| 1 |
Dinescu SC, Stoica D, Bita CE, et al. Applications of artificial intelligence in musculoskeletal ultrasound: narrative review[J]. Front Med (Lausanne), 2023, 10: 1286085.
|
| 2 |
Shin Y, Yang J, Lee YH, et al. Artificial intelligence in musculoskeletal ultrasound imaging[J]. Ultrasonography, 2021, 40(1): 30-44.
|
| 3 |
Yan L, Li Q, Fu K, et al. Progress in the application of artificial intelligence in ultrasound-assisted medical diagnosis[J]. Bioengineering (Basel), 2025, 12(3): 288.
|
| 4 |
Xin C, Li B, Wang D, et al. Deep learning for the rapid automatic segmentation of forearm muscle boundaries from ultrasound datasets[J]. Front Physiol, 2023, 14: 1166061.
|
| 5 |
Vafaeezadeh M, Behnam H, Gifani P. Ultrasound image analysis with vision transformers-review[J]. Diagnostics (Basel), 2024, 14(5): 542.
|
| 6 |
Kumar R, Sporn K, Borole A, et al. Biomarker-guided imaging and AI-augmented diagnosis of degenerative joint disease[J]. Diagnostics (Basel), 2025, 15(11): 1418.
|
| 7 |
Zhou W, Zhou C, Hu L, et al. Automated elbow ultrasound image recognition: a two-stage deep learning system via Swin Transformer[J]. Quant Imaging Med Surg, 2025, 15(1): 731-740.
|
| 8 |
Shin Y, Yang J, Lee YH. Deep generative adversarial networks: applications in musculoskeletal imaging[J]. Radiol Artif Intell, 2021, 3(3): e200157.
|
| 9 |
Cronin NJ, Finni T, Seynnes O. Using deep learning to generate synthetic B-mode musculoskeletal ultrasound images[J]. Comput Methods Programs Biomed, 2020, 196: 105583.
|
| 10 |
Dietrichson F, Smistad E, Ostvik A, et al. Ultrasound speckle reduction using generative adversial networks[C]. Kobe, Japan: 2018 IEEE International Ultrasonics Symposium (IUS), 2018: 1-4.
|
| 11 |
Hyun D, Brickson LL, Looby KT, et al. Beamforming and speckle reduction using neural networks[J]. IEEE Trans Ultrason Ferroelectr Freq Control, 2019, 66(5): 898-910.
|
| 12 |
Moinuddin M, Khan S, Alsaggaf AU, et al. Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network[J]. Front Physiol, 2022, 13: 961571.
|
| 13 |
Jiang H, Xia S, Yang Y, et al. Transforming free-text radiology reports into structured reports using ChatGPT: A study on thyroid ultrasonography[J]. Eur J Radiol, 2024, 175: 111458.
|
| 14 |
Jiao J, Zhou J, Li X, et al. USFM: A universal ultrasound foundation model generalized to tasks and organs towards label efficient image analysis[J]. Med Image Anal, 2024, 96: 103202.
|
| 15 |
Knight J, Zhou Y, Keen C, et al. 2D/3D ultrasound diagnosis of pediatric distal radius fractures by human readers vs artificial intelligence[J]. Sci Rep, 2023, 13(1): 14535.
|
| 16 |
Ho TT, Kim GT, Kim T, et al. Classification of rotator cuff tears in ultrasound images using deep learning models[J]. Med Biol Eng Comput, 2022, 60(5): 1269-1278.
|
| 17 |
Alzyadat T, Praet S, Chetty G, et al. Automatic segmentation of achilles tendon tissues using deep convolutional neural network, machine learning in medical imaging[C]. Lima, Peru: Machine Learning in Medical Imaging: 11th International Workshop, 2020: 444-454.
|
| 18 |
Jahanifar M, Zamani Tajeddin N, et al. Automatic recognition of the supraspinatus tendinopathy from ultrasound images using convolutional neural networks[DB/OL]. arXiv preprint, 2020.
URL
|
| 19 |
Chiu PH, Boudier-Revéret M, Chang SW, et al. Deep learning for detecting supraspinatus calcific tendinopathy on ultrasound images[J]. J Med Ultrasound, 2022, 30(3): 196-202.
|
| 20 |
Özçakar L. AI (as an ally) for musculoskeletal ultrasound in PRM- haute couture after renaissance[J]. Am J Phys Med Rehabil, 2024, 103(11): 967-969.
|
| 21 |
He X, Wang M, Zhao C, et al. Deep learning-based automatic scoring models for the disease activity of rheumatoid arthritis based on multimodal ultrasound images[J]. Rheumatology (Oxford), 2024, 63(3): 866-873.
|
| 22 |
Tang J, Jin Z, Zhou X, et al. Enhancing convolutional neural network scheme for rheumatoid arthritis grading with limited clinical data[J]. Chinese Physics B, 2019, 28(3): 038701.
|
| 23 |
Kato M, Ikeda K, Sugiyama T, et al. Associations of ultrasound-based inflammation patterns with peripheral innate lymphoid cell populations, serum cytokines/chemokines, and treatment response to methotrexate in rheumatoid arthritis and spondyloarthritis[J]. PLoS One, 2021, 16(5): e0252116.
|
| 24 |
Momtazmanesh S, Nowroozi A, Rezaei N. Artificial intelligence in rheumatoid arthritis: current status and future perspectives: a state-of-the-art review[J]. Rheumatol Ther, 2022, 9(5): 1249-1304.
|
| 25 |
Burlina P, Billings S, Joshi N, et al. Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods[J]. PLoS One, 2017, 12(8): e0184059.
|
| 26 |
D’agostino V, Sorriento A, Cafarelli A, et al. Ultrasound imaging in knee osteoarthritis: current role, recent advancements, and future perspectives[J]. J Clin Med, 2024, 13(16): 4930.
|
| 27 |
Hattori S, Saggar R, Heidinger E, et al. Advances in ultrasound-guided surgery and artificial intelligence applications in musculoskeletal diseases[J]. Diagnostics (Basel), 2024, 14(18): 2008.
|
| 28 |
Getzmann J M, Zantonelli G, Messina C, et al. The use of artificial intelligence in musculoskeletal ultrasound: a systematic review of the literature[J]. Radiol Med, 2024, 129(9): 1405-1411.
|
| 29 |
Liu C, Wei M, Qin Y, et al. Harnessing large language models for structured reporting in breast ultrasound: a comparative study of Open AI (GPT-4.0) and Microsoft Bing (GPT-4)[J]. Ultrasound Med Biol, 2024, 50(11): 1697-1703.
|