{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T18:55:40Z","timestamp":1790016940161,"version":"4.0.1"},"reference-count":214,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Background\/Objectives: Degenerative joint diseases (DJDs) involve intricate molecular disruptions within bone, cartilage, and synovial tissues, often preceding overt radiographic changes. These tissues exhibit complex biomolecular architectures and their degeneration leads to microstructural disorganization and inflammation that are challenging to detect with conventional imaging techniques. This review aims to synthesize recent advances in imaging, computational modeling, and sequencing technologies that enable high-resolution, non-invasive characterization of joint tissue health. Methods: We examined advanced modalities including high-resolution MRI (e.g., T1\u03c1, sodium MRI), quantitative and dual-energy CT (qCT, DECT), and ultrasound elastography, integrating them with radiomics, deep learning, and multi-scale modeling approaches. We also evaluated RNA-seq, spatial transcriptomics, and mass spectrometry-based proteomics for omics-guided imaging biomarker discovery. Results: Emerging technologies now permit detailed visualization of proteoglycan content, collagen integrity, mineralization patterns, and inflammatory microenvironments. Computational frameworks ranging from convolutional neural networks to finite element and agent-based models enhance diagnostic granularity. Multi-omics integration links imaging phenotypes to gene and protein expression, enabling predictive modeling of tissue remodeling, risk stratification, and personalized therapy planning. Conclusions: The convergence of imaging, AI, and molecular profiling is transforming musculoskeletal diagnostics. These synergistic platforms enable early detection, multi-parametric tissue assessment, and targeted intervention. Widespread clinical integration requires robust data infrastructure, regulatory compliance, and physician education, but offers a pathway toward precision musculoskeletal care.<\/jats:p>","DOI":"10.3390\/jpm15070298","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T09:38:27Z","timestamp":1752140307000},"page":"298","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Computational and Imaging Approaches for Precision Characterization of Bone, Cartilage, and Synovial Biomolecules"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8574-2895","authenticated-orcid":false,"given":"Rahul","family":"Kumar","sequence":"first","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, School of Medicine, University of Miami Miller, 1600 NW 10th Avenue, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5707-9009","authenticated-orcid":false,"given":"Kyle","family":"Sporn","sequence":"additional","affiliation":[{"name":"Norton College of Medicine, Upstate Medical University, Syracuse, NY 13210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2294-9140","authenticated-orcid":false,"given":"Vibhav","family":"Prabhakar","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL 60607, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahab","family":"Alnemri","sequence":"additional","affiliation":[{"name":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4384-2693","authenticated-orcid":false,"given":"Akshay","family":"Khanna","sequence":"additional","affiliation":[{"name":"Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19144, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phani","family":"Paladugu","sequence":"additional","affiliation":[{"name":"Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19144, USA"},{"name":"Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8177-2784","authenticated-orcid":false,"given":"Chirag","family":"Gowda","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, School of Medicine, University of Miami Miller, 1600 NW 10th Avenue, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Louis","family":"Clarkson","sequence":"additional","affiliation":[{"name":"School of Medicine, University of Cambridge, Cambridge CB2 1TN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0120-0939","authenticated-orcid":false,"given":"Nasif","family":"Zaman","sequence":"additional","affiliation":[{"name":"Human-Machine Perception Laboratory, Department of Computer Science, University of Nevada Reno, Reno, NV 89557, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9460-1269","authenticated-orcid":false,"given":"Alireza","family":"Tavakkoli","sequence":"additional","affiliation":[{"name":"Human-Machine Perception Laboratory, Department of Computer Science, University of Nevada Reno, Reno, NV 89557, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S27","DOI":"10.1097\/01.blo.0000144854.66565.8f","article-title":"The Role of Cytokines in Cartilage Matrix Degeneration in Osteoarthritis","volume":"427","author":"Goldring","year":"2004","journal-title":"Clin. Orthop. Relat. Res."},{"key":"ref_2","first-page":"465","article-title":"Articular Cartilage and Osteoarthritis","volume":"54","author":"Buckwalter","year":"2005","journal-title":"Instr. Course Lect."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1038\/nrrheum.2010.198","article-title":"The Role of the Cartilage Matrix in Osteoarthritis","volume":"7","author":"Saxne","year":"2011","journal-title":"Nat. Rev. Rheumatol."},{"key":"ref_4","first-page":"303","article-title":"Molecular Mechanisms of Cartilage Destruction in Osteoarthritis","volume":"8","author":"Loeser","year":"2008","journal-title":"J. Musculoskelet. Neuronal Interact."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1002\/1529-0131(200106)44:6<1237::AID-ART214>3.0.CO;2-F","article-title":"Osteoarthritis, an Inflammatory Disease: Potential Implication for the Selection of New Therapeutic Targets","volume":"44","author":"Pelletier","year":"2001","journal-title":"Arthritis Rheum."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1136\/bmj.332.7542.639","article-title":"Osteoarthritis","volume":"332","author":"Hunter","year":"2006","journal-title":"BMJ"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.bone.2012.02.012","article-title":"The Role of Synovitis in Osteoarthritis Pathogenesis","volume":"51","author":"Scanzello","year":"2012","journal-title":"Bone"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1038\/nrrheum.2010.159","article-title":"The Role of Synovitis in Pathophysiology and Clinical Symptoms of Osteoarthritis","volume":"6","author":"Sellam","year":"2010","journal-title":"Nat. Rev. Rheumatol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1002\/nbm.1063","article-title":"Quantitative MRI of Cartilage and Bone: Degenerative Changes in Osteoarthritis","volume":"19","author":"Eckstein","year":"2006","journal-title":"NMR Biomed."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1186\/ar3488","article-title":"Why Radiography Should No Longer Be Considered a Surrogate Outcome Measure for Longitudinal Assessment of Cartilage in Knee Osteoarthritis","volume":"13","author":"Guermazi","year":"2011","journal-title":"Arthritis Res. Ther."