Research and implementation of intelligent diagnostic system for temporomandibular joint disorder_Journal of Biomedical Engineering

Authors：

ZHANG Minghao ¹ , YANG Dong ¹ , LI Xiaonan ² , ZHANG Qian ² ,  LIU Zhiyang ^1,3

1. College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, P. R. China;
2. School and Hospital of Stomatology, Tianjin Medical University, Tianjin 300070, P. R. China;
3. Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Tianjin 300350, P. R. China;

Corresponding author：

LIU Zhiyang, Email: liuzhiyang@nankai.edu.cn

Keywords：

Temporomandibular joint disorder; Deep learning; Generative adversarial nets; Semi-supervised learning; Edge computing

DOI：

10.7507/1001-5515.202402002

Video：

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Temporomandibular joint disorder (TMD) is a common oral and maxillofacial disease, which is difficult to detect due to its subtle early symptoms. In this study, a TMD intelligent diagnostic system implemented on edge computing devices was proposed, which can achieve rapid detection of TMD in clinical diagnosis and facilitate its early-stage clinical intervention. The proposed system first automatically segments the important components of the temporomandibular joint, followed by quantitative measurement of the joint gap area, and finally predicts the existence of TMD according to the measurements. In terms of segmentation, this study employs semi-supervised learning to achieve the accurate segmentation of temporomandibular joint, with an average Dice coefficient (DC) of 0.846. A 3D region extraction algorithm for the temporomandibular joint gap area is also developed, based on which an automatic TMD diagnosis model is proposed, with an accuracy of 83.87%. In summary, the intelligent TMD diagnosis system developed in this paper can be deployed at edge computing devices within a local area network, which is able to achieve rapid detecting and intelligent diagnosis of TMD with privacy guarantee.

Citation： ZHANG Minghao, YANG Dong, LI Xiaonan, ZHANG Qian, LIU Zhiyang. Research and implementation of intelligent diagnostic system for temporomandibular joint disorder. Journal of Biomedical Engineering, 2024, 41(5): 869-877. doi: 10.7507/1001-5515.202402002 Copy

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13.	Chen J, Lu Y, Yu Q, et al. TransUNet: Transformers make strong encoders for medical image segmentation. arXiv preprint, 2021, arXiv: 2102.04306.
14.	Cao H, Wang Y, Chen J, et al. Swin-Unet: Unet-like pure transformer for medical image segmentation//European Conference on Computer Vision, Tel Aviv: European Computer Vision Association, 2022: 205-218.
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23.	Laine S, Aila T. Temporal ensembling for semi-supervised learning. arXiv preprint, 2016, arXiv: 1610.02242.
24.	Zhou Z, Siddiquee M M R, Tajbakhsh N, et al. UNet++: a nested U-Net architecture for medical image segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018), 2018, 11045: 3-11.
25.	Lee Y H, Hong I K, An J S. Anterior joint space narrowing in patients with temporomandibular disorder. J Orofac Orthop, 2019, 80(3): 116-127.

1. 邢盼盼, 马宇锋, 李彧. 颞下颌关节紊乱病的MRI图像特点及与临床症状相关性研究进展. 山东医药, 2019, 59(8): 107-110.
2. 郭宇峰. CBCT三维成像技术在口腔医学领域中应用探究. 中国医疗器械信息, 2023, 29(16): 136-138.
3. 傅开元, 胡敏, 余强, 等. 颞下颌关节紊乱病锥形束CT检查规范及诊断标准的专家共识. 中华口腔医学杂志, 2020, 55(9): 613-616.
4. 杨晓丰, 赵阳, 刘奕. 颞下颌关节紊乱病的医学影像学诊断方法. 中国实用口腔科杂志, 2023, 16(2): 152-155.
5. Jang T J, Kim K C, Cho H C, et al. A fully automated method for 3D individual tooth identification and segmentation in dental CBCT. IEEE Trans Pattern Anal Mach Intell, 2022, 44(10): 6562-6568.
6. Yang Y, Xie R, Jia W, et al. Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method. Neurocomputing, 2021, 419: 108-125.
7. Liu Y, Xin R, Yang T, et al. Inferior alveolar nerve segmentation in CBCT images using connectivity-based selective re-training. arXiv preprint, 2023, arXiv: 2308.09298.
8. Liu Z, Yang D, Zhang M, et al. Inferior Alveolar nerve canal segmentation on CBCT using U-Net with frequency attentions. Bioengineering, 2024, 11(4): 354.
9. Chen L C, Papandreou G, Kokkinos I, et al. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell, 2018, 40(4): 834-848.
10. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation// Medical Image Computing and Computer-Assisted Intervention 2015 (MICCAI 2015): 18th International Conference, Munich: MICCAI, 2015: 234-241.
11. Çiçek Ö, Abdulkadir A, Lienkamp S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation// Medical Image Computing and Computer-Assisted Intervention 2016 (MICCAI 2016): 19th International Conference, Athens: MICCAI, 2016: 424-432.
12. Schlemper J, Oktay O, Schaap M, et al. Attention gated networks: learning to leverage salient regions in medical images. Medical Image Analysis, 2019, 53: 197-207.
13. Chen J, Lu Y, Yu Q, et al. TransUNet: Transformers make strong encoders for medical image segmentation. arXiv preprint, 2021, arXiv: 2102.04306.
14. Cao H, Wang Y, Chen J, et al. Swin-Unet: Unet-like pure transformer for medical image segmentation//European Conference on Computer Vision, Tel Aviv: European Computer Vision Association, 2022: 205-218.
15. Liu Z, Lin Y, Cao Y, et al. Swin Transformer: hierarchical vision transformer using shifted windows//Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal: IEEE/CVF, 2021: 10012-10022.
16. Hospedales T, Antoniou A, Micaelli P, et al. Meta-learning in neural networks: A survey. IEEE Trans Pattern Anal Mach Intell, 2022, 44(9): 5149-5169.
17. Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets//Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal: NIPS, 2014, 2: 2672–2680.
18. Zhang C, Lin G, Liu F, et al. CANet: class-agnostic segmentation networks with iterative refinement and attentive few-shot learning//IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach: IEEE/CVF, 2019: 5217-5226.
19. Dong N, Xing E P. Few-shot semantic segmentation with prototype learning//British Machine Vision Conference 2018, Newcastle: British Machine Vision Association, 2018: 4.
20. Lai X, Tian Z, Jiang L, et al. Semi-supervised semantic segmentation with directional context-aware consistency// IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville: IEEE/CVF, 2021: 1205-1214.
21. Ouyang C, Biffi C, Chen C, et al. Self-supervision with superpixels: training few-shot medical image segmentation without annotation//Computer Vision–ECCV 2020: 16th European Conference, Glasgow: European Computer Vision Association, 2020: 762-780.
22. 白人驹, 徐克. 医学影像学. 第七版. 北京: 人民卫生出版社, 2013: 1-408.
23. Laine S, Aila T. Temporal ensembling for semi-supervised learning. arXiv preprint, 2016, arXiv: 1610.02242.
24. Zhou Z, Siddiquee M M R, Tajbakhsh N, et al. UNet++: a nested U-Net architecture for medical image segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018), 2018, 11045: 3-11.
25. Lee Y H, Hong I K, An J S. Anterior joint space narrowing in patients with temporomandibular disorder. J Orofac Orthop, 2019, 80(3): 116-127.

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