| [1] |
Kim H, Kwak JM, Kholinne E, et al. Determination of the patient acceptable symptomatic state after osteocapsular arthroplasty for primary elbow osteoarthritis[J]. J Shoulder Elbow Surg, 2021, 30(9):2127-2133.
|
| [2] |
Jahn A, Kjærgaard C, Havnegjerde A, et al. Elbow osteoarthritis and occupational mechanical exposures: a systematic review and meta-analysis[J]. Occup Med (Lond), 2025, 75(7):433-441.
|
| [3] |
Temporin K, Miyoshi Y, Miyamura S, et al. Bone deformity in sports-related elbow osteoarthritis: influence of osteochondritis dissecans of the capitellum-a cross-sectional study[J]. Arch Orthop Trauma Surg, 2024, 144(4):1685-1691.
|
| [4] |
Ravalli S, Pulici C, Binetti S, et al. An overview of the pathogenesis and treatment of elbow osteoarthritis[J]. J Funct Morphol Kinesiol, 2019, 4(2):30.
|
| [5] |
Hwang JS, Won SJ, Gong HS. How does the subchondral bone density distribution of the distal humerus change between early and advanced stages of osteoarthritis?[J]. Clin Orthop Relat Res, 2024, 482(7):1210-1215.
|
| [6] |
Martinez-Catalan N, Sanchez-Sotelo J. Primary elbow osteoarthritis: evaluation and management[J]. J Clin Orthop Trauma, 2021, 19:67-74.
|
| [7] |
Sharma M, Soundararajan R, Verma N, et al. Magnetic resonance imaging in arthropathies of elbow joint: a pictorial review[J]. Indian J Musculoskelet Radiol, 2025, 7(2):159-164.
|
| [8] |
Kijowski R, Fritz J, Deniz CM. Deep learning applications in osteoarthritis imaging[J]. Skeletal Radiol, 2023, 52(11):2225-2238.
|
| [9] |
Mohammadi S, Salehi MA, Jahanshahi A, et al. Artificial intelligence in osteoarthritis detection: a systematic review and meta-analysis[J]. Osteoarthritis Cartilage, 2024, 32(3):241-253.
|
| [10] |
Lai Q, Chen W, Ding X, et al. Quality control of elbow joint radiography using a YOLOv8-based artificial intelligence technology[J]. Eur Radiol Exp, 2024, 8(1):107.
|
| [11] |
Chung SW, Han SS, Lee JW, et al. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm[J]. Acta Orthop, 2018, 89(4):468-473.
|
| [12] |
Huhtanen JT, Nyman M, Doncenco D, et al. Deep learning accurately classifies elbow joint effusion in adult and pediatric radiographs[J]. Sci Rep, 2022, 12(1):11803.
|
| [13] |
Kwak JM, Kholinne E, Sun Y, et al. Intraobserver and interobserver reliability of the computed tomography-based radiographic classification of primary elbow osteoarthritis: comparison with plain radiograph-based classification and clinical assessment[J]. Osteoarthritis Cartilage, 2019, 27(7):1057-1063.
|
| [14] |
Cui Y, Ji S, Zha Y, et al. An automatic method for elbow joint recognition, segmentation and reconstruction[J]. Sensors (Basel), 2024, 24(13):4330.
|
| [15] |
Wang J, Ding X, Zhang Y, et al. Morphological classification of elbow joints based on deep learning with CT images[C]//2024 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics. Hangzhou, 2024:447-451.
|
| [16] |
Jeon YD, Kang MJ, Kuh SU, et al. Deep learning model based on you only look once algorithm for detection and visualization of fracture areas in three-dimensional skeletal images[J]. Diagnostics (Basel), 2023, 14(1):11.
|
| [17] |
Inui A, Mifune Y, Nishimoto H, et al. Detection of elbow OCD in the ultrasound image by artificial intelligence using YOLOv8[J]. Appl Sci, 2023, 13(13):7623.
|
| [18] |
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.
|
| [19] |
Herrmann J, Afat S, Gassenmaier S, et al. Faster elbow MRI with deep learning reconstruction-assessment of image quality, diagnostic confidence, and anatomy visualization compared to standard imaging[J]. Diagnostics (Basel), 2023, 13(17):2747.
|
| [20] |
Yi J, Hahn S, Lee HJ, et al. Thin-slice elbow MRI with deep learning reconstruction: superior diagnostic performance of elbow ligament pathologies[J]. Eur J Radiol, 2024, 175:111471.
|
| [21] |
Judge CS, Krewer F, O’Donnell MJ, et al. Multimodal artificial intelligence in medicine[J]. Kidney360, 2024, 5(11):1771-1779.
|
| [22] |
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis[J]. Med Image Anal, 2017, 42:60-88.
|
| [23] |
Oettl FC, Zsidai B, Oeding JF, et al. Artificial intelligence-assisted analysis of musculoskeletal imaging-a narrative review of the current state of machine learning models[J]. Knee Surg Sports Traumatol Arthrosc, 2025, 33(8):3032-3038.
|
| [24] |
Tiulpin A, Klein S, Bierma-Zeinstra SMA, et al. Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical data[J]. Sci Rep, 2019, 9(1):20038.
|
| [25] |
Liu Y, Xiao G, Zhang Y, et al. Predictive value of machine learning in knee osteoarthritis progression: systematic review and meta-analysis[J]. J Med Internet Res, 2025, 27:e80430.