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1186\/ar4405","article-title":"Subchondral Bone in Osteoarthritis: Insight into Risk Factors and Microstructural Changes","volume":"15","author":"Li","year":"2013","journal-title":"Arthritis Res. Ther."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.joca.2003.11.003","article-title":"Whole-Organ Magnetic Resonance Imaging Score (WORMS) of the Knee in Osteoarthritis","volume":"12","author":"Peterfy","year":"2004","journal-title":"Osteoarthr. Cartil."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1148\/radiol.11101359","article-title":"Advances in Imaging of Osteoarthritis and Cartilage","volume":"260","author":"Roemer","year":"2011","journal-title":"Radiology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.semarthrit.2010.12.003","article-title":"Imaging of Synovitis in Osteoarthritis: Current Status and Outlook","volume":"41","author":"Hayashi","year":"2011","journal-title":"Semin. Arthritis Rheum."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1148\/rg.311105084","article-title":"Articular Cartilage in the Knee: Current MR Imaging Techniques and Applications in Clinical Practice and Research","volume":"31","author":"Crema","year":"2011","journal-title":"Radiographics"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.csm.2008.08.004","article-title":"New Techniques in Articular Cartilage Imaging","volume":"28","author":"Potter","year":"2009","journal-title":"Clin. Sports Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s11420-005-0131-1","article-title":"Musculoskeletal Imaging Update: Current Applications of Advanced Imaging Techniques to Evaluate the Early and Long-Term Complications of Patients with Orthopedic Implants","volume":"2","author":"Sofka","year":"2006","journal-title":"HSS J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1002\/jmri.25554","article-title":"Prestructural Cartilage Assessment Using MRI","volume":"45","author":"Link","year":"2017","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/j.rdc.2014.07.012","article-title":"Identifying and Treating Preclinical and Early Osteoarthritis","volume":"40","author":"Felson","year":"2014","journal-title":"Rheum. Dis. Clin. N. Am."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1111\/nyas.13572","article-title":"Boskey AL, Imbert L. Bone quality changes associated with aging and disease: A review","volume":"1410","author":"Boskey","year":"2017","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"898","DOI":"10.2741\/2730","article-title":"Regulation of Bone Development and Maintenance by Runx2","volume":"13","author":"Komori","year":"2008","journal-title":"Front. Biosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1177\/1941738109350438","article-title":"The Basic Science of Articular Cartilage: Structure, Composition, and Function","volume":"1","author":"Bedi","year":"2009","journal-title":"Sports Health"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1146\/annurev.bioeng.4.110701.120309","article-title":"Mechano-Electrochemical Properties of Articular Cartilage: Their Inhomogeneities and Anisotropies","volume":"4","author":"Mow","year":"2002","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s13075-017-1229-9","article-title":"Synovitis in Osteoarthritis: Current Understanding with Therapeutic Implications","volume":"19","author":"Mathiessen","year":"2017","journal-title":"Arthritis Res. Ther."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1038\/nrrheum.2016.136","article-title":"Low-Grade Inflammation as a Key Mediator of the Pathogenesis of Osteoarthritis","volume":"12","author":"Robinson","year":"2016","journal-title":"Nat. Rev. Rheumatol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1038\/nrrheum.2010.196","article-title":"Role of Proinflammatory Cytokines in the Pathophysiology of Osteoarthritis","volume":"7","author":"Kapoor","year":"2011","journal-title":"Nat. Rev. Rheumatol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1038\/nrrheum.2012.130","article-title":"Bone Remodelling in Osteoarthritis","volume":"8","author":"Burr","year":"2012","journal-title":"Nat. Rev. Rheumatol."},{"key":"ref_28","first-page":"49","article-title":"Dynamics of Bone and Cartilage Metabolism","volume":"27","author":"Seibel","year":"2006","journal-title":"Clin. Biochem. Rev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"203","DOI":"10.2519\/jospt.1998.28.4.203","article-title":"Composition and Dynamics of Articular Cartilage: Structure, Function, and Maintaining Healthy State","volume":"28","author":"Cohen","year":"1998","journal-title":"J. Orthop. Sports Phys. Ther."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.berh.2008.02.001","article-title":"Cartilage in Normal and Osteoarthritis Conditions","volume":"22","author":"Boileau","year":"2008","journal-title":"Best Pract. Res. Clin. Rheumatol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1002\/art.34453","article-title":"Osteoarthritis: A Disease of the Joint as an Organ","volume":"64","author":"Loeser","year":"2012","journal-title":"Arthritis Rheum."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"899","DOI":"10.2214\/AJR.05.0099","article-title":"MRI of Articular Cartilage: Revisiting Current Status and Future Directions","volume":"185","author":"Recht","year":"2005","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_33","first-page":"1390","article-title":"Comparison of Quantitative and Qualitative Magnetic Resonance Imaging Measures of Cartilage Degeneration in Osteoarthritis","volume":"32","author":"Kijowski","year":"2010","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s11154-006-9004-2","article-title":"New Imaging Technologies in the Diagnosis of Osteoporosis","volume":"7","author":"Kazakia","year":"2006","journal-title":"Rev. Endocr. Metab. Disord."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1002\/1529-0131(200005)43:5<953::AID-ANR1>3.0.CO;2-Q","article-title":"Molecular Basis and Clinical Use of Biochemical Markers of Bone, Cartilage, and Synovium in Joint Diseases","volume":"43","author":"Garnero","year":"2000","journal-title":"Arthritis Rheum."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/j.joca.2010.08.019","article-title":"Application of Biomarkers in the Development of Drugs Intended for the Treatment of Osteoarthritis","volume":"19","author":"Kraus","year":"2011","journal-title":"Osteoarthr. Cartil."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1016\/S0140-6736(11)60243-2","article-title":"Osteoarthritis: An Update with Relevance for Clinical Practice","volume":"377","author":"Bijlsma","year":"2011","journal-title":"Lancet"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1038\/ncprheum0534","article-title":"Mechanisms of Disease: Role of Chondrocytes in the Pathogenesis of Osteoarthritis\u2014Structure, Chaos and Senescence","volume":"3","author":"Aigner","year":"2007","journal-title":"Nat. Clin. Pract. Rheumatol."