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Research progress of deep learning in sports injuries of bone and joint based on MRI
NI Ming  YUAN Huishu 

Cite this article as: Ni M, Yuan HS. Research progress of deep learning in sports injuries of bone and joint based on MRI[J]. Chin J Magn Reson Imaging, 2021, 12(8): 118-120. DOI:10.12015/issn.1674-8034.2021.08.028.


[Abstract] Deep learning is a hot direction in bone and joint imaging research. Unlike traditional machine learning, deep learning allows to learn data of different abstract levels directly through models constructed by multiple processing layers. At present, deep learning is mainly used in bone age measurement, fracture detection and osteoporosis research in the bone and joint system, while MRI-based research on bone and joint sports injuries is very rare, and the related research is still in its infancy. This study summarizes the research progress of deep learning based on MRI in bone and joint injuries, hoping to promote research in this field.
[Keywords] artificial intelligence；deep learning；magnetic resonance imaging；skeletal muscle；sports injury

NI Ming   YUAN Huishu*  

Department of Radiology, Peking University Third Hospital, Beijing 100191, China

Yuan HS, E-mail: huishuy@bjmu.edu.cn

Conflicts of interest   None.

Received  2021-02-25
Accepted  2021-03-18
DOI: 10.12015/issn.1674-8034.2021.08.028
Cite this article as: Ni M, Yuan HS. Research progress of deep learning in sports injuries of bone and joint based on MRI[J]. Chin J Magn Reson Imaging, 2021, 12(8): 118-120. DOI:10.12015/issn.1674-8034.2021.08.028.

