Share：
Share this content in WeChat
X
[Chinese] [PDF] 5 1
• Reviews •
Research progress of deep learning combined with radiomics in musculoskeletal diseases
WEI Bingqi  LI Yijing  ZHANG Xinyue  LI Yuntan  ZHANG Luwei  YAO Qianwen  CHENG Shao  WANG Shangzeng 

Cite this article as: WEI B Q, LI Y J, ZHANG X Y, et al. Research progress of deep learning combined with radiomics in musculoskeletal diseases[J]. Chin J Magn Reson Imaging, 2026, 17(3): 213-220. DOI:10.12015/issn.1674-8034.2026.03.031.


[Abstract] Musculoskeletal diseases are among the most prevalent and debilitating chronic conditions worldwide. With the acceleration of population aging, their incidence continues to rise, posing a major public health challenge. Conventional imaging-based diagnosis relies heavily on the subjective interpretation of clinicians and is limited by high inter-observer variability, insufficient sensitivity for early lesions, and a lack of robust quantitative assessment tools, making it difficult to meet the requirements of precision medicine. In recent years, the rapid development of deep learning and radiomics has provided new technical pathways for intelligent assessment and decision-making in musculoskeletal disorders. This review systematically summarizes the research progress of deep learning and radiomics in a range of musculoskeletal conditions, including osteoarthritis, osteoporosis and fragility fractures, bone tumors and benign-malignant differentiation, muscle diseases and muscle atrophy, as well as tendon and ligament injuries. We focus on their applications in automatic segmentation, computer-aided diagnosis, disease classification, progression prediction, treatment decision support, and prognostic evaluation, highlighting their potential advantages in improving diagnostic accuracy, enabling quantitative characterization of lesions, and supporting individualized therapeutic strategies. In addition, we outline the major challenges currently limiting clinical translation, such as insufficient data standardization, limited model interpretability, suboptimal multicenter generalizability, and uncertainties in implementation pathways. Finally, future research directions are discussed with the aim of providing methodological reference and theoretical support for early diagnosis, prognostic assessment, and precision treatment of musculoskeletal diseases based on deep learning and radiomics.
[Keywords] musculoskeletal diseases；radiomics；deep learning；image segmentation；diagnosis；prognostic evaluation；magnetic resonance imaging

WEI Bingqi1, 2   LI Yijing3   ZHANG Xinyue3   LI Yuntan3   ZHANG Luwei1, 2   YAO Qianwen1, 2   CHENG Shao1, 2   WANG Shangzeng1, 2*  

1 School of Orthopedics, Henan University of Chinese Medicine, Zhengzhou 450002, China

2 Department of Orthopaedics, Henan Provincial Hospital of Chinese Medicine (The Second Affiliated Hospital of Henan University of Chinese Medicine), Zhengzhou 450002, China

3 School of Traditional Chinese medicine, Henan University of Chinese medicine, Zhengzhou 450006, China

Corresponding author: WANG S Z, E-mail: wangsz74@163.com

Conflicts of interest   None.

Received  2025-11-29
Accepted  2026-01-31
DOI: 10.12015/issn.1674-8034.2026.03.031
Cite this article as: WEI B Q, LI Y J, ZHANG X Y, et al. Research progress of deep learning combined with radiomics in musculoskeletal diseases[J]. Chin J Magn Reson Imaging, 2026, 17(3): 213-220. DOI:10.12015/issn.1674-8034.2026.03.031.

