Magnetic resonance imaging diagnosis of ankle joint athletic injury based on machine learning algorithms
Abstract
The diagnosis of ankle joint athletic injuries using traditional magnetic resonance imaging (MRI) relies on the subjective judgment and experience of doctors, and small structural changes in athletic injuries are difficult to accurately detect and diagnose. By using machine learning (ML) algorithms and image processing techniques to obtain objective and consistent diagnostic results, the accuracy of diagnosing ankle joint athletic injuries can be improved. This article collected a large number of MRI images of ankle joint athletic injuries, and preprocessed the collected images to extract morphological and texture features, and perform feature fusion. The Residual Network (ResNet) was improved, and the Leaky linear rectification function (ReLU, Corrected linear unit) activation function was introduced. The transfer learning was utilized to increase the convergence speed of the model, and the global maximum pooling layer and softmax classifier were used to construct the fully connected layer. After sufficient training on the training set, the findings on the test set indicated that the average accuracy of the improved ResNet model for ankle joint injury classification was 98.3%. The use of an improved ResNet model can effectively improve the diagnostic effectiveness of ankle joint athletic injuries, providing a new method for medical diagnosis of MRI.
References
1. Herzog, Mackenzie M., Zachary Y. Kerr, Stephen W. Marshall, Erik A. Wikstrom. "Epidemiology of ankle sprains and chronic ankle instability." Journal of athletic training 54.6 (2019): 603-610, DOI: https://doi.org/10.4085/1062-6050-447-17.
2. Vosseller, J. Turner, Elizabeth R. Dennis, and Shaw Bronner. "Ankle injuries in dancers." JAAOS-Journal of the American Academy of Orthopaedic Surgeons 27.16 (2019): 582-589, DOI: 10.5435/JAAOS-D-18-00596.
3. Stupic, Karl F., Maureen Ainslie, Michael A. Boss, Cecil Charles, Andrew M. Dienstfrey, Jeffrey L. Evelhoch, et al. "A standard system phantom for magnetic resonance imaging." Magnetic resonance in medicine 86.3 (2021): 1194-1211, DOI: https://doi.org/10.1002/mrm.28779.
4. Knoll, Florian, Kerstin Hammernik, Chi Zhang, Steen Moeller, Thomas Pock, Daniel K. Sodickson, et al. "Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues." IEEE signal processing magazine 37.1 (2020): 128-140, DOI: 10.1109/MSP.2019.2950640.
5. Sun Yulong. "CT image post-processing combined with magnetic resonance technology for the classification, diagnosis, and prognosis analysis of ankle joint fracture patients." Heilongjiang Medical Journal 45.17 (2021): 1869-1870.
6. Chen Jian. "Comparative study of multi row spiral CT combined with MRI in the diagnosis of lateral ligament injury of the ankle joint." Chinese Medical Equipment 34.3 (2019): 64-67.
7. Zhang Qianying, Liu Xuhong, Huang Ying, Han Xiaobing, Zhang Sizhu, A Huan, et al. "The effect of magnetic resonance three-dimensional water excitation sequence on ankle joint cartilage imaging." Journal of Molecular Imaging 45.4 (2022): 572-575.
8. Ye Yunkai. "Analysis of the Clinical Significance of High Field Magnetic Resonance (MR) Diagnosis of Ankle Joint Anterolateral Impact Syndrome." Chinese Medical Device Information 24.23 (2018): 41-43.
9. Gorbachova, Tetyana. "Magnetic resonance imaging of the ankle and foot." Polish Journal of Radiology 85.1 (2020): 532-549, DOI: https://doi.org/10.5114/pjr.2020.99472.
10. Alves, Timothy, Qian Dong, Jon Jacobson, Corrie Yablon, Girish Gandikota. "Normal and injured ankle ligaments on ultrasonography with magnetic resonance imaging correlation." Journal of Ultrasound in Medicine 38.2 (2019): 513-528, DOI: https://doi.org/10.1002/jum.14716.
11. Sawant, Yogini Nilkantha, and Darshana Sanghvi. "Magnetic resonance imaging of ankle ligaments: A pictorial essay." Indian Journal of Radiology and Imaging 28.04 (2018): 419-426, DOI: 10.4103/ijri.IJRI_77_16.
12. Warner, Stephen J., Matthew R. Garner, Dean G. Lorich. "The diagnostic accuracy of radiographs and magnetic resonance imaging in predicting deltoid ligament ruptures in ankle fractures." HSS Journal® 15.2 (2019): 115-121, DOI: https://doi.org/10.1007/s11420-018-09655-x.
13. Pan Yaling, Wang Hanqi, and Lu Yong. "The Application of Artificial Intelligence in Medical Imaging CAD." International Journal of Medical Radiology 42.1 (2019): 3-7.
14. Liu Wenguang, Xie Simin, Zhou Yafang, Hu Jiaxi, Li Mengsi, Li Wenzheng, et al. "Machine learning and its research progress in imaging diagnosis of liver diseases." International Journal of Medical Radiology 42.1 (2019): 16-21.
15. Giger, Maryellen L. "Machine learning in medical imaging." Journal of the American College of Radiology 15.3 (2018): 512-520, DOI: https://doi.org/10.1016/j.jacr.2017.12.028.
16. Rana, Meghavi, and Megha Bhushan. "Machine learning and deep learning approach for medical image analysis: diagnosis to detection." Multimedia Tools and Applications 82.17 (2023): 26731-26769.
17. Willemink, Martin J., Wojciech A. Koszek, Cailin Hardell, Jie Wu, Dominik Fleischmann, Hugh Harvey, et al. "Preparing medical imaging data for machine learning." Radiology 295.1 (2020): 4-15, DOI: https://doi.org/10.1148/radiol.2020192224.