},{"key":"ref_39","first-page":"1461","article-title":"What Drives Osteoarthritis?\u2014Synovial Versus Subchondral Bone Pathology","volume":"56","author":"Geurts","year":"2017","journal-title":"Rheumatology"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1038\/s41413-023-00304-6","article-title":"Spatial Analysis of the Osteoarthritis Microenvironment: Techniques, Insights, and Applications","volume":"12","author":"Fonseka","year":"2024","journal-title":"Bone Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.rdc.2008.05.011","article-title":"Etiopathogenesis of Osteoarthritis","volume":"34","author":"Brandt","year":"2008","journal-title":"Rheum. Dis. Clin. N. Am."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1136\/ard.16.4.494","article-title":"Radiological Assessment of Osteo-Arthrosis","volume":"16","author":"Kellgren","year":"1957","journal-title":"Ann. Rheum. Dis."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"A1","DOI":"10.1016\/j.joca.2006.11.009","article-title":"Atlas of Individual Radiographic Features in Osteoarthritis, Revised","volume":"15","author":"Altman","year":"2007","journal-title":"Osteoarthr. Cartil."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.joca.2005.07.014","article-title":"Osteoarthritis Cartilage Histopathology Grading and Staging","volume":"14","author":"Pritzker","year":"2006","journal-title":"Osteoarthr. Cartil."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.joca.2011.12.003","article-title":"Chondrocyte Hypertrophy and Osteoarthritis: Role in Initiation and Progression of Cartilage Degeneration?","volume":"20","year":"2012","journal-title":"Osteoarthr. Cartil."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.joca.2012.11.012","article-title":"Osteoarthritis as an Inflammatory Disease (Osteoarthritis Is Not Osteoarthrosis!)","volume":"21","author":"Berenbaum","year":"2013","journal-title":"Osteoarthr. Cartil."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1016\/S0140-6736(05)71086-2","article-title":"Pathogenesis and Management of Pain in Osteoarthritis","volume":"365","author":"Dieppe","year":"2005","journal-title":"Lancet"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/S0140-6736(14)60802-3","article-title":"Osteoarthritis","volume":"386","author":"Palmer","year":"2015","journal-title":"Lancet"},{"key":"ref_49","first-page":"1453","article-title":"The Evolution of Musculoskeletal MRI: From Low to Ultra-High Field Strengths","volume":"47","author":"Winalski","year":"2018","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1055\/s-2004-861764","article-title":"Cartilage MRI T2 Relaxation Time Mapping: Overview and Applications","volume":"8","author":"Mosher","year":"2004","journal-title":"Semin. Musculoskelet. Radiol."},{"key":"ref_51","first-page":"741","article-title":"3D-T1rho-Relaxation Mapping of Articular Cartilage: In Vivo Assessment of Early Degenerative Changes in Symptomatic Osteoarthritic Subjects","volume":"11","author":"Regatte","year":"2004","journal-title":"Acad. Radiol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1002\/mrm.10389","article-title":"Delayed Gadolinium-Enhanced MRI of Cartilage (dGEMRIC) in Early Knee Osteoarthritis","volume":"49","author":"Tiderius","year":"2003","journal-title":"Magn. Reson. Med."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1002\/1522-2594(200101)45:1<36::AID-MRM1006>3.0.CO;2-W","article-title":"Protocol Issues for Delayed Gd(DTPA)(2-)-Enhanced MRI (dGEMRIC) for Clinical Evaluation of Articular Cartilage","volume":"45","author":"Burstein","year":"2001","journal-title":"Magn. Reson. Med."},{"key":"ref_54","first-page":"1627","article-title":"Optimized 3D Magnetization-Prepared Rapid Gradient-Echo Imaging for Knee Cartilage at 3T and 7T","volume":"67","author":"Bieri","year":"2012","journal-title":"Magn. Reson. Med."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1002\/jmri.24168","article-title":"Biomedical Applications of Sodium MRI In Vivo","volume":"38","author":"Madelin","year":"2013","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s40134-014-0041-4","article-title":"Sodium MR Imaging of Articular Cartilage Pathologies","volume":"2","author":"Juras","year":"2014","journal-title":"Curr. Radiol. Rep."},{"key":"ref_57","first-page":"1043","article-title":"Susceptibility Artifacts in High-Field MRI: Mechanisms and Solutions","volume":"50","author":"Chung","year":"2019","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"195","DOI":"10.2463\/mrms.mp.2019-0018","article-title":"Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers","volume":"19","author":"Kidoh","year":"2020","journal-title":"Magn. Reson. Med. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1002\/mrm.21391","article-title":"Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging","volume":"58","author":"Lustig","year":"2007","journal-title":"Magn. Reson. Med."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.2214\/ajr.183.5.1831479","article-title":"Musculoskeletal MRI at 3.0 T: Initial Clinical Experience","volume":"183","author":"Gold","year":"2004","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_61","first-page":"1726","article-title":"Quantitative Susceptibility Mapping in the Evaluation of Osteoporosis","volume":"49","author":"Liu","year":"2019","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Deniz, C.M., Xiang, S., Hallyburton, R.S., Welbeck, A., Babb, J.S., Honig, S., Cho, K., and Chang, G. (2018). Segmentation of the Proximal Femur from MR Images Using Deep Convolutional Neural Networks. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-34817-6"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1016\/j.joca.2017.11.018","article-title":"Systematic review and meta-analysis of the reliability and discriminative validity of cartilage compositional MRI in knee osteoarthritis","volume":"26","author":"MacKay","year":"2018","journal-title":"Osteoarthr. Cartil."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1002\/(SICI)1522-2594(199911)42:5<963::AID-MRM17>3.0.CO;2-L","article-title":"Motion Correction with PROPELLER MRI: Application to Head Motion and Free-Breathing Cardiac Imaging","volume":"42","author":"Pipe","year":"1999","journal-title":"Magn. Reson. Med."},{"key":"ref_65","first-page":"366","article-title":"Brain Imaging at Ultrahigh Field: Pushing the Limits of Sensitivity and Specificity","volume":"168","author":"Ugurbil","year":"2018","journal-title":"Neuroimage"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1002\/mrm.24136","article-title":"Regional Variations of T2* in Healthy and Pathologic Achilles Tendon In Vivo at 7 T: Preliminary Results","volume":"68","author":"Juras","year":"2012","journal-title":"Magn. Reson. Med."},{"key":"ref_67","first-page":"243","article-title":"Improved Prediction of Outcome in Parkinson\u2019s Disease Using Radiomics and Machine Learning Applied to DaTscan SPECT","volume":"46","author":"Rahmim","year":"2019","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1002\/(SICI)1522-2594(199905)41:5<857::AID-MRM1>3.0.CO;2-E","article-title":"Nondestructive Imaging of Human Cartilage Glycosaminoglycan Concentration by MRI","volume":"41","author":"Bashir","year":"1999","journal-title":"Magn. Reson. Med."