Study on automatic and rapid diagnosis of distal radius fracture by X-ray_Journal of Biomedical Engineering

Authors：

LIU Yunpeng ¹ ,  GAN Kaifeng ² , LI Jin ³ , SUN Dechao ⁴ , QIU Hong ³ , LIU Dongquan ⁵

1. Information and Computing Science Department, International Exchange College, Ningbo University of Technology, Ningbo, Zhejiang 315000, P. R. China;
2. Orthopedics, Lihuili Hospital Affiliated to Ningbo University, Ningbo, Zhejiang 3151000, P. R. China;
3. Zhejiang Wanli University, Ningbo, Zhejiang 315000, P. R. China;
4. College of Digital Technology and Engineering, Ningbo University of Finance & Economics, Ningbo, Zhejiang 315000, P. R. China;
5. Radiology Department, Ninghai First Hospital, Ningbo, Zhejiang 315000, P. R. China;

Corresponding author：

GAN Kaifeng, Email: gankaifeng_nb@163.com

Keywords：

Deep learning; Medical images; Segmentation; Classification; Fracture diagnosis

DOI：

10.7507/1001-5515.202309050

Video：

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Abstract Full text Figures/Tables Video References Cited by

This article aims to combine deep learning with image analysis technology and propose an effective classification method for distal radius fracture types. Firstly, an extended U-Net three-layer cascaded segmentation network was used to accurately segment the most important joint surface and non joint surface areas for identifying fractures. Then, the images of the joint surface area and non joint surface area separately were classified and trained to distinguish fractures. Finally, based on the classification results of the two images, the normal or ABC fracture classification results could be comprehensively determined. The accuracy rates of normal, A-type, B-type, and C-type fracture on the test set were 0.99, 0.92, 0.91, and 0.82, respectively. For orthopedic medical experts, the average recognition accuracy rates were 0.98, 0.90, 0.87, and 0.81, respectively. The proposed automatic recognition method is generally better than experts, and can be used for preliminary auxiliary diagnosis of distal radius fractures in scenarios without expert participation.

Citation： LIU Yunpeng, GAN Kaifeng, LI Jin, SUN Dechao, QIU Hong, LIU Dongquan. Study on automatic and rapid diagnosis of distal radius fracture by X-ray. Journal of Biomedical Engineering, 2024, 41(4): 798-806. doi: 10.7507/1001-5515.202309050 Copy

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18.	Warin K, Limprasert W, Suebnukarn S, et al. Maxillofacial fracture detection and classification in computed tomography images using convolutional neural network-based models. Sci Rep, 2023, 13(1): 3434.
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26.	Zheng S, Lu J, Zhao H, et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 6881-6890.
27.	Kirillov A, Mintun E, Ravi N, et al. Segment anything. arXiv, 2023: 2304.02643.

1. Adigun O, Ronke B, Mohammed R, et al. Detection of fracture bones in X-ray images categorization. J Adv Math Comput Sci, 2020, 35: 1-11.
2. Hržić F, Štajduhar I, Tschauner S, et al. Local-entropy based approach for X-ray image segmentation and fracture detection. Entropy, 2019, 21(4): 338.
3. Kitamura G. Deep learning evaluation of pelvic radiographs for position, hardware presence and fracture detection. Eur J Radiol, 2020, 130: 109139.
4. Guan B, Zhang G, Yao J, et al. Arm fracture detection in X-rays based on improved deep convolutional neural network. Comput Electr Eng, 2020, 81: 106530.
5. Meena T, Roy S. Bone fracture detection using deep supervised learning from radiological images: a paradigm shift. Diagnostics, 2022, 12(10): 2420.
6. Guan B, Yao J, Wang S, et al. Automatic detection and localization of thighbone fractures in X-ray based on improved deep learning method. Comput Vis Image Underst, 2022, 216: 103345.
7. Kuo R Y L, Harrison C, Curran T A, et al. Artificial intelligence in fracture detection: a systematic review and meta-analysis. Radiology, 2022, 304(1): 50-62.
8. Krogue J D, Cheng K V, Hwang K M, et al. Automatic hip fracture identification and functional subclassification with deep learning. Radiol Artif Intell, 2020, 2(2): e190023.
9. Kim D H, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol, 2018, 73(5): 439-445.
10. Raisuddin A M, Vaattovaara E, Nevalainen M, et al. Critical evaluation of deep neural networks for wrist fracture detection. Sci Rep, 2021, 11(1): 1-11.
11. Hardalaç F, Uysal F, Peker O, et al. Fracture detection in wrist X-ray images using deep learning-based object detection models. Sensors, 2022, 22(3): 1285.
12. Anttila T T, Karjalainen T V, Mäkelä T O, et al. Detecting distal radius fractures using a segmentation-based deep learning model. J Digit Imaging, 2023, 36: 679-687.
13. Yahalomi E, Chernofsky M, Werman M. Detection of distal radius fractures trained by a small set of X-ray images and faster R-CNN. Adv Intell Syst Comput, 2019, 997: 1-9.
14. Min H, Rabi Y, Wadhawan A, et al. Automatic classification of distal radius fracture using a two-stage ensemble deep learning framework. Phys Eng Sci Med, 2023, 46(2): 877-886.
15. Gan K, Xu D, Lin Y, et al. Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments. Acta Orthop, 2019, 90(4): 394-400.
16. Suzuki T, Maki S, Yamazaki T, et al. Detecting distal radial fractures from wrist radiographs using a deep convolutional neural network with an accuracy comparable to hand orthopedic surgeons. J Digit Imaging, 2022, 35: 39-46.
17. Lee K M, Lee S Y, Han C S, et al. Long bone fracture type classification for limited number of CT data with deep learning// Proceedings of the 35th Annual ACM Symposium on Applied Computing. Brno: ACM, 2020: 1090-1095.
18. Warin K, Limprasert W, Suebnukarn S, et al. Maxillofacial fracture detection and classification in computed tomography images using convolutional neural network-based models. Sci Rep, 2023, 13(1): 3434.
19. Isola P, Zhu J Y, Zhou T H, et al. Image-to-image translation with conditional adversarial networks// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2020: 5967-5976.
20. Shi W Z, Caballero J, Huszár F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 1874-1883.
21. Oktay O, Schlemper J, Le Folgoc L L, et al. Attention U-Net: learning where to look for the pancreas. arXiv, 2018: 1804.03999.
22. Yang Y, Mehrkanoon S. AA-TransUNet: Attention augmented transunet for nowcasting tasks// International Joint Conference on Neural Networks. Padua: IEEE, 2022: 1-8.
23. Ma J, Wang B. Segment anything in medical images. arXiv, 2023: 2304.12306.
24. Cheng J, Tian S, Yu L, et al. ResGANet: residual group attention network for medical image classification and segmentation. Med Image Anal, 2022, 76: 102313.
25. Chen H, Sun K, Tian Z, et al. BlendMask: top-down meets bottom-up for instance segmentation// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 8573-8581.
26. Zheng S, Lu J, Zhao H, et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 6881-6890.
27. Kirillov A, Mintun E, Ravi N, et al. Segment anything. arXiv, 2023: 2304.02643.

Journal of Biomedical Engineering

Study on automatic and rapid diagnosis of distal radius fracture by X-ray

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