Deep learning provides favorable conditions for the application of artificial intelligence in the medical field, which has been widely used in medical images and has great application potential in the diagnosis and treatment of lower limb joint diseases. Deep learning techniques used to train and analyze images of the lower limb joints can be used in the clinical auxiliary diagnosis and offer a fresh approach to the study of joint diseases. For many tasks involving imaging of joint diseases, significant results have currently been attained. This paper reviews the development history of deep learning, describes the application progress of deep learning in lower limb joint images, expounds on the existing problems in the application of deep learning in the diagnosis and treatment of lower limb joint diseases, and looks forward to the future development direction.

Zhang Jingyi, Wang Youzhi, Li Jun. Application of Deep Learning in Lower Limb Joint Images. Academic Journal of Medicine & Health Sciences (2023) Vol. 4, Issue 1: 13-18. https://doi.org/10.25236/AJMHS.2023.040103.

[1] Hosny A, Parmar C, Quackenbush J, et al.Artificial intelligence in radiology [J] Nat Rev Cancer. 2018: 500-510.10.1038/s41568-018-0016-5

[2] Xu Song, Ye Zhewei.The application status and prospect ofartificial intelligence in orthopedics. [J]. Chinese Journal of Medicine. 2019: 117-119

[3] Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets [J]. Neural Comput, 2006, 18(7): 1527-1554.

[4] Zhang Junyang, Wang Huili, Guoyan, et al. Review of deep learning. [J]. Application Research of Computers. 2018: 1921-1928+1936

[5] Shen D, Wu G, Suk H I. Deep Learning in Medical Image Analysis [J]. Annu Rev Biomed Eng, 2017, 19: 221-248.

[6] Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks [J]. Communications of the ACM, 2017, 60(6): 84-90.

[7] Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs [J]. Jama, 2016, 316(22): 2402-2410.

[8] Esteva A, Kuprel B, Novoa R A, et al. Dermatologist-level classification of skin cancer with deep neural networks [J]. Nature, 2017, 542(7639): 115-118.

[9] Al-Masni M A, Al-Antari M A, Park J M, et al. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system [J]. Comput Methods Programs Biomed, 2018, 157: 85-94.

[10] Riggs B L, Melton L J, 3rd. The worldwide problem of osteoporosis: insights afforded by epidemiology [J]. Bone, 1995, 17(5 Suppl): 505s-511s.

[11] Han Xuekun, Yang Wengui. Prevention and treatment for hip fracture in the elderly [J]. Journal of Traumatic Surgery, 2020, 22(10): 798-801.

[12] Yamada Y, Maki S, Kishida S, et al. Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs [J]. Acta Orthop, 2020, 91(6): 699-704.

[13] Pejic A, Hansson S, Rogmark C. Magnetic resonance imaging for verifying hip fracture diagnosis why, when and how? [J]. Injury, 2017, 48(3): 687-691.

[14] Rehman H, Clement R G, Perks F, et al. Imaging of occult hip fractures: CT or MRI? [J]. Injury, 2016, 47(6): 1297-1301.

[15] Chellam W B. Missed subtle fractures on the trauma-meeting digital projector [J]. Injury, 2016, 47(3): 674-676.

[16] Parker M J. Missed hip fractures [J]. Arch Emerg Med, 1992, 9(1): 23-27.

[17] Szegedy C, Liu W, Jia Y, et al. Going Deeper with Convolutions [J]. 2014.

[18] Adams M, Chen W, Holcdorf D, et al. Computer vs human: Deep learning versus perceptual training for the detection of neck of femur fractures [J]. J Med Imaging Radiat Oncol, 2019, 63(1): 27-32.

[19] Badgeley M A, Zech J R, Oakden-Rayner L, et al. Deep learning predicts hip fracture using confounding patient and healthcare variables [J]. NPJ Digit Med, 2019, 2: 31.

[20] Urakawa T, Tanaka Y, Goto S, et al. Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network [J]. Skeletal Radiol, 2019, 48(2): 239-244.

[21] Mutasa S, Varada S, Goel A, et al. Advanced Deep Learning Techniques Applied to Automated Femoral Neck Fracture Detection and Classification [J]. J Digit Imaging, 2020, 33(5): 1209-1217.

[22] Bijlsma J W, Berenbaum F, Lafeber F P. Osteoarthritis: an update with relevance for clinical practice [J]. Lancet, 2011, 377(9783): 2115-2126.

[23] Abedin J, Antony J, Mcguinness K, et al. Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images [J]. Sci Rep, 2019, 9(1): 5761.

[24] Tiulpin A, Thevenot J, Rahtu E, et al. Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach [J]. Sci Rep, 2018, 8(1): 1727.

[25] Xue Y, Zhang R, Deng Y, et al. A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis [J]. PLoS One, 2017, 12(6): e0178992.

[26] Tack A, Mukhopadhyay A, Zachow S. Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative [J]. Osteoarthritis Cartilage, 2018, 26(5): 680-688.

[27] 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.

[28] Norman B, Pedoia V, Majumdar S. Use of 2D U-Net Convolutional Neural Networks for Automated Cartilage and Meniscus Segmentation of Knee MR Imaging Data to Determine Relaxometry and Morphometry [J]. Radiology, 2018, 288(1): 177-185.

[29] Borjali A, Chen A F, Muratoglu O K, et al. Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network [J]. J Orthop Res, 2020, 38(7): 1465-1471.