[1]
LIANG B, WEI Y, PEI H M, et al. Burden of musculoskeletal disorders-global and BRICS+ nations, 1990-2021[J]. China CDC Wkly, 2025, 7(15): 514-522. DOI: 10.46234/ccdcw2025.081.
[2]
DU H C, LI S, TIAN Z C, et al. Global burden of six musculoskeletal disorders in women of childbearing age from 1990 to 2021[J/OL]. Sci Rep, 2025, 15(1): 35423 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/41073600/. DOI: 10.1038/s41598-025-19382-z.
[3]
GBD 2021 Other Musculoskeletal Disorders Collaborators. Global, regional, and national burden of other musculoskeletal disorders, 1990-2020, and toprojections 2050: a systematic analysis of the Global Burden of Disease Study 2021[J/OL]. Lancet Rheumatol, 2023, 5(11): e670-e682 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/37927903/. DOI: 10.1016/S2665-9913(23)00232-1.
[4]
NGUYEN A, LEE P, RODRIGUEZ E K, et al. Addressing the growing burden of musculoskeletal diseases in the ageing US population: challenges and innovations[J/OL]. Lancet Healthy Longev, 2025, 6(5): 100707 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/40381641/. DOI: 10.1016/j.lanhl.2025.100707.
[5]
ZHANG L, WEN X, LI J W, et al. Diagnostic error and bias in the department of radiology: a pictorial essay[J/OL]. Insights Imaging, 2023, 14(1): 163 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/37782396/. DOI: 10.1186/s13244-023-01521-7.
[6]
ZHU D, ZHANG Z T, LI W J. Accuracy and reliability of multimodal imaging in diagnosing knee sports injuries[J/OL]. Curr Med Imaging, 2025, 21: e15734056360665 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/40377156/. DOI: 10.2174/0115734056360665250506115221.
[7]
LI J Y, LUO W X, WANG Z F, et al. Advances in radiomics for early diagnosis and precision treatment of lung cancer[J]. J Biomed Eng, 2025, 42(5): 1062-1068. DOI: 10.7507/1001-5515.202405059.
[8]
LAÇI H, SEVRANI K, IQBAL S. Deep learning approaches for classification tasks in medical X-ray, MRI, and ultrasound images: a scoping review[J/OL]. BMC Med Imaging, 2025, 25(1): 156 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/40335965/. DOI: 10.1186/s12880-025-01701-5.
[9]
ZWANENBURG A, VALLIÈRES M, ABDALAH M A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping[J]. Radiology, 2020, 295(2): 328-338. DOI: 10.1148/radiol.2020191145.
[10]
SHAO J J, LIN H X, DING L, et al. Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study[J/OL]. Insights Imaging, 2024, 15(1): 35 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/38321327/. DOI: 10.1186/s13244-024-01610-1.
[11]
FEIERABEND M, WOLFGART J M, PRASTER M, et al. Applications of machine learning and deep learning in musculoskeletal medicine: a narrative review[J/OL]. Eur J Med Res, 2025, 30(1): 386 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/40375335/. DOI: 10.1186/s40001-025-02511-9.
[12]
BONALDI L, PRETTO A, PIRRI C, et al. Deep learning-based medical images segmentation of musculoskeletal anatomical structures: a survey of bottlenecks and strategies[J/OL]. Bioengineering, 2023, 10(2): 137 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/36829631/. DOI: 10.3390/bioengineering10020137.
[13]
DEBS P, FAYAD L M. The promise and limitations of artificial intelligence in musculoskeletal imaging[J/OL]. Front Radiol, 2023, 3: 1242902 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/37609456/. DOI: 10.3389/fradi.2023.1242902.
[14]
LOTLIKAR V S, SATPUTE N, GUPTA A. Brain tumor detection using machine learning and deep learning: a review[J]. Curr Med Imaging, 2022, 18(6): 604-622. DOI: 10.2174/1573405617666210923144739.
[15]
JAFARI M, SHOEIBI A, KHODATARS M, et al. Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: a review[J/OL]. Comput Biol Med, 2023, 160: 106998 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/37182422/. DOI: 10.1016/j.compbiomed.2023.106998.
