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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Abstract Full text Figures/Tables Video References Cited by

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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22.	白人駒, 徐克. 醫學影像學. 第七版. 北京: 人民衛生出版社, 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.

1. 邢盼盼, 馬宇鋒, 李彧. 顳下頜關節紊亂病的MRI圖像特點及與臨床癥狀相關性研究進展. 山東醫藥, 2019, 59(8): 107-110.
2. 郭宇峰. CBCT三維成像技術在口腔醫學領域中應用探究. 中國醫療器械信息, 2023, 29(16): 136-138.
3. 傅開元, 胡敏, 余強, 等. 顳下頜關節紊亂病錐形束CT檢查規范及診斷標準的專家共識. 中華口腔醫學雜志, 2020, 55(9): 613-616.
4. 楊曉豐, 趙陽, 劉奕. 顳下頜關節紊亂病的醫學影像學診斷方法. 中國實用口腔科雜志, 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. ?i?ek ?, 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. 白人駒, 徐克. 醫學影像學. 第七版. 北京: 人民衛生出版社, 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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