|
| [26] |
Nishiwaki M, Willing R, Johnson JA, et al. Identifying the location and volume of bony impingement in elbow osteoarthritis by 3-dimensional computational modeling[J]. J Hand Surg Am, 2013, 38(7):1370-1376.
|
| [27] |
Willing RT, Nishiwaki M, Johnson JA, et al. Evaluation of a computational model to predict elbow range of motion[J]. Comput Aided Surg, 2014, 19(4-6):57-63.
|
| [28] |
Miyake J, Shimada K, Moritomo H, et al. Kinematic changes in elbow osteoarthritis: in vivo and 3-dimensional analysis using computed tomographic data[J]. J Hand Surg Am, 2013, 38(5):957-964.
|
| [29] |
Willing RT, Lalone EA, Shannon H, et al. Validation of a finite element model of the human elbow for determining cartilage contact mechanics[J]. J Biomech, 2013, 46(10):1767-1771.
|
| [30] |
Chen R, Yang G, Li S, et al. Bony landmarks guided mapping of the osteophytes of the elbow osteoarthritis patients: a three dimensional computed tomograph based study[J]. J Orthop Surg Res, 2025, 20(1):714.
|
| [31] |
Eskinazi I, Fregly BJ. An open-source toolbox for surrogate modeling of joint contact mechanics[J]. IEEE Trans Biomed Eng, 2016, 63(2):269-277.
|
| [32] |
Kneifl J, Rosin D, Avci O, et al. Low-dimensional data-based surrogate model of a continuum-mechanical musculoskeletal system based on non-intrusive model order reduction[J]. Arch Appl Mech, 2023, 93(9):3637-3663.
|
| [33] |
陈仁杰, 鲁谊. 肘关节骨关节炎手术治疗进展 [J/CD]. 中华老年骨科与康复电子杂志, 2024, 10(1): 57-64.
|
| [34] |
Gupta P, Marigi EM, Sanchez-Sotelo J. Research on artificial intelligence in shoulder and elbow surgery is increasing[J]. JSES Int, 2023, 7(1):158-161.
|
| [35] |
Shiode R, Oka K, Shigi A, et al. Arthroscopic debridement of elbow osteoarthritis using CT-based computer-aided navigation systems is accurate[J]. Arthrosc Sports Med Rehabil, 2021, 3(6):e1687-e1696.
|
| [36] |
Gregory TM, Gregory J, Sledge J, et al. Surgery guided by mixed reality: presentation of a proof of concept[J]. Acta Orthop, 2018, 89(5):480-483.
|
| [37] |
Yamamoto M, Oyama S, Otsuka S, et al. Experimental pilot study for augmented reality-enhanced elbow arthroscopy[J]. Sci Rep, 2021, 11(1):4650.
|
| [38] |
Calem DB, Lubiatowski P, Trenhaile S, et al. Mixed reality applications in upper extremity surgery: the future is now[J]. EFORT Open Rev, 2024, 9(11):1034-1046.
|
| [39] |
Daher M, Koa J, Boufadel P, et al. Breaking barriers: can ChatGPT compete with a shoulder and elbow specialist in diagnosis and management?[J]. JSES Int, 2023, 7(6):2534-2541.
|
| [40] |
张忠, 齐岩松, 吴海贺, 等. 关节镜下治疗肘关节骨性关节炎伴僵硬短期疗效观察 [J/CD]. 中华肩肘外科电子杂志, 2019, 7(4): 355-359.
|
| [41] |
Lim TK, Koh KH, Lee HI, et al. Arthroscopic debridement for primary osteoarthritis of the elbow: analysis of preoperative factors affecting outcome[J]. J Shoulder Elbow Surg, 2014, 23(9):1381-1387.
|
| [42] |
Ma Y, Liu D, Cai L. Deep learning-based upper limb functional assessment using a single Kinect v2 sensor[J]. Sensors (Basel), 2020, 20(7):1903.
|
| [43] |
Costa V, Ramírez ó, Otero A, et al. Validity and reliability of inertial sensors for elbow and wrist range of motion assessment[J]. Peer J, 2020, 8:e9687.
|
| [44] |
Xie Y, Lu L, Gao F, et al. Integration of artificial intelligence, blockchain, and wearable technology for chronic disease management: a new paradigm in smart healthcare[J]. Curr Med Sci, 2021, 41(6):1123-1133.
|
| [45] |
Little K, Pappachan BK, Yang S, et al. Elbow motion trajectory prediction using a multi-modal wearable system: a comparative analysis of machine learning techniques[J]. Sensors (Basel), 2021, 21(2):498.
|
| [46] |
Adans-Dester C, Hankov N, O'Brien A, et al. Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery[J]. NPJ Digit Med, 2020, 3(1):121.
|
| [47] |
Sonntag J, Yu L, Wang X, et al. Neurophysiological predictors of deep learning based unilateral upper limb motor imagery classification[J]. Front Hum Neurosci, 2025, 19:1617748.
|
| [48] |
Jamsrandorj A, Kumar KS, Arshad MZ, et al. Deep learning networks for view-independent knee and elbow joint angle estimation[C]//Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society. Glasgow, 2022: 2703-2707.
|
| [49] |
Khan MN, Altalbe A, Naseer F, et al. Telehealth-enabled in-home elbow rehabilitation for brachial plexus injuries using deep-reinforcement-learning-assisted telepresence robots[J]. Sensors (Basel), 2024, 24(4):1273.
|