1
Yasaka K, Akai H, Kunimatsu A, et al. Deep learning with convolutional neural network in radiology[J]. Japan J Radiol, 2018, 36(4): 257-272. DOI: 10.1007/s11604-018-0726-3.
2
Zheng F, Chen XZ. Status of artificial intelligence in meningioma image[J]. Chin J Magn Reson Imaging, 2020, 11(10): 934-936. DOI: 10.12015/issn.1674-8034.2020.10.025.
3
Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications[J]. CA Cancer J Clin, 2019, 69(2): 127-157. DOI: 10.3322/caac.21552.
4
Zhu WZ, Hu QJ. The fusion development of artificial intelligence and medical imaging: opportunities and challenges[J]. Radiol Pract, 2019, 34(9): 938-941. DOI: 10.13609/j.cnki.1000-0313.2019.09.001.
5
Quatman CE, Hettrich CM, Schmitt LC, et al. The clinical utility and diagnostic performance of magnetic resonance imaging for identification of early and advanced knee osteoarthritis: a systematic review[J]. Am J Sports Med, 2011, 39(7): 1557-1568. DOI: 10.1177/0363546511407612.
6
Cheng Q, Zhao FC. Comparison of 1.5- and 3.0-T magnetic resonance imaging for evaluating lesions of the knee: A systematic review and meta-analysis (PRISMA-compliant article)[J]. Medicine, 2018, 97(38): e12401. DOI: 10.1097/md.0000000000012401.
7
Coris EE, Zwygart K, Fletcher M, et al. Imaging in sports medicine: an overview[J]. Sports medicine and arthroscopy review, 2009, 17(1): 2-12. DOI: 10.1097/JSA.0b013e318195ffb0.
8
Zhou Z, Zhao G, Kijowski R, et al. Deep convolutional neural network for segmentation of knee joint anatomy[J]. Magn Reson Med, 2018, 80(6): 2759-2770. DOI: 10.1002/mrm.27229.
9
Gaj S, Yang M, Nakamura K, et al. Automated cartilage and meniscus segmentation of knee MRI with conditional generative adversarial networks[J]. Magn Reson Med, 2020, 84(1): 437-449. DOI: 10.1002/mrm.28111.
10
Kashyap S, Zhang H, Rao K, et al. Learning-based cost functions for 3-D and 4-D multi-surface multi-object segmentation of knee MRI: Data from the osteoarthritis initiative[J]. IEEE Trans Med Imaging, 2018, 37(5): 1103-1113. DOI: 10.1109/tmi.2017.2781541.
11
Liu F. SUSAN: segment unannotated image structure using adversarial network[J]. Magn Reson Med, 2019, 81(5): 3330-3345. DOI: 10.1002/mrm.27627.
12
Zhang K, Lu W, Marziliano P. Automatic knee cartilage segmentation from multi-contrast MR images using support vector machine classification with spatial dependencies[J]. Magn Reson Imaging, 2013, 31(10): 1731-1743. DOI: 10.1016/j.mri.2013.06.005.
13
Byra M, Wu M, Zhang X, et al. Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U-Net with transfer learning[J]. Magn Reson Med, 2020, 83(3): 1109-1122. DOI: 10.1002/mrm.27969.
14
Wang G, Han Y. Convolutional neural network for automatically segmenting magnetic resonance images of the shoulder joint[J]. Comput Methods Programs Biomed, 2021, 200: 105862. DOI: 10.1016/j.cmpb.2020.105862.
15
Medina G, Buckless CG, Thomasson E, et al. Deep learning method for segmentation of rotator cuff muscles on MR images[J]. Skeletal Radiol, 2021, 50(4): 683-692. DOI: 10.1007/s00256-020-03599-2.
16
Musahl V, Karlsson J. Anterior cruciate ligament tear[J]. N Engl J Med, 2019, 380(24): 2341-2348. DOI: 10.1056/NEJMcp1805931.
17
Halabchi F, Hassabi M. Acute ankle sprain in athletes: Clinical aspects and algorithmic approach[J]. World J Orthop, 2020, 11(12): 534-558. DOI: 10.5312/wjo.v11.i12.534.
18
Bien N, Rajpurkar P, Ball RL, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet[J]. PLoS Med, 2018, 15(11): e1002699. DOI: 10.1371/journal.pmed.1002699.
19
Štajduhar I, Mamula M, Miletić D, et al. Semi-automated detection of anterior cruciate ligament injury from MRI[J]. Comput Methods Programs Biomed, 2017, 140: 151-164. DOI: 10.1016/j.cmpb.2016.12.006.
20
Chang PD, Wong TT, Rasiej MJ. Deep learning for detection of complete anterior cruciate ligament tear[J]. J Digit Imaging, 2019, 32(6): 980-986. DOI: 10.1007/s10278-019-00193-4.
21
Namiri NK, Flament I, Astuto B, et al. Deep learning for hierarchical severity staging of anterior cruciate ligament injuries from MRI[J]. Radiol Artif Intell, 2020, 2(4): e190207. DOI: 10.1148/ryai.2020190207.
22
Liu F, Guan B, Zhou Z, et al. Fully automated diagnosis of anterior cruciate ligament tears on knee MR images by using deep learning[J]. Radiol Artif Intell, 2019, 1(3): 180091. DOI: 10.1148/ryai.2019180091.
23
Zhang L, Li M, Zhou Y, et al. Deep learning approach for anterior cruciate ligament lesion detection: Evaluation of diagnostic performance using arthroscopy as the reference standard[J]. J Magn Reson Imaging, 2020, 52(6): 1745-1752. DOI: 10.1002/jmri.27266.
24
Bahr R, Clarsen B, Derman W, et al. International olympic committee consensus statement: methods for recording and reporting of epidemiological data on injury and illness in sport 2020 (including STROBE Extension for Sport Injury and Illness Surveillance (STROBE-SIIS))[J]. Br J Sports Med, 2020, 54(7): 372-389. DOI: 10.1136/bjsports-2019-101969.
25
Pedoia V, Norman B, Mehany SN, et al. 3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects [J]. J Magn Reson Imaging, 2019, 49(2): 400-410. DOI: 10.1002/jmri.26246.
26
Couteaux V, Si-Mohamed S, Nempont O, et al. Automatic knee meniscus tear detection and orientation classification with Mask-RCNN[J]. Diagn Interv Imaging, 2019, 100(4): 235-242. DOI: 10.1016/j.diii.2019.03.002.
27
Pochon L, Peterson CK, Sutter R, et al. Hip MRI findings and outcomes following imaging-guided hip injections[J]. Br J Radiol, 2020, 93(1108): 20190817. DOI: 10.1259/bjr.20190817.
28
White CL, Chauvin NA, Waryasz GR, et al. MRI of native knee cartilage delamination injuries[J]. AJR Am J Roentgenol, 2017, 209(5): W317-W321. DOI: 10.2214/ajr.16.17708.
29
Krych AJ, Saris DBF, Stuart MJ, et al. Cartilage injury in the knee: Assessment and treatment options[J]. J Am Acad Orthop Surg, 2020, 28(22): 914-922. DOI: 10.5435/jaaos-d-20-00266.
30
Fritz RC, Chaudhari AS, Boutin RD. Preoperative MRI of articular cartilage in the knee: A practical approach[J]. J Knee Surg, 2020, 33(11): 1088-1099. DOI: 10.1055/s-0040-1716719.
31
Liu F, Zhou Z, Samsonov A, et al. Deep learning approach for evaluating knee MR images: Achieving high diagnostic performance for cartilage lesion detection[J]. Radiology, 2018, 289(1): 160-169. DOI: 10.1148/radiol.2018172986.
32
Yang GC, Zhong YQ, Zhu Y, et al. Diagnostic efficiency of multi-active convolutional neural network automatic learning model on knee cartilage damage[J]. Shandong Med J, 2019, 59(35): 29-32. DOI: 10.3969/j.issn.1002-266X.2019.35.007.
33
Si LP, Xuan K, Yao WW. Automatic segmentation and classification evaluation based on semi-quantitative score of magnetic resonance imaging of knee articular cartilage[J]. Chin J Magn Reson Imaging, 2018, 9(12): 928-935. DOI: 10.12015/issn.1674-8034.2018.12.009.
34
Shim E, Kim JY, Yoon JP, et al. Automated rotator cuff tear classification using 3D convolutional neural network[J]. Sci Rep, 2020, 10(1): 15632. DOI: 10.1038/s41598-020-72357-0.
35
Xiao Y, Liu SY. Objectively to treat the role of artificial intelligence in medical imaging[J]. Radiol Pract, 2018, 33(10): 992-994. DOI: 10.13609/j.cnki.1000-0313.2018.10.002.
36
Jin ZY. Application of artificial intelligence in medical imaging: reality and challenge[J]. Radiol Pract, 2018, 33(10): 989-991. DOI: 10.13609/j.cnki.1000-0313.2018.10.001.

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