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jocd.2007.12.010","article-title":"Clinical Use of Quantitative Computed Tomography and Peripheral Quantitative Computed Tomography in the Management of Osteoporosis in Adults: The 2007 ISCD Official Positions","volume":"11","author":"Engelke","year":"2008","journal-title":"J. Clin. Densitom."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"iv9","DOI":"10.1093\/rheumatology\/ken180","article-title":"Advanced CT Bone Imaging in Osteoporosis","volume":"47","author":"Genant","year":"2008","journal-title":"Rheumatology"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1016\/j.berh.2009.09.008","article-title":"Quantifying the Material and Structural Determinants of Bone Strength","volume":"23","author":"Bouxsein","year":"2009","journal-title":"Best. Pract. Res. Clin. Rheumatol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1097\/01.blo.0000164400.37905.22","article-title":"Predicting Proximal Femoral Strength Using Structural Engineering Models","volume":"437","author":"Keyak","year":"2005","journal-title":"Clin. Orthop. Relat. Res."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.jocd.2007.12.002","article-title":"Official Positions of the International Society for Clinical Densitometry and Executive Summary of the 2007 ISCD Pediatric Position Development Conference","volume":"11","author":"Baim","year":"2008","journal-title":"J. Clin. Densitom."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1002\/jbmr.506","article-title":"Simulated Increases in Body Fat and Errors in Bone Mineral Density Measurements by DXA and QCT","volume":"27","author":"Yu","year":"2012","journal-title":"J. Bone Miner. Res."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1007\/s00330-006-0517-6","article-title":"Material Differentiation by Dual Energy CT: Initial Experience","volume":"17","author":"Johnson","year":"2007","journal-title":"Eur. Radiol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1007\/s00330-018-5810-7","article-title":"The Evolution of Image Reconstruction for CT-from Filtered Back Projection to Artificial Intelligence","volume":"29","author":"Willemink","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1016\/j.arthro.2007.08.009","article-title":"Results of Modified Latarjet Reconstruction in Patients with Anteroinferior Instability and Significant Bone Loss","volume":"23","author":"Burkhart","year":"2007","journal-title":"Arthroscopy"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"588","DOI":"10.7326\/0003-4819-158-8-201304160-00003","article-title":"Opportunistic Screening for Osteoporosis Using Abdominal Computed Tomography Scans Obtained for Other Indications","volume":"158","author":"Pickhardt","year":"2013","journal-title":"Ann. Intern. Med."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"491","DOI":"10.2214\/AJR.17.17853","article-title":"Prevalence of Vertebral Compression Fractures on Routine CT Scans According to L1 Trabecular Attenuation: Determining Relevant Thresholds for Opportunistic Osteoporosis Screening","volume":"209","author":"Graffy","year":"2017","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.3174\/ajnr.A3893","article-title":"Bone Mineral Density Values Derived from Routine Lumbar Spine Multidetector Row CT Predict Osteoporotic Vertebral Fractures and Screw Loosening","volume":"35","author":"Schwaiger","year":"2014","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1148\/radiol.14131480","article-title":"State of the Art: Dual-Energy CT of the Abdomen","volume":"271","author":"Marin","year":"2014","journal-title":"Radiology"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1088\/0031-9155\/21\/5\/002","article-title":"Energy-Selective Reconstructions in X-ray Computerized Tomography","volume":"21","author":"Alvarez","year":"1976","journal-title":"Phys. Med. Biol."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"S78","DOI":"10.2214\/AJR.12.9117","article-title":"Dual-Energy CT: A Promising New Technique for the Evaluation of the Musculoskeletal System","volume":"199","author":"Nicolaou","year":"2012","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"5034","DOI":"10.1007\/s00330-017-4931-8","article-title":"Dual Energy CT Iodine Map for Delineating Inflammation of Inflammatory Arthritis","volume":"27","author":"Fukuda","year":"2017","journal-title":"Eur. Radiol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1148\/radiol.2015142631","article-title":"Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications","volume":"276","author":"McCollough","year":"2015","journal-title":"Radiology"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"S9","DOI":"10.2214\/AJR.12.9121","article-title":"Dual-Energy CT-based Monochromatic Imaging","volume":"199","author":"Yu","year":"2012","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1148\/rg.2016150220","article-title":"Material Separation Using Dual-Energy CT: Current and Emerging Applications","volume":"36","author":"Patino","year":"2016","journal-title":"Radiographics"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1148\/radiol.2016151109","article-title":"Dual-Energy CT for the Musculoskeletal System","volume":"281","author":"Mallinson","year":"2016","journal-title":"Radiology"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s00256-016-2533-1","article-title":"Osteitis: A Retrospective Feasibility Study Comparing Single-Source Dual-Energy CT to MRI in Selected Patients with Suspected Acute Gout","volume":"46","author":"Diekhoff","year":"2017","journal-title":"Skelet. Radiol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1055\/s-0035-1569253","article-title":"Dual-Energy CT: Basic Principles, Technical Approaches, and Applications in Musculoskeletal Imaging (Part 1)","volume":"19","author":"Omoumi","year":"2015","journal-title":"Semin. Musculoskelet. Radiol."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"2112","DOI":"10.1007\/s00247-024-06074-5","article-title":"Dual-energy computed tomography: Pediatric considerations","volume":"54","author":"Gee","year":"2024","journal-title":"Pediatr. Radiol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.7150\/thno.18650","article-title":"Ultrasound Elastography: Review of Techniques and Clinical Applications","volume":"7","author":"Sigrist","year":"2017","journal-title":"Theranostics"},{"key":"ref_93","first-page":"1349","article-title":"Ultrasound Backscatter and Attenuation in Human Cartilage with Osteoarthritis: Ex Vivo Study","volume":"32","author":"Saarakkala","year":"2006","journal-title":"Ultrasound Med. Biol."},{"key":"ref_94","first-page":"602","article-title":"Ultrasonography for Assessment of Synovitis in Osteoarthritis: A Reliability Exercise","volume":"26","author":"Wu","year":"2016","journal-title":"Mod. Rheumatol."