[16]
LAMBIN P, WOODRUFF H C, MALI S A, et al. Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine[J]. Nat Rev Clin Oncol, 2025, 22(11): 831-846. DOI: 10.1038/s41571-025-01067-1.
[17]
ZHANG J Y, CHE Y L, LIU R R, et al. Deep learning-driven multi-omics analysis: enhancing cancer diagnostics and therapeutics[J/OL]. Brief Bioinform, 2025, 26(4): bbaf440 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/40874818/. DOI: 10.1093/bib/bbaf440.
[18]
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/OL]. PLoS One, 2022, 17(10): e0274075 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/36215291/. DOI: 10.1371/journal.pone.0274075.
[19]
ZHANG J Y, YANG J M, WANG X M, et al. Application and prospects of deep learning technology in fracture diagnosis[J]. Curr Med Sci, 2024, 44(6): 1132-1140. DOI: 10.1007/s11596-024-2928-5.
[20]
LI J, CHEN J Y, TANG Y C, et al. Transforming medical imaging with Transformers A comparative review of key properties, current progresses, and future perspectives[J/OL]. Med Image Anal, 2023, 85: 102762 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/36738650/. DOI: 10.1016/j.media.2023.102762.
[21]
AVANZO M, WEI L S, STANCANELLO J, et al. Machine and deep learning methods for radiomics[J/OL]. Med Phys, 2020, 47(5) [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/32418336/. DOI: 10.1002/mp.13678 DOI: 10.1002/mp.13678.
[22]
LAFATA K J, WANG Y Q, KONKEL B, et al. Radiomics: a primer on high-throughput image phenotyping[J]. Abdom Radiol, 2022, 47(9): 2986-3002. DOI: 10.1007/s00261-021-03254-x.
[23]
SAADH M J, HUSSAIN Q M, ALBADR R J, et al. Advanced feature fusion of radiomics and deep learning for accurate detection of wrist fractures on X-ray images[J/OL]. BMC Musculoskelet Disord, 2025, 26(1): 498 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/40394557/. DOI: 10.1186/s12891-025-08733-6.
[24]
KUMM J, TURKIEWICZ A, ZHANG F, et al. Structural abnormalities detected by knee magnetic resonance imaging are common in middle-aged subjects with and without risk factors for osteoarthritis[J]. Acta Orthop, 2018, 89(5): 535-540. DOI: 10.1080/17453674.2018.1495164.
[25]
ISENSEE F, JAEGER P F, KOHL S A A, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nat Methods, 2021, 18(2): 203-211. DOI: 10.1038/s41592-020-01008-z.
[26]
AMBELLAN F, TACK A, EHLKE M, et al. Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative[J/OL]. Med Image Anal, 2019, 52: 109-118 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/30529224/. DOI: 10.1016/j.media.2018.11.009.
[27]
ZHOU Z Y, ZHAO G Y, KIJOWSKI R, et al. Deep convolutional neural network for segmentation of knee joint anatomy[J]. Magn Reson Med, 2018, 80(6): 2759-2770. DOI: 10.1002/mrm.27229.
[28]
WIRTH W, MASCHEK S, WISSER A, et al. Evaluation of an automated laminar cartilage T2 relaxation time analysis method in an early osteoarthritis model[J/OL]. Skeletal Radiol, 2025, 54(3): 571-584 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/39230576/. DOI: 10.1007/s00256-024-04786-1.
[29]
OLSSON S, AKBARIAN E, LIND A, et al. Automating classification of osteoarthritis according to Kellgren-Lawrence in the knee using deep learning in an unfiltered adult population[J/OL]. BMC Musculoskelet Disord, 2021, 22(1): 844 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/34600505/. DOI: 10.1186/s12891-021-04722-7.
[30]
KIJOWSKI R, FRITZ J, DENIZ C M. Deep learning applications in osteoarthritis imaging[J]. Skeletal Radiol, 2023, 52(11): 2225-2238. DOI: 10.1007/s00256-023-04296-6.
[31]
