[1] |
Jain NB, Wilcox RB 3rd, Katz JN, et al. Clinical Examination of the Rotator Cuff[J]. PM R, 2013, 5(1):45-56.
|
[2] |
Dang A, Davies M. Rotator Cuff Disease:Treatment Options and Considerations[J]. Sports Med Arthrosc Rev,2018,26(3):129-133.
|
[3] |
刘蓬然,陆林,霍彤彤,等.人工智能技术在骨科领域中的应用进展[J].中华骨科杂志,2020,40(24):1699-1704.
|
[4] |
Karnuta JM, Haeberle HS, Luu BC, et al. Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip[J]. J Arthroplasty,2021,36(7S) :S290-S294.e1.
|
[5] |
Minelli M, Cina A, Galbusera F, et al. Correction to:Measuring the critical shoulder angle on radiographs:an accurate and repeatable deep learning model[J]. Skeletal Radiol,2022,51(9):1905.
|
[6] |
Iio R, Ueda D, Matsumoto T, et al. Deep learning-based screening tool for rotator cuff tears on shoulder radiography[J]. J Orthop Sci,2024 ,29(3):828-834.
|
[7] |
Kim Y, Choi D, Lee KJ, et al. Ruling out rotator cuff tear in shoulder radiograph series using deep learning:redefining the role of conventional radiograph[J]. Eur Radiol,2020,30(5):2843-2852.
|
[8] |
Kang Y, Choi D, Lee KJ, et al. Evaluating subscapularis tendon tears on axillary lateral radiographs using deep learning[J]. Eur Radiol,2021,31(12):9408-9417.
|
[9] |
Hashimoto E, Maki S, Ochiai N, et al. Automated detection and classification of the rotator cuff tear on plain shoulder radiograph using deep learning[J]. J Shoulder Elbow Surg,2024,33(8):1733-1739.
|
[10] |
Zhao Y, Qiu J, Li Y, et al. Machine-learning models for diagnosis of rotator cuff tears in osteoporosis patients based on anteroposterior X-rays of the shoulder joint[J]. SLAS Technol,2024,29(4):100149.
|
[11] |
Okuda S , Fujita D , Tanaka H ,et al.Detection of Shoulder Rotator Cuff Tears from X-Ray Image by Using Convolutional Neural Network[J].J Japan Soci Fuzzy Theory Intelligent Informatics,2023, 35(1):593-597.
|
[12] |
Longo UG, Di Naro C, Campisi S, et al. Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies:A Clinical Data-Based Approach[J]. Diagnostics(Basel),2023,13(18):2915.
|
[13] |
张继民. 基于MSCT 技术对肩关节骨性结构与肩袖损伤的相关性研究[J]. 中国CT 和MRI 杂志, 2018, 16 (9):137-140.
|
[14] |
Taghizadeh E, Truffer O, Becce F, et al. Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets[J]. Eur Radiol,2021,31(1):181-190.
|
[15] |
Eminian S , Taghizadeh E , Truffer O ,et al. Deep Learning for the Automatic Quantification of Rotator Cuff Muscle Degeneration from Shoulder CT Data Sets[J]. Eur Radiol, 2019, 23(S02):A024.
|
[16] |
Kim KC, Lee WY, Shin HD, et al. Repair integrity and functional outcomes of arthroscopic repair for intratendinous partial-thickness rotator cuff tears[J]. J Orthop Surg (Hong Kong) ,2019,27(2):2309499019847227.
|
[17] |
Lee KC, Cho Y, Ahn KS, et al. Deep-Learning-Based Automated Rotator Cuff Tear Screening in Three Planes of Shoulder MRI[J]. Diagnostics (Basel) , 2023,13(20):3254.
|
[18] |
Lin DJ, Schwier M, Geiger B, et al. Deep Learning Diagnosis and Classification of Rotator Cuff Tears on Shoulder MRI[J]. Invest Radiol,2023,58(6):405-412.
|
[19] |
Guo D, Liu X, Wang D, et al. Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears[J]. J Orthop Surg Res,2023,18(1):426.
|
[20] |
Kim SH, Yoo HJ, Yoon SH, et al. Development of a deep learningbased fully automated segmentation of rotator cuff muscles from clinical MR scans[J]. Acta Radiol,2024,65(9):1126-1132.
|
[21] |
Yao J, Chepelev L, Nisha Y, et al. Evaluation of a deep learning method for the automated detection of supraspinatus tears on MRI[J]. Skeletal Radiol, 2022,51(9):1765-1775.
|
[22] |
Esfandiari MA, Fallah Tafti M, Jafarnia Dabanloo N, et al. Detection of the rotator cuff tears using a novel convolutional neural network from magnetic resonance image (MRI) [J]. Heliyon,2023,9(5):e15804.
|
[23] |
Johnson PM, Recht MP, Knoll F. Improving the Speed of MRI with Artificial Intelligence[J]. Semin Musculoskelet Radiol,2020,24(1):12-20.
|
[24] |
Lin DJ, Walter SS, Fritz J. Artificial Intelligence-Driven Ultra-Fast Superresolution MRI:10-Fold Accelerated Musculoskeletal Turbo Spin Echo MRI Within Reach[J]. Invest Radiol,2023 ,58(1):28-42.
|
[25] |
Kim M, Park H, Kim JY, et al. MRI-based diagnosis of rotator cuff tears using deep learning and weighted linear combinations[C]// Ravikumar P, Zhang T. Machine Learning for Healthcare Conference. Ghent:PMLR, 2020:292-308.
|
[26] |
Ro K, Kim JY, Park H, et al. Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI[J]. Sci Rep,2021,11(1):15065.
|
[27] |
Riem L, Feng X, Cousins M, et al. A Deep Learning Algorithm for Automatic 3D Segmentation of Rotator Cuff Muscle and Fat from Clinical MRI Scans[J]. Radiol Artif Intell,2023,5(2):e220132.
|
[28] |
Kim H, Shin K, Kim H, et al. Can deep learning reduce the time and effort required for manual segmentation in 3D reconstruction of MRI in rotator cuff tears? [J]. PLoS One,2022,17(10):e0274075.
|
[29] |
Ro K, Kim JY, Park H, et al. Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI[J]. Sci Rep,2021,11(1):15065.
|
[30] |
Shim E, Kim JY, Yoon JP, et al. Author Correction:Automated rotator cuff tear classification using 3D convolutional neural network[J]. Sci Rep,2021,11(1):15996.
|
[31] |
Lee SH, Lee J, Oh KS, et al. Automated 3-dimensional MRI segmentation for the posterosuperior rotator cuff tear lesion using deep learning algorithm[J]. PLoS One,2023,18(5):e0284111.
|
[32] |
Key S, Demir S, Gurger M,et al. ViVGG19:Novel exemplar deep feature extraction-based shoulder rotator cuff tear and biceps tendinosis detection using magnetic resonance images[J]. Med Eng Phys,2022 ,110:103864.
|
[33] |
Lee K, Kim JY, Lee MH, et al. Imbalanced Loss-Integrated Deep-Learning-Based Ultrasound Image Analysis for Diagnosis of Rotator-Cuff Tear[J]. Sensors (Basel) ,2021,21(6):2214.
|
[34] |
Lee K , Yang J , Lee MH, et al. USG-Net:Deep Learning-based Ultrasound Scanning-Guide for an Orthopedic Sonographer[J].Springer Cham, 2022:23-32.
|
[35] |
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.
|
[36] |
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.
|
[37] |
Yu L, Li Y, Wang XF, et al. Analysis of the value of artificial intelligence combined with musculoskeletal ultrasound in the differential diagnosis of pain rehabilitation of scapulohumeral periarthritis[J]. Medicine (Baltimore) ,2023,102(14):e33125.
|
[38] |
Potty AG, Potty ASR, Maffulli N, et al. Approaching Artificial Intelligence in Orthopaedics:Predictive Analytics and Machine Learning to Prognosticate Arthroscopic Rotator Cuff Surgical Outcomes[J]. J Clin Med,2023,12(6):2369.
|
[39] |
Allaart LJH, Spanning SV, Lafosse L, et al. Machine Learning Consortium. Developing a machine learning algorithm to predict probability of retear and functional outcomes in patients undergoing rotator cuff repair surgery:protocol for a retrospective, multicentre study[J]. BMJ Open,2023,13(2):e063673.
|
[40] |
费扬.基于影像组学技术的肩袖损伤诊断和肩袖修补术后再撕裂预测模型[D].杭州:浙江大学,2021.
|
[41] |
Zhang Z, Ke C, Zhang Z, et al. Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm[J]. Front Artif Intell,2024,29(7):1331853.
|
[42] |
Cho SH, Kim YS. Prediction of Retear After Arthroscopic Rotator Cuff Repair Based on Intraoperative Arthroscopic Images Using Deep Learning[J]. Am J Sports Med,2023,51(11):2824-2830.
|
[43] |
Audigé L, Aghlmandi S, Grobet C, et al. Prediction of Shoulder Stiffness After Arthroscopic Rotator Cuff Repair[J]. Am J Sports Med,2021,49(11):3030-3039.
|
[44] |
Li C, Alike Y, Hou J, et al. Machine learning model successfully identifies important clinical features for predicting outpatients with rotator cuff tears[J]. Knee Surg Sports Traumatol Arthrosc,2023,31(7):2615-2623.
|
[45] |
Darevsky DM, Hu DA, Gomez FA, et al. A Tool for Low-Cost,Quantitative Assessment of Shoulder Function Using Machine Learning[J]. medRxiv,2023 ,17:2023.04.14.23288613.
|
[46] |
Zhou XY , Guo Y , Shen M ,et al.Artificial Intelligence in Surgery[J].医学前沿:英文版, 2020, 14(4):14.
|
[47] |
Familiari F, Galasso O, Massazza F, et al.. Artificial Intelligence in the Management of Rotator Cuff Tears[J]. Int J Environ Res Public Health,2022,19(24):16779.
|
[48] |
Barakat-Johnson M, Jones A, Burger M,et al. Reshaping Wound Care:Evaluation of an Artificial Intelligence App to Improve Wound Assessment and Management[J]. Stud Health Technol Inform,2024,25(310):941-945.
|
[49] |
Cunha B, Ferreira R, Sousa ASP. Home-Based Rehabilitation of the Shoulder Using Auxiliary Systems and Artificial Intelligence:An Overview[J]. Sensors (Basel) ,2023,23(16):7100.
|
[50] |
Liao WJ, Lee KT, Chiang LY,et al.Postoperative Rehabilitation after Anterior Cruciate Ligament Reconstruction through Telerehabilitation with Artificial Intelligence Brace during COVID-19 Pandemic[J]. J Clin Med,2023,12(14):4865.
|
[51] |
Kubota S, Kadone H, Shimizu Y, et al. Robotic Shoulder Rehabilitation With the Hybrid Assistive Limb in a Patient With Delayed Recovery After Postoperative C5 Palsy:A Case Report[J]. Front Neurol, 2021, 12:676352.
|