Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future
Source abstract
Abstract
Since its development, deep learning has been quickly incorporated into the field of medicine and has had a profound impact. Since 2017, many studies applying deep learning-based diagnostics in the field of orthopedics have demonstrated outstanding performance. However, most published papers have focused on disease detection or classification, leaving some unsatisfactory reports in areas such as segmentation and prediction. This review introduces research published in the field of orthopedics classified according to disease from the perspective of orthopedic surgeons, and areas of future research are discussed. This paper provides orthopedic surgeons with an overall understanding of artificial intelligence-based image analysis and the information that medical data should be treated with low prejudice, providing developers and researchers with insight into the real-world context in which clinicians are embracing medical artificial intelligence.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Medical Imaging and Analysis · Artificial Intelligence in Healthcare and Education · AI in cancer detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Orthopaedic Surgery, School of Medicine, Konkuk University, 120-1 Neungdong-ro (Hwayang-dong), Seoul 143-729, Korea
References (78)
Show source-provided references
1. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex10.1113/jphysiol.1962.sp006837 ↗
2. Multilayer feedforward networks are universal approximators10.1016/0893-6080(89)90020-8 ↗
3. Object detection with deep learning: A review10.1109/TNNLS.2018.2876865 ↗
4. Deep learning in medicine—Promise, progress, and challenges10.1001/jamainternmed.2018.7117 ↗
5. A survey on active learning and human-in-the-loop deep learning for medical image analysis10.1016/j.media.2021.102062 ↗
6. AI-based computer-aided diagnosis (AI-CAD): The latest review to read first10.1007/s12194-019-00552-4 ↗
7. AI applications to medical images: From machine learning to deep learning10.1016/j.ejmp.2021.02.006 ↗
8. A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI10.1109/TNNLS.2020.3027314 ↗
9. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016, January 30). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA.10.1109/CVPR.2016.319 ↗
10. Automated rotator cuff tear classification using 3D convolutional neural network10.1038/s41598-020-72357-0 ↗
11. Singh, A., Sengupta, S., and Lakshminarayanan, V. (2020). Explainable Deep Learning Models in Medical Image Analysis. J. Imaging, 6.10.3390/jimaging6060052 ↗
12. Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., and Viegas, F. (2018, January 10–15). Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). Proceedings of the 35th International Conference on Machine Learning, PMLR, Stockholm, Sweden.
13. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs10.1001/jama.2016.17216 ↗
14. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer10.1001/jama.2017.14585 ↗
15. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm10.1080/17453674.2018.1453714 ↗
16. An exemplar pyramid feature extraction based humerus fracture classification method10.1016/j.mehy.2020.109663 ↗
17. Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network10.1007/s00256-018-3016-3 ↗
18. Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: Ensemble decision-making with antero-posterior and lateral radiographs10.1080/17453674.2020.1803664 ↗
19. Classification of femur fracture in pelvic X-ray images using meta-learned deep neural network
20. Lind, A., Akbarian, E., Olsson, S., Nåsell, H., Sköldenberg, O., Razavian, A.S., and Gordon, M. (2021). Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system. PLoS ONE, 16.10.1371/journal.pone.0248809 ↗
21. Sanders classification of calcaneal fractures in CT images with deep learning and differential data augmentation techniques10.1016/j.injury.2020.09.010 ↗
22. Ozkaya, E., Topal, F.E., Bulut, T., Gursoy, M., Ozuysal, M., and Karakaya, Z. (2020). Evaluation of an artificial intelligence system for diagnosing scaphoid fracture on direct radiography. Eur. J. Trauma Emerg. Surg., 1–8.10.1007/s00068-020-01468-0 ↗
23. Is deep learning on par with human observers for detection of radiographically visible and occult fractures of the scaphoid?10.1097/CORR.0000000000001318 ↗
24. Chen, H.-Y., Hsu, B.W.-Y., Yin, Y.-K., Lin, F.-H., Yang, T.-H., Yang, R.-S., Lee, C.-K., and Tseng, V.S. (2021). Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs. PLoS ONE, 16.10.1371/journal.pone.0245992 ↗
25. Using artificial intelligence to diagnose fresh osteoporotic vertebral fractures on magnetic resonance images10.1016/j.spinee.2021.03.006 ↗
26. Ecaluation of focal bone mineral density using three-dimensional of Hounsfield units in the proximal humerus
27. Xue, Y., Zhang, R., Deng, Y., Chen, K., and Jiang, T. (2017). A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis. PLoS ONE, 12.10.1371/journal.pone.0178992 ↗
28. Detection of hip osteoarthritis by using plain pelvic radiographs with deep learning methods10.1007/s00256-020-03433-9 ↗
29. Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach10.1038/s41598-018-20132-7 ↗
30. Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists10.1016/j.compbiomed.2021.104334 ↗
31. Diagnosing osteoarthritis from T2 maps using deep learning: An analysis of the entire Osteoarthritis Initiative baseline cohort10.1016/j.joca.2019.02.800 ↗
32. Kim, D.H., Lee, K.J., Choi, D., Lee, J.I., Choi, H.G., and Lee, Y.S. (2020). Can Additional Patient Information Improve the Diagnostic Performance of Deep Learning for the Interpretation of Knee Osteoarthritis Severity. J. Clin. Med., 9.10.3390/jcm9103341 ↗
33. Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Knee10.1016/j.arth.2020.10.021 ↗
34. Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network10.1002/jor.24617 ↗
35. Machine learning–based identification of hip arthroplasty designs
36. Classifying shoulder implants in X-ray images using deep learning10.1016/j.csbj.2020.04.005 ↗
37. Sultan, H., Owais, M., Park, C., Mahmood, T., Haider, A., and Park, K.R. (2021). Artificial Intelligence-Based Recognition of Different Types of Shoulder Implants in X-ray Scans Based on Dense Residual Ensemble-Network for Personalized Medicine. J. Pers. Med., 11.10.3390/jpm11060482 ↗
38. A review of semantic segmentation using deep neural networks10.1007/s13735-017-0141-z ↗
39. Skeletal bone age prediction based on a deep residual network with spatial transformer10.1016/j.cmpb.2020.105754 ↗
40. Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning10.1016/j.cmpb.2019.105063 ↗
41. Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets10.1007/s00330-020-07070-7 ↗
42. Deep learning method for segmentation of rotator cuff muscles on MR images10.1007/s00256-020-03599-2 ↗
43. Lee, K., Kim, J.Y., Lee, M.H., Choi, C.-H., and Hwang, J.Y. (2021). Imbalanced Loss-Integrated Deep-Learning-Based Ultrasound Image Analysis for Diagnosis of Rotator-Cuff Tear. Sensors, 21.10.3390/s21062214 ↗
44. Automatic knee meniscus tear detection and orientation classification with Mask-RCNN10.1016/j.diii.2019.03.002 ↗
45. Artificial intelligence to diagnose meniscus tears on MRI10.1016/j.diii.2019.02.007 ↗
46. Deep Learning for Detection of Complete Anterior Cruciate Ligament Tear10.1007/s10278-019-00193-4 ↗
47. Automated magnetic resonance image segmentation of the anterior cruciate ligament10.1002/jor.24926 ↗
48. Skeletal growth estimation using radiographic image processing and analysis10.1109/4233.897061 ↗
49. Disparity between clinical and ultrasound examinations in neonatal hip screening
50. Clinical application of artificial intelligence-assisted diagnosis using anteroposterior pelvic radiographs in children with developmental dysplasia of the hip10.1302/0301-620X.102B11.BJJ-2020-0712.R2 ↗
51. The ulnar collateral ligament is always torn in the posterolateral elbow dislocation: A suggestion on the new mechanism of dislocation using MRI findings
52. Detection of Traumatic Pediatric Elbow Joint Effusion Using a Deep Convolutional Neural Network10.2214/AJR.18.19974 ↗
53. Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study10.1016/j.bone.2020.115561 ↗
54. Yamamoto, N., Sukegawa, S., Kitamura, A., Goto, R., Noda, T., Nakano, K., Takabatake, K., Kawai, H., Nagatsuka, H., and Kawasaki, K. (2020). Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates. Biomolecules, 10.10.3390/biom10111534 ↗
55. Automated measurement of hip–knee–ankle angle on the unilateral lower limb X-rays using deep learning10.1007/s13246-020-00951-7 ↗
56. A Deep Learning Tool for Automated Radiographic Measurement of Acetabular Component Inclination and Version After Total Hip Arthroplasty10.1016/j.arth.2021.02.026 ↗
57. Chronic osteomyelitis of the lumbar transverse process
58. A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks10.1016/j.compbiomed.2017.03.024 ↗
59. Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data10.1016/j.media.2018.07.008 ↗
60. Performance of the deep convolutional neural network based magnetic resonance image scoring algorithm for differentiating between tuberculous and pyogenic spondylitis10.1038/s41598-018-31486-3 ↗
61. Spinal Stenosis Grading in Magnetic Resonance Imaging Using Deep Convolutional Neural Networks10.1097/BRS.0000000000003377 ↗
62. Deep Learning Artificial Intelligence Model for Assessment of Hip Dislocation Risk Following Primary Total Hip Arthroplasty From Postoperative Radiographs10.1016/j.arth.2021.02.028 ↗
63. DA-CapsNet: Dual attention mechanism capsule network10.1038/s41598-020-68453-w ↗
64. Automated segmentation of 2D low-dose CT images of the psoas-major muscle using deep convolutional neural networks10.1007/s12194-019-00512-y ↗
65. Fully automatic segmentation of paraspinal muscles from 3D torso CT images via multi-scale iterative random forest classifications10.1007/s11548-018-1852-1 ↗
66. Medical Image Segmentation based on U-Net: A Review10.2352/J.ImagingSci.Technol.2020.64.2.020508 ↗
67. Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal Modeling10.1109/TMI.2019.2940555 ↗
68. Attention gated networks: Learning to leverage salient regions in medical images10.1016/j.media.2019.01.012 ↗
69. USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets10.1016/j.neucom.2019.07.006 ↗
70. Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy10.1016/j.compbiomed.2021.104815 ↗
71. Machine learning and medical education10.1038/s41746-018-0061-1 ↗
72. Nie, D., Trullo, R., Lian, J., Petitjean, C., Ruan, S., Wang, Q., and Shen, D. (2017, January 11–13). Medical image synthesis with context-aware generative adversarial networks. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec City, QC, Canada.10.1007/978-3-319-66179-7_48 ↗
73. Deep Learning Applications in Medical Image Analysis10.1109/ACCESS.2017.2788044 ↗
74. The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database10.1038/s41746-020-00324-0 ↗
75. Welcoming new guidelines for AI clinical research10.1038/s41591-020-1042-x ↗
76. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI Extension10.1136/bmj.m3164 ↗
77. Deep learning for accurately recognizing common causes of shoulder pain on radiographs10.1007/s00256-021-03740-9 ↗
78. Evaluating subscapularis tendon tears on axillary lateral radiographs using deep learning10.1007/s00330-021-08034-1 ↗