ZHAO H M, OU L, ZHANG Z M, et al. The value of deep learning-based X-ray techniques in detecting and classifying K-L grades of knee osteoarthritis: a systematic review and meta-analysis[J]. Eur Radiol, 2025, 35(1): 327-340. DOI: 10.1007/s00330-024-10928-9.
[32]
WANG T, LIU H, ZHAO W B, et al. Predicting knee osteoarthritis progression using neural network with longitudinal MRI radiomics, and biochemical biomarkers: a modeling study[J/OL]. PLoS Med, 2025, 22(8): e1004665 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/40839548/. DOI: 10.1371/journal.pmed.1004665.
[33]
HALILAJ E, LE Y, HICKS J L, et al. Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative[J]. Osteoarthritis Cartilage, 2018, 26(12): 1643-1650. DOI: 10.1016/j.joca.2018.08.003.
[34]
LEUNG K, ZHANG B F, TAN J M, et al. Prediction of total knee replacement and diagnosis of osteoarthritis by using deep learning on knee radiographs: data from the osteoarthritis initiative[J]. Radiology, 2020, 296(3): 584-593. DOI: 10.1148/radiol.2020192091.
[35]
KIM M S, CHO R K, YANG S C, et al. Machine learning for detecting total knee arthroplasty implant loosening on plain radiographs[J/OL]. Bioengineering, 2023, 10(6): 632 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/37370563/. DOI: 10.3390/bioengineering10060632.
[36]
HALVORSON R T, TORRES-ESPIN A, CHERCHES M, et al. Predicting recovery following total hip and knee arthroplasty using a clustering algorithm[J/OL]. Arthroplasty Today, 2024, 27: 101395 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/39071835/. DOI: 10.1016/j.artd.2024.101395.
[37]
WALKER J. Osteoporosis and fragility fractures: risk assessment, management and prevention[J]. Nurs Older People, 2020, 32(1): 34-41. DOI: 10.7748/nop.2019.e1153.
[38]
GATINEAU G, SHEVROJA E, VENDRAMI C, et al. Development and reporting of artificial intelligence in osteoporosis management[J]. J Bone Miner Res, 2024, 39(11): 1553-1573. DOI: 10.1093/jbmr/zjae131.
[39]
FANG Y J, LI W, CHEN X J, et al. Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks[J]. Eur Radiol, 2021, 31(4): 1831-1842. DOI: 10.1007/s00330-020-07312-8.
[40]
HONG N, CHO S W, SHIN S, et al. Deep-learning-based detection of vertebral fracture and osteoporosis using lateral spine X-ray radiography[J]. J Bone Miner Res, 2023, 38(6): 887-895. DOI: 10.1002/jbmr.4814.
[41]
DENG L, SHUAI P, LIU Y R, et al. Diagnostic performance of radiomics for predicting osteoporosis in adults: a systematic review and meta-analysis[J]. Osteoporos Int, 2024, 35(10): 1693-1707. DOI: 10.1007/s00198-024-07136-y.
[42]
WANG S G, TONG X Y, FAN Y, et al. Combining deep learning and radiomics for automated, objective, comprehensive bone mineral density assessment from low-dose chest computed tomography[J]. Acad Radiol, 2024, 31(3): 1180-1188. DOI: 10.1016/j.acra.2023.08.030.
[43]
LI Y L, LIU S W, ZHANG Y, et al. Deep learning-enhanced opportunistic osteoporosis screening in ultralow-voltage (80 kV) chest CT: a preliminary study[J]. Acad Radiol, 2025, 32(7): 4254-4265. DOI: 10.1016/j.acra.2024.11.062.
[44]
WANG L X, ZHANG C, WANG Y Z, et al. Development and validation of a deep learning system for the detection of nondisplaced femoral neck fractures[J/OL]. Bioengineering, 2025, 12(5): 466 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/40428085/. DOI: 10.3390/bioengineering12050466.
[45]
HONG N, PARK H, KIM C O, et al. Bone radiomics score derived from DXA hip images enhances hip fracture prediction in older women[J]. J Bone Miner Res, 2021, 36(9): 1708-1716. DOI: 10.1002/jbmr.4342.
[46]
CHUAN F N, MEI M, LIAO K, et al. DXA based radiomics scoring: a novel approach for fragility fracture risk assessment in elderly type 2 diabetes patients[J]. Osteoporos Int, 2025, 36(9): 1691-1700. DOI: 10.1007/s00198-025-07644-5.