18. Yadav, Samir S., and Shivajirao M. Jadhav. "Deep convolutional neural network based medical image classification for disease diagnosis." Journal of Big data 6.1 (2019): 1-18.
19. Sarvamangala, D. R., and Raghavendra V. Kulkarni. "Convolutional neural networks in medical image understanding: a survey." Evolutionary intelligence 15.1 (2022): 1-22.
20. Poedjiastoeti, Wiwiek, and Siriwan Suebnukarn. "Application of convolutional neural network in the diagnosis of jaw tumors." Healthcare informatics research 24.3 (2018): 236-241, DOI: https://doi.org/10.4258/hir.2018.24.3.236.
21. Chen Mingliang, Gu Chengyi, Xu Liuhai, Zhou You. "Research progress in the diagnosis and treatment of lateral collateral ligament injury in the ankle joint." Chinese Journal of Sports Medicine 38.2 (2019): 152-158.
22. Chen Xin, Shi Shaoyun, Chen Xiuqing, Hu Baijun, Chen Shurong. "The application of exercise therapy in the rehabilitation treatment of ankle joint injuries." Chinese Journal of Bone and Joint Injury 33.8 (2018): 892-893.
23. Gulbrandsen, Matthew, David E. Hartigan, Karan A. Patel, Justin L Makovicka, Sailesh V Tummala, Anikar Chhabra, et al. "Ten-year epidemiology of ankle injuries in men's and women's collegiate soccer players." Journal of athletic training 54.8 (2019): 881-888, DOI: https://doi.org/10.4085/1062-6050-144-18.
24. Skazalski, Christopher, Jacek Kruczynski, Martin Aase Bahr, Tone Bere, Rod Whiteley, Roald Bahr, et al. "Landing-related ankle injuries do not occur in plantarflexion as once thought: a systematic video analysis of ankle injuries in world-class volleyball." British journal of sports medicine 52.2 (2018): 74-82, DOI: http://dx.doi.org/10.1136/bjsports-2016-097155.
25. Drost, Frank-Jan H., Daniel Osses, Daan Nieboer, Chris H. Bangma, Ewout W. Steyerberg, Monique J. Roobol, et al. "Prostate magnetic resonance imaging, with or without magnetic resonance imaging-targeted biopsy, and systematic biopsy for detecting prostate cancer: a Cochrane systematic review and meta-analysis." European urology 77.1 (2020): 78-94, DOI: https://doi.org/10.1016/j.eururo.2019.06.023.
26. Keenan, Kathryn E., Maureen Ainslie, Alex J. Barker, Michael A. Boss, Kim M. Cecil, Cecil Charles, et al. "Quantitative magnetic resonance imaging phantoms: a review and the need for a system phantom." Magnetic resonance in medicine 79.1 (2018): 48-61, DOI: https://doi.org/10.1002/mrm.26982.
27. Zhao, Kai, Li-Guo Tan, and Shen-Min Song. "Gaussian filter for nonlinear networked systems with synchronously correlated noises and one-step randomly delayed measurements and multiple packet dropouts." IEEE Sensors Journal 19.20 (2019): 9271-9281.
28. Li Jian, Ding Xiaoqi, Chen Guang, Sun Yang, Jiang Nan. "A denoising method for leaf images based on improved Gaussian filtering algorithm." Southern Agricultural Journal 50.6 (2019): 1385-1391.
29. Boroumand, Mehdi, Mo Chen, and Jessica Fridrich. "Deep residual network for steganalysis of digital images." IEEE Transactions on Information Forensics and Security 14.5 (2018): 1181-1193.
30. Wang, Ruhua, Wanquan Liu. "Deep residual network framework for structural health monitoring." Structural Health Monitoring 20.4 (2021): 1443-1461, DOI: https://doi.org/10.1177/1475921720918378.
31. Dhillon, Anamika, and Gyanendra K. Verma. "Convolutional neural network: a review of models, methodologies and applications to object detection." Progress in Artificial Intelligence 9.2 (2020): 85-112.
32. Cong, Iris, Soonwon Choi, and Mikhail D. Lukin. "Quantum convolutional neural networks." Nature Physics 15.12 (2019): 1273-1278.
33. Wang Ke, and Zhang Genyao. "Thyroid SPECT imaging diagnosis based on ResNet model." Journal of Hebei University of Science and Technology 41.3 (2020): 242-248.
34. Zheng Yao, Zhang Ye, Du Peng, Zhang Wenli, Liu Yang, Zhang Xi, et al. "Research on the application of ResNet model construction based on T2W magnetic resonance imaging in the double objective prediction of bladder cancer grading and staging." China Medical Equipment 17.8 (2020): 1-4.
35. Zheng Qiumei, Tan Dan, and Wang Fenghua. "Research on Traffic Sign Recognition Based on Improved ResNet Networks." Computer and Digital Engineering 49.5 (2021): 947-951.
36. Jin Ying, Ye Sa, and Li Honglei. "Research on an Intelligent Diagnosis Model for Fruit Tree Diseases Based on ResNet-50 Deep Convolutional Network." Journal of Agricultural Library and Information Science 33.4 (2021): 58-67.
37. Maican, C. I., Sumedrea, S., Tecau, A., Nichifor, E., Chitu, I. B., Lixandroiu, R., & Bratucu, G. (2023). Factors Influencing the Behavioural Intention to Use AI-Generated Images in Business: A UTAUT2 Perspective with Moderators. Journal of Organizational and End User Computing, 35(1), 1-32.
38. Wu, J., & Zhang, K. (2022). Machine Learning Algorithms for Big Data Applications with Policy Implementation. Journal of Organizational and End User Computing, 34(3), 1-13.
Copyright (c) 2024 Hongxia Han, Yuanwei Li
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
