S. Lopes, L. P. Comar, and A. L. F. Costa. 3D morphology analysis of TMJ articular eminence in magnetic resonance imaging. Int. J. Dent. 2017:5130241, 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Ottria, L., V. Candotto, F. Guzzo, M. Gargari, and A. Barlattani. Temporomandibular joint and related structures: anatomical and Histological aspects. J. Biol. Regul. Homeost. Agents. 32:203–207, 2018.

    CAS  PubMed  Google Scholar 

  16. Racich, M. J. Occlusion, temporomandibular disorders, and orofacial pain: an evidence-based overview and update with recommendations. J. Prosthet. Dent. 120:678–685, 2018.

    Article  PubMed  Google Scholar 

  17. Scrivani, S. J., D. A. Keith, and L. B. Kaban. Temporomandibular disorders. N. Engl. J. Med. 359:2693–2705, 2008.

    Article  CAS  PubMed  Google Scholar 

  18. Senior, A., G. Heigold, M. Ranzato, K. Yang. An empirical study of learning rates in deep neural networks for speech recognition. 2013 IEEE international conference on acoustics, speech and signal processing, 6724–6728, 2013.

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

  20. Sudre, C. H., W. Li, T. Vercauteren, S. Ourselin, and M. Jorge Cardoso. Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations. Cham: Springer, pp. 240–248, 2017.

    Google Scholar 

  21. Summa, S., R. Ursini, P. F. Manicone, F. Molinari, and R. Deli. MRI assessment of temporomandibular disorders: an approach to diagnostic and therapeutic setting. Cranio. 32:131–138, 2014.

    Article  CAS  PubMed  Google Scholar 

  22. Szegedy, C., S. Ioffe, V. Vanhoucke, A. Alemi. Inception-v4, inception-ResNet and the impact of residual connections on learning. AAAI conference on artificial intelligence, 2016.

  23. Szegedy, C., V. Vanhoucke, S. Ioffe, J. Shlens, Z. B. Wojna. Rethinking the Inception Architecture for Computer Vision, 2016.

  24. Szegedy, C., L. Wei, J. Yangqing, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. IEEE Conf. Comput. Vis. Pattern Recogn. (CVPR). 2015:1–9, 2015.

    Google Scholar 

  25. Talaat, W. M., O. I. Adel, and S. Al Bayatti. Prevalence of temporomandibular disorders discovered incidentally during routine dental examination using the Research Diagnostic Criteria for Temporomandibular Disorders. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 125:250–259, 2018.

    Article  PubMed  Google Scholar 

  26. Talmaceanu, D., L. M. Lenghel, N. Bolog, M. Hedesiu, S. Buduru, H. Rotar, M. Baciut, and G. Baciut. Imaging modalities for temporomandibular joint disorders: an update. Clujul Med. (1957). 91(3):280–287, 2018.

    Google Scholar 

  27. Vogl, T. J., H.-C. Lauer, T. Lehnert, N. N. N. Naguib, P. Ottl, N. Filmann, H. Soekamto, and N.-E.A. Nour-Eldin. The value of MRI in patients with temporomandibular joint dysfunction: correlation of MRI and clinical findings. Eur. J. Radiol. 85(4):714–719, 2016.

    Article  PubMed  Google Scholar 

  28. Weiss, K., T. M. Khoshgoftaar, and D. Wang. A survey of transfer learning. J. Big Data. 3:9, 2016.

    Article  Google Scholar 

  29. Yosinski, J., J. Clune, Y. Bengio, H. Lipson. How transferable are features in deep neural networks? Proceedings of the 27th international conference on neural information processing systems - Volume 2, 3320–3328, 2014.

  30. Zhou, B., A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba. Learning deep features for discriminative localization. IEEE Conf. Comput. Vis. Pattern Recogn. (CVPR). 2016:2921–2929, 2016.

    Google Scholar 

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Acknowledgments

The authors acknowledge the financial support for research purpose by the Clinical Innovation Center (CiC) of the Taipei Veterans General Hospital, Taipei, Taiwan (CIC008) and Pervasive Artificial Intelligence Research Labs (PAIR Labs) of National Yang Ming Chiao Tung University, Taipei, Taiwan (111W10159). The funders have no role in study design, data collection, analysis, and interpretation, or in writing of the manuscript.

Author Contributions

ZKK: Conceptualization, Data Curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing—original draft. NTC: Conceptualization, Methodology, Software. HTHW: Investigation, Resources. DHW: Formal analysis, Writing—original draft. YYK: Resources. WCC: Software, Visualization. PCT: Visualization. WLL: Conceptualization, Funding acquisition, Project administration, Writing—review & editing. YTW: Validation, Supervision, Writing—review & editing.

Conflict of interest

All authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. Institute of Biophotonics, National Yang Ming Chiao Tung University, No.155, Section 2, Li-Nong Street, Beitou District, 11221, Taipei, Taiwan

    Zih-Kai Kao, Neng-Tai Chiu & Yu-Te Wu

  2. Department of Information Management, Taipei Veterans General Hospital, Taipei, Taiwan

    Zih-Kai Kao

  3. School of Medicine, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Neng-Tai Chiu, Hung-Ta Hondar Wu & Yen-Ying Kung

  4. Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan

    Hung-Ta Hondar Wu

  5. Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Wan-Chen Chang

  6. Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan

    Wan-Chen Chang & Pei-Chi Tu

  7. Department of Dentistry, College of Dentistry, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Ding-Han Wang & Wen-Liang Lo

  8. Institute of Traditional Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Yen-Ying Kung

  9. Center for Traditional Medicine, Taipei Veterans General Hospital, Taipei, Taiwan

    Yen-Ying Kung

  10. Institute of Philosophy of Mind and Cognition, Yang Ming Chiao Tung University, Taipei, Taiwan

    Pei-Chi Tu

  11. Division of Oral and Maxillofacial Surgery, Department of Stomatology, Taipei Veterans General Hospital, No.201, Section 2, Shih-Pai Road, Beitou District, 11217, Taipei, Taiwan

    Wen-Liang Lo

  12. Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Yu-Te Wu

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  1. Zih-Kai Kao
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  2. Neng-Tai Chiu
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Corresponding authors

Correspondence to Wen-Liang Lo or Yu-Te Wu.

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Associate Editor Stefan M. Duma oversaw the review of this article.

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Kao, ZK., Chiu, NT., Wu, HT.H. et al. Classifying Temporomandibular Disorder with Artificial Intelligent Architecture Using Magnetic Resonance Imaging. Ann Biomed Eng 51, 517–526 (2023). https://doi.org/10.1007/s10439-022-03056-2

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  • DOI: https://doi.org/10.1007/s10439-022-03056-2

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