[47]
LIU H T, ZHANG Y H, MA X H, et al. Quantitative assessment of benign and malignant bone tumours using synthetic magnetic resonance imaging and diffusion measures[J/OL]. Sci Rep, 2025, 15: 39730 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/41224943/. DOI: 10.1038/s41598-025-23520-y.
[48]
YAMANA M, BISE R, ENDO M, et al. Development and evaluation of deep learning models for detecting and classifying various bone tumours in full-field limb radiographs using automated object detection models[J]. Bone Joint Res, 2025, 14(9): 760-768. DOI: 10.1302/2046-3758.149.BJR-2024-0505.R1.
[49]
VON SCHACKY C E, WILHELM N J, SCHÄFER V S, et al. Multitask deep learning for segmentation and classification of primary bone tumors on radiographs[J]. Radiology, 2021, 301(2): 398-406. DOI: 10.1148/radiol.2021204531.
[50]
YILDIZ POTTER I, YERITSYAN D, MAHAR S, et al. Automated bone tumor segmentation and classification as benign or malignant using computed tomographic imaging[J]. J Digit Imaging, 2023, 36(3): 869-878. DOI: 10.1007/s10278-022-00771-z.
[51]
LIU R Y, PAN D R, XU Y, et al. A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors[J]. Eur Radiol, 2022, 32(2): 1371-1383. DOI: 10.1007/s00330-021-08195-z.
[52]
PARK H, LEE J, LEE S, et al. Grading chondroid tumors through MRI radiomics: enchondroma, low-grade chondrosarcoma and higher-grade chondrosarcoma[J/OL]. BMC Cancer, 2025, 25(1): 918 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/40405123/. DOI: 10.1186/s12885-025-14330-6.
[53]
GITTO S, CUOCOLO R, VAN LANGEVELDE K, et al. MRI radiomics-based machine learning classification of atypical cartilaginous tumour and grade II chondrosarcoma of long bones[J/OL]. EBioMedicine, 2022, 75: 103757 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/34933178/. DOI: 10.1016/j.ebiom.2021.103757.
[54]
GITTO S, CUOCOLO R, ANNOVAZZI A, et al. CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas[J/OL]. EBioMedicine, 2021, 68: 103407 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/34051442/. DOI: 10.1016/j.ebiom.2021.103407.
[55]
PAN J L, ZHANG K, LE H B, et al. Radiomics nomograms based on non-enhanced MRI and clinical risk factors for the differentiation of chondrosarcoma from enchondroma[J]. J Magn Reson Imaging, 2021, 54(4): 1314-1323. DOI: 10.1002/jmri.27690.
[56]
ZHOU Z X, XIE P, DAI Z H, et al. Self-supervised tumor segmentation and prognosis prediction in osteosarcoma using multiparametric MRI and clinical characteristics[J/OL]. Comput Methods Programs Biomed, 2024, 244: 107974 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/38154327/. DOI: 10.1016/j.cmpb.2023.107974.
[57]
PEREIRA H M, LEITE DUARTE M E, RIBEIRO DAMASCENO I, et al. Machine learning-based CT radiomics features for the prediction of pulmonary metastasis in osteosarcoma[J/OL]. Br J Radiol, 2021, 94(1124): 20201391 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/34111978/. DOI: 10.1259/bjr.20201391.
[58]
KAWAGUCHI K, MIYAMA K, ENDO M, et al. Viable tumor cell density after neoadjuvant chemotherapy assessed using deep learning model reflects the prognosis of osteosarcoma[J/OL]. NPJ Precis Oncol, 2024, 8(1): 16 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/38253709/. DOI: 10.1038/s41698-024-00515-y.
[59]
NIE P, ZHAO X, MA J L, et al. Can the preoperative CT-based deep learning radiomics model predict histologic grade and prognosis of chondrosarcoma [J/OL]. Eur J Radiol, 2024, 181: 111719 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/39305748/. DOI: 10.1016/j.ejrad.2024.111719.
[60]
MINETTO M A, GIANNINI A, MCCONNELL R, et al. Common musculoskeletal disorders in the elderly: the star triad[J/OL]. J Clin Med, 2020, 9(4): 1216 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/32340331/. DOI: 10.3390/jcm9041216.