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1016\/S0301-5629(98)00110-0","article-title":"Shear Wave Elasticity Imaging: A New Ultrasonic Technology of Medical Diagnostics","volume":"24","author":"Sarvazyan","year":"1998","journal-title":"Ultrasound Med. Biol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1148\/rg.2017160116","article-title":"Shear-Wave Elastography: Basic Physics and Musculoskeletal Applications","volume":"37","author":"Taljanovic","year":"2017","journal-title":"Radiographics"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"8698","DOI":"10.1038\/s41598-022-12786-1","article-title":"Reliability of shear wave elastography for the assessment of gastrocnemius fascia elasticity in healthy individual","volume":"12","author":"Zhou","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_98","first-page":"2055","article-title":"The Efficiency of Ultrasound Elastography in the Differential Diagnosis of Soft Tissue Masses","volume":"38","author":"Ozcan","year":"2019","journal-title":"J. Ultrasound Med."},{"key":"ref_99","first-page":"2023","article-title":"Quantifying Subchondral Bone Stiffness Using Shear Wave Elastography: A Pilot Study","volume":"39","author":"Venne","year":"2020","journal-title":"J. Ultrasound Med."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"E137","DOI":"10.1055\/s-0043-119412","article-title":"Strain Elastography\u2014How To Do It?","volume":"3","author":"Dietrich","year":"2017","journal-title":"Ultrasound Int. Open"},{"key":"ref_101","first-page":"2588","article-title":"Quantitative Ultrasound Elastography Combined with RNA-Seq for Detection of Early-Stage Osteoarthritis","volume":"39","author":"Cao","year":"2021","journal-title":"J. Orthop. Res."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1007\/s11517-018-1785-4","article-title":"Vibroarthrography For Early Detection of Knee Osteoarthritis Using Normalized Frequency Features","volume":"56","author":"Befrui","year":"2018","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Karpi\u0144ski, R., Prus, A., Jonak, K., and Krakowski, P. (2025). Vibroarthrography as a Noninvasive Screening Method for Early Diagnosis of Knee Osteoarthritis: A Review of Current Research. Appl. Sci., 15.","DOI":"10.3390\/app15010279"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"90","DOI":"10.35784\/acs-2024-18","article-title":"Application of EEMD-DFA Algorithms and ANN Classification for Detection of Knee Osteoarthritis Using Vibroarthrography","volume":"20","author":"Machrowska","year":"2024","journal-title":"Appl. Comput. Sci."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"19","DOI":"10.12913\/22998624\/189512","article-title":"Application of Recurrence Quantification Analysis in the Detection of Osteoarthritis of the Knee with the Use of Vibroarthrography","volume":"18","author":"Prus","year":"2024","journal-title":"Adv. Sci. Technol. Res. J."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Machrowska, A., Karpi\u0144ski, R., Maciejewski, M., Jonak, J., Krakowski, P., and Syta, A. (2025). Multi-Scale Analysis of Knee Joint Acoustic Signals for Cartilage Degeneration Assessment. Sensors, 25.","DOI":"10.3390\/s25030706"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"1759720X231158198","DOI":"10.1177\/1759720X231158198","article-title":"The Application of Machine Learning in Early Diagnosis of Osteoarthritis: A Narrative Review","volume":"15","author":"Xuan","year":"2023","journal-title":"Ther. Adv. Musculoskelet. Dis."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur. J. Cancer"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in Medical Imaging\u2014\u201cHow-To\u201d Guide and Critical Reflection","volume":"11","author":"Cester","year":"2020","journal-title":"Insights Imaging"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"488","DOI":"10.2967\/jnumed.118.222893","article-title":"Introduction to Radiomics","volume":"61","author":"Mayerhoefer","year":"2020","journal-title":"J. Nucl. Med."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1016\/j.mri.2012.06.010","article-title":"Radiomics: The Process and the Challenges","volume":"30","author":"Kumar","year":"2012","journal-title":"Magn. Reson. Imaging"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"4259","DOI":"10.1158\/1078-0432.CCR-16-2910","article-title":"Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma","volume":"23","author":"Zhang","year":"2017","journal-title":"Clin. Cancer Res."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational Radiomics System to Decode the Radiographic Phenotype","volume":"77","author":"Fedorov","year":"2017","journal-title":"Cancer Res."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Parmar, C., Grossmann, P., Bussink, J., Lambin, P., and Aerts, H.J.W.L. (2015). Machine Learning Methods for Quantitative Radiomic Biomarkers. Sci. Rep., 5.","DOI":"10.3389\/fonc.2015.00272"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Lao, J., Chen, Y., Li, Z.C., Li, Q., Zhang, J., Liu, J., and Zhai, G. (2017). A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme. Sci. Rep., 7.","DOI":"10.1038\/s41598-017-10649-8"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.7150\/thno.30309","article-title":"The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges","volume":"9","author":"Liu","year":"2019","journal-title":"Theranostics"},{"key":"ref_117","first-page":"1219","article-title":"Texture Analysis of Quantitative Susceptibility Maps to Differentiate Type of Knee Cartilage","volume":"54","author":"Peuna","year":"2021","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_118","first-page":"1030","article-title":"Machine Learning Classification of Articular Cartilage Integrity Using MRI Data","volume":"29","author":"Ashinsky","year":"2021","journal-title":"Osteoarthr. Cartil."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-Based Phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1007\/s00259-023-06446-3","article-title":"Integrating TSPO PET imaging and transcriptomics to unveil the role of neuroinflammation and amyloid-\u03b2 deposition in Alzheimer\u2019s disease","volume":"51","author":"Zhang","year":"2024","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"1295511","DOI":"10.3389\/fphar.2023.1295511","article-title":"Application of radiomics in diagnosis and treatment of lung cancer","volume":"14","author":"Pan","year":"2023","journal-title":"Front. Pharmacol."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A Survey on Deep Learning in Medical Image Analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_123","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"9351","author":"Ronneberger","year":"2015","journal-title":"Med. Image Comput. Comput. Assist. Interv."},{"key":"ref_124","first-page":"3347","article-title":"Transfusion: Understanding Transfer Learning for Medical Imaging","volume":"32","author":"Raghu","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.media.2019.03.009","article-title":"Not-So-Supervised: A Survey of Semi-Supervised, Multi-Instance, and Transfer Learning in Medical Image Analysis","volume":"54","author":"Cheplygina","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"101552","DOI":"10.1016\/j.media.2019.101552","article-title":"Generative Adversarial Network in Medical Imaging: A Review","volume":"58","author":"Yi","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Chen, H., Zhao, N., Tan, T., Kang, Y., Sun, C., Xie, G., Verdonschot, N., and Sprengers, A. (2022). Knee Bone and Cartilage Segmentation Based on a 3D Deep Neural Network Using Adversarial Loss for Prior Shape Constraint. Front. Med., 9.","DOI":"10.3389\/fmed.2022.792900"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s10334-020-00889-7","article-title":"Accuracy and Longitudinal Reproducibility of Quantitative Femorotibial Cartilage Measures Derived from Automated U-Net-Based Segmentation of Two Different MRI Contrasts: Data from the Osteoarthritis Initiative Healthy Reference Cohort","volume":"34","author":"Wirth","year":"2021","journal-title":"Magn. Reson. Mater. Phy."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","article-title":"Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images","volume":"53","author":"Schlemper","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Li, P., Wang, Y., Zhao, R., Hao, L., Chai, W., Jiying, C., Feng, Z., Ji, Q., and Zhang, G. (2025). The Application of Artificial Intelligence in Periprosthetic Joint Infection. J. Adv. Res., in press.","DOI":"10.1016\/j.jare.2025.03.039"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Xie, Q., Chen, Y., Hu, Y., Zeng, F., Wang, P., Xu, L., Wu, J., Li, J., Zhu, J., and Xiang, M. (2022). Development and Validation of a Machine Learning-Derived Radiomics Model for Diagnosis of Osteoporosis and Osteopenia Using Quantitative Computed Tomography. BMC Med. Imaging, 22.","DOI":"10.1186\/s12880-022-00868-5"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Tiulpin, A., Klein, S., Bierma-Zeinstra, S.M.A., Thevenot, J., Rahtu, E., van Meurs, J., Oei, E.H.G., and Saarakkala, S. (2019). Multimodal Machine Learning-Based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-56527-3"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1093\/bioinformatics\/btp120","article-title":"TopHat: Discovering Splice Junctions with RNA-Seq","volume":"25","author":"Trapnell","year":"2009","journal-title":"Bioinformatics"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"1888","DOI":"10.1016\/j.cell.2019.05.031","article-title":"Comprehensive Integration of Single-Cell Data","volume":"177","author":"Stuart","year":"2019","journal-title":"Cell"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1126\/science.aaf2403","article-title":"Visualization and Analysis of Gene Expression in Tissue Sections by Spatial Transcriptomics","volume":"353","author":"Vickovic","year":"2016","journal-title":"Science"},{"key":"ref_136","first-page":"351","article-title":"Canonical Correlation Analysis for Multi-Omics Data Integration","volume":"1910","author":"Sun","year":"2019","journal-title":"Methods Mol. Biol."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Love, M.I., Huber, W., and Anders, S. (2014). Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2. Genome Biol., 15.","DOI":"10.1186\/s13059-014-0550-8"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1093\/bioinformatics\/btp515","article-title":"integrOmics: An R Package to Unravel Relationships Between Two Omics Datasets","volume":"25","year":"2009","journal-title":"Bioinformatics"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"2906","DOI":"10.1093\/bioinformatics\/btp543","article-title":"Integrative Clustering of Multiple Genomic Data Types Using a Joint Latent Variable Model with Application to Breast and Lung Cancer Subtype Analysis","volume":"25","author":"Shen","year":"2009","journal-title":"Bioinformatics"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1093\/bioinformatics\/bts655","article-title":"iBAG: Integrative Bayesian Analysis of High-Dimensional Multiplatform Genomics Data","volume":"29","author":"Wang","year":"2013","journal-title":"Bioinformatics"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1038\/s41586-021-03634-9","article-title":"Exploring Tissue Architecture Using Spatial Transcriptomics","volume":"596","author":"Rao","year":"2021","journal-title":"Nature"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1038\/s41587-020-0591-3","article-title":"Generalizing RNA Velocity to Transient Cell States Through Dynamical Modeling","volume":"38","author":"Bergen","year":"2020","journal-title":"Nat. Biotechnol."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1038\/s41592-022-01409-2","article-title":"Museum of Spatial Transcriptomics","volume":"19","author":"Moses","year":"2022","journal-title":"Nat. Methods"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1093\/biostatistics\/kxj037","article-title":"Adjusting Batch Effects in Microarray Expression Data Using Empirical Bayes Methods","volume":"8","author":"Johnson","year":"2007","journal-title":"Biostatistics"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1016\/j.cell.2021.04.048","article-title":"Integrated Analysis of Multimodal Single-Cell Data","volume":"184","author":"Hao","year":"2021","journal-title":"Cell"},{"key":"ref_146","first-page":"80","article-title":"Extracting a Biologically Relevant Latent Space from Cancer Transcriptomes with Variational Autoencoders","volume":"23","author":"Way","year":"2018","journal-title":"Pac. Symp. Biocomput."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1038\/nature01511","article-title":"Mass Spectrometry-Based Proteomics","volume":"422","author":"Aebersold","year":"2003","journal-title":"Nature"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"2301","DOI":"10.1038\/nprot.2016.136","article-title":"The MaxQuant Computational Platform for Mass Spectrometry-Based Shotgun Proteomics","volume":"11","author":"Tyanova","year":"2016","journal-title":"Nat. Protoc."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"2869","DOI":"10.1016\/j.jprot.2011.12.042","article-title":"Differential Protein Profiling of Synovial Fluid from Rheumatoid Arthritis and Osteoarthritis Patients Using LC-MS\/MS","volume":"75","author":"Mateos","year":"2012","journal-title":"J. Proteomics"},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Rohart, F., Gautier, B., Singh, A., and L\u00ea Cao, K.A. (2017). mixOmics: An R Package for \u2018omics Feature Selection and Multiple Data Integration. PLoS Comput. Biol., 13.","DOI":"10.1101\/108597"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"O111.016717","DOI":"10.1074\/mcp.O111.016717","article-title":"Targeted Data Extraction of the MS\/MS Spectra Generated by Data-Independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis","volume":"11","author":"Gillet","year":"2012","journal-title":"Mol. Cell. Proteom."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/s41592-019-0638-x","article-title":"DIA-NN: Neural Networks and Interference Correction Enable Deep Proteome Coverage in High Throughput","volume":"17","author":"Demichev","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1038\/nbt.1511","article-title":"MaxQuant Enables High Peptide Identification Rates, Individualized p.p.b.-Range Mass Accuracies and Proteome-Wide Protein Quantification","volume":"26","author":"Cox","year":"2008","journal-title":"Nat. Biotechnol."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"463","DOI":"10.2147\/DDDT.S153927","article-title":"Synovial tissue quantitative proteomics analysis reveals paeoniflorin decreases LIFR and ASPN proteins in experimental rheumatoid arthritis","volume":"12","author":"Yang","year":"2018","journal-title":"Drug. Des. Devel. Ther."},{"key":"ref_155","first-page":"1802","article-title":"Integrative Transcriptomic and Proteomic Analysis of Osteoarthritic Cartilage and Synovium","volume":"39","author":"Takashima","year":"2021","journal-title":"J. Orthop. Res."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1016\/j.molcel.2006.06.024","article-title":"UBE2T Is the E2 in the Fanconi Anemia Pathway and Undergoes Negative Autoregulation","volume":"23","author":"Machida","year":"2006","journal-title":"Mol. Cell"},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1038\/nature13438","article-title":"Proteogenomic Characterization of Human Colon and Rectal Cancer","volume":"513","author":"Zhang","year":"2014","journal-title":"Nature"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1007\/s00216-007-1486-6","article-title":"Quantitative Mass Spectrometry in Proteomics: A Critical Review","volume":"389","author":"Bantscheff","year":"2007","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1038\/nature13319","article-title":"Mass-Spectrometry-Based Draft of the Human Proteome","volume":"509","author":"Wilhelm","year":"2014","journal-title":"Nature"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1109\/TPAMI.2004.60","article-title":"An Experimental Comparison of Min-Cut\/Max-Flow Algorithms for Energy Minimization in Vision","volume":"26","author":"Boykov","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_162","first-page":"5998","article-title":"Attention Is All You Need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_163","first-page":"424","article-title":"3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation","volume":"9901","author":"Abdulkadir","year":"2016","journal-title":"Med. Image Comput. Comput. Assist. Interv."},{"key":"ref_164","first-page":"178","article-title":"Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks","volume":"11383","author":"Wang","year":"2018","journal-title":"Brainlesion"},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","article-title":"Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?","volume":"35","author":"Tajbakhsh","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation","volume":"36","author":"Kamnitsas","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1016\/j.cell.2020.06.038","article-title":"Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer\u2019s Disease","volume":"182","author":"Palla","year":"2020","journal-title":"Cell"},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1038\/s41586-019-1923-7","article-title":"Improved Protein Structure Prediction Using Potentials from Deep Learning","volume":"577","author":"Senior","year":"2020","journal-title":"Nature"},{"key":"ref_169","first-page":"411","article-title":"Attention U-Net: Learning Where to Look for the Pancreas","volume":"11070","author":"Oktay","year":"2018","journal-title":"Med. Image Comput. Comput. Assist. Interv."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","article-title":"Brain Tumor Segmentation with Deep Neural Networks","volume":"35","author":"Havaei","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_171","unstructured":"Kohavi, R. (1995, January 20\u201325). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, QC, Canada."},{"key":"ref_172","first-page":"4765","article-title":"A Unified Approach to Interpreting Model Predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial Intelligence in Radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat. Rev. Cancer"},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1002\/(SICI)1521-1878(200002)22:2<138::AID-BIES5>3.0.CO;2-4","article-title":"All for One and One for All: Condensations and the Initiation of Skeletal Development","volume":"22","author":"Hall","year":"2000","journal-title":"Bioessays"},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1038\/35015116","article-title":"Effects of mechanical forces on maintenance and adaptation of form in trabecular bone","volume":"405","author":"Huiskes","year":"2000","journal-title":"Nature"},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/S0955-0674(03)00017-6","article-title":"Sniffers, Buzzers, Toggles and Blinkers: Dynamics of Regulatory and Signaling Pathways in the Cell","volume":"15","author":"Tyson","year":"2003","journal-title":"Curr. Opin. Cell Biol."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1146\/annurev.genom.2.1.343","article-title":"A New Approach to Decoding Life: Systems Biology","volume":"2","author":"Ideker","year":"2001","journal-title":"Annu. Rev. Genom. Hum. Genet."},{"key":"ref_178","first-page":"104","article-title":"The VPH\u2014Physiome Project: Standards and Tools for Multiscale Modelling in Clinical Applications","volume":"1","author":"Viceconti","year":"2008","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1016\/j.cell.2015.05.002","article-title":"Highly Parallel Genome-Wide Expression Profiling of Individual Cells Using Nanoliter Droplets","volume":"161","author":"Macosko","year":"2015","journal-title":"Cell"},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1186\/ar3657","article-title":"Is There Any Scientific Evidence for the Use of Glucosamine in the Management of Human Osteoarthritis?","volume":"14","author":"Henrotin","year":"2012","journal-title":"Arthritis Res. Ther."},{"key":"ref_181","first-page":"389","article-title":"Analysis of Chondrocyte Functional Marker Expression in Human Chondrocyte Cultures","volume":"300","author":"Cristino","year":"2000","journal-title":"Cell Tissue Res."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1148\/radiology.205.2.9356644","article-title":"Glycosaminoglycan in Articular Cartilage: In Vivo Assessment with Delayed Gd(DTPA)(2-)-Enhanced MR Imaging","volume":"205","author":"Bashir","year":"1997","journal-title":"Radiology"},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1002\/jmri.21091","article-title":"Three-Dimensional Delayed Gadolinium-Enhanced MRI of Cartilage (dGEMRIC) for In Vivo Evaluation of Reparative Cartilage After Matrix-Associated Autologous Chondrocyte Transplantation at 3.0T: Preliminary Results","volume":"26","author":"Trattnig","year":"2007","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.2106\/00004623-200608000-00010","article-title":"Risk Factors for Recurrence of Shoulder Instability After Arthroscopic Bankart Repair","volume":"88","author":"Boileau","year":"2006","journal-title":"J. Bone Jt. Surg. Am."},{"key":"ref_185","first-page":"184","article-title":"Imaging of Synovial Inflammation: State of the Art and Future Perspectives","volume":"65","author":"Glaser","year":"2008","journal-title":"Eur. J. Radiol."},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images Are More than Pictures, They Are Data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1097\/BRS.0000000000002442","article-title":"Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion","volume":"43","author":"Kim","year":"2018","journal-title":"Spine"},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/nrg2484","article-title":"RNA-Seq: A Revolutionary Tool for Transcriptomics","volume":"10","author":"Wang","year":"2009","journal-title":"Nat. Rev. Genet."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1055\/s-0030-1254521","article-title":"Is Sonoelastography of Value in Assessing Tendons?","volume":"14","author":"Klauser","year":"2010","journal-title":"Semin. Musculoskelet. Radiol."},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1056\/NEJMp1500523","article-title":"A New Initiative on Precision Medicine","volume":"372","author":"Collins","year":"2015","journal-title":"N. Engl. J. Med."},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-Performance Medicine: The Convergence of Human and Artificial Intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_193","doi-asserted-by":"crossref","unstructured":"Parmar, C., Rios Velazquez, E., Leijenaar, R., Jermoumi, M., Carvalho, S., Mak, R.H., Tyagi, N., Yan, B.P., Lambin, P., and Aerts, H.J. (2014). Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0102107"},{"key":"ref_194","first-page":"329","article-title":"Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning","volume":"7","author":"Pearl","year":"1985","journal-title":"Proc. Cogn. Sci. Soc."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The Graph Neural Network Model","volume":"20","author":"Scarselli","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1038\/s41551-021-00751-8","article-title":"Synthetic Data in Machine Learning for Medicine and Healthcare","volume":"5","author":"Chen","year":"2021","journal-title":"Nat. Biomed. Eng."},{"key":"ref_197","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_198","unstructured":"Gal, Y., and Ghahramani, Z. (2016, January 20\u201322). Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1186\/s12974-021-02364-z","article-title":"Circulating extracellular vesicles activate the pyroptosis pathway in the brain following ventilation-induced lung injury","volume":"18","author":"Chavez","year":"2021","journal-title":"J. Neuroinflamm."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1186\/ar113","article-title":"Fibroblast Biology in Rheumatoid Arthritis: Synovial Fibroblasts as Effector Cells in Inflammation and Tissue Destruction","volume":"2","author":"Pap","year":"2000","journal-title":"Arthritis Res."},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1053\/joca.1998.0161","article-title":"The Extracellular Matrix, Interstitial Fluid and Ions as a Mechanical Signal Transducer in Articular Cartilage","volume":"7","author":"Mow","year":"1999","journal-title":"Osteoarthr. Cartil."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_203","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv."},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1377\/hlthaff.2014.0041","article-title":"Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients","volume":"33","author":"Bates","year":"2014","journal-title":"Health Aff."},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.ejrad.2010.05.004","article-title":"Open Source Software and Social Networks: Disruptive Alternatives for Medical Imaging","volume":"78","author":"Ratib","year":"2011","journal-title":"Eur. J. Radiol."},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.joca.2024.10.009","article-title":"Osteoarthritis Year in Review 2024: Imaging","volume":"33","author":"Hayashi","year":"2025","journal-title":"Osteoarthr. Cartil."},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"1530","DOI":"10.1016\/j.joca.2024.09.011","article-title":"Osteoarthritis Year in Review 2024: Biomechanics","volume":"32","author":"Ewald","year":"2024","journal-title":"Osteoarthr. Cartil."},{"key":"ref_208","doi-asserted-by":"crossref","unstructured":"Atkinson, H.F., Birmingham, T.B., Moyer, R.F., Yacoub, D., Kanko, L.E., Bryant, D.M., Thiessen, J.D., and Thompson, R.T. (2019). MRI T2 and T1\u03c1 Relaxation in Patients at Risk for Knee Osteoarthritis: A Systematic Review and Meta-Analysis. BMC Musculoskelet. Disord., 20.","DOI":"10.1186\/s12891-019-2547-7"},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1186\/s13244-023-01407-8","article-title":"Dual-Energy CT Virtual Non-Calcium: An Accurate Method for Detection of Knee Osteoarthritis-Related Edema-like Marrow Signal Intensity","volume":"14","author":"Zhao","year":"2023","journal-title":"Insights Imaging"},{"key":"ref_210","doi-asserted-by":"crossref","unstructured":"Eskelinen, A.S.A., Tanska, P., Florea, C., Orozco, G.A., Julkunen, P., Grodzinsky, A.J., and Korhonen, R.K. (2020). Mechanobiological Model for Simulation of Injured Cartilage Degradation via Pro-Inflammatory Cytokines and Mechanical Stimulus. PLoS Comput. Biol., 16.","DOI":"10.1371\/journal.pcbi.1007998"},{"key":"ref_211","doi-asserted-by":"crossref","unstructured":"Dubey, A., Uldin, H., Khan, Z., Panchal, H., Iyengar, K.P., and Botchu, R. (2025). Role of Artificial Intelligence in Musculoskeletal Interventions. Cancers, 17.","DOI":"10.3390\/cancers17101615"},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s41747-024-00422-8","article-title":"AI Applications in Musculoskeletal Imaging: A Narrative Review","volume":"8","author":"Gitto","year":"2024","journal-title":"Eur. Radiol. Exp."},{"key":"ref_213","first-page":"321","article-title":"Reproducibility and Interpretability in Radiomics: A Critical Assessment","volume":"31","year":"2025","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_214","first-page":"133","article-title":"Application of Artificial Intelligence in Medical Education: Current Scenario and Future Perspectives","volume":"11","author":"Mir","year":"2023","journal-title":"J. Adv. Med. Educ. Prof."}],"container-title":["Journal of Personalized Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4426\/15\/7\/298\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:07:02Z","timestamp":1760033222000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4426\/15\/7\/298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,9]]},"references-count":214,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["jpm15070298"],"URL":"https:\/\/doi.org\/10.3390\/jpm15070298","relation":{},"ISSN":["2075-4426"],"issn-type":[{"value":"2075-4426","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,9]]}}}