[61]
KEMNITZ J, BAUMGARTNER C F, ECKSTEIN F, et al. Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain[J]. MAGMA, 2020, 33(4): 483-493. DOI: 10.1007/s10334-019-00816-5.
[62]
DING J, CAO P, CHANG H C, et al. Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat-water decomposition MRI[J/OL]. Insights Imaging, 2020, 11(1): 128 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/33252711/. DOI: 10.1186/s13244-020-00946-8.
[63]
OBA H, MATSUI Y, ARAI H, et al. Evaluation of muscle quality and quantity for the assessment of sarcopenia using mid-thigh computed tomography: a cohort study[J/OL]. BMC Geriatr, 2021, 21(1): 239 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/33849469/. DOI: 10.1186/s12877-021-02187-w.
[64]
CHEN Z, JIN M C, HE H Y, et al. Mesenchymal stem cells and macrophages and their interactions in tendon-bone healing[J/OL]. J Orthop Translat, 2023, 39: 63-73 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/37188000/. DOI: 10.1016/j.jot.2022.12.005.
[65]
FRITZ B, FRITZ J. Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches[J]. Skeletal Radiol, 2022, 51(2): 315-329. DOI: 10.1007/s00256-021-03830-8.
[66]
LEE S H, LEE J, OH K S, et al. Automated 3-dimensional MRI segmentation for the posterosuperior rotator cuff tear lesion using deep learning algorithm[J/OL]. PLoS One, 2023, 18(5): e0284111 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/37200275/. DOI: 10.1371/journal.pone.0284111.
[67]
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/OL]. Radiol Artif Intell, 2023, 5(2): e220132 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/37035430/. DOI: 10.1148/ryai.220132.
[68]
FEI Y, WAN Y D, XU L, et al. Novel methods to diagnose rotator cuff tear and predict post-operative re-tear: Radiomics models[J/OL]. Asia Pac J Sports Med Arthrosc Rehabil Technol, 2024, 37: 14-20 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/38766605/. DOI: 10.1016/j.asmart.2024.03.003.
[69]
QU C, YANG H, WANG C, et al. A deep learning approach for anterior cruciate ligament rupture localization on knee MR images[J/OL]. Front Bioeng Biotechnol, 2022, 10: 1024527 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/36246358/. DOI: 10.3389/fbioe.2022.1024527.
[70]
BIEN N, RAJPURKAR P, BALL R L, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet[J/OL]. PLoS Med, 2018, 15(11): e1002699 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/30481176/. DOI: 10.1371/journal.pmed.1002699.
[71]
MINAMOTO Y, AKAGI R, MAKI S, et al. Automated detection of anterior cruciate ligament tears using a deep convolutional neural network[J/OL]. BMC Musculoskelet Disord, 2022, 23(1): 577 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/35705930/. DOI: 10.1186/s12891-022-05524-1.
[72]
CHO S H, KIM Y S. Predicting major preoperative risk factors for retears after arthroscopic rotator cuff repair using machine learning algorithms[J/OL]. J Clin Med, 2025, 14(6): 1843 [2025-11-28]. https://pubmed.ncbi.nlm.nih.gov/40142650/. DOI: 10.3390/jcm14061843.
[73]
CHO S H, KIM Y S. 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. DOI: 10.1177/03635465231189201.
[74]
SHINOHARA I, MIFUNE Y, INUI A, et al. Re-tear after arthroscopic rotator cuff tear surgery: risk analysis using machine learning[J]. J Shoulder Elbow Surg, 2024, 33(4): 815-822. DOI: 10.1016/j.jse.2023.07.017.

PREV Research progress on the application of T1 mapping and T2 mapping technology in gynecological malignant tumors
NEXT New progress in magnetic resonance imaging for evaluating ectopic fat deposition in type 2 diabetes mellitus
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn