{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T14:35:48Z","timestamp":1791815748064,"version":"4.3.4"},"reference-count":184,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Degenerative joint disease remains a leading cause of global disability, with early diagnosis posing a significant clinical challenge due to its gradual onset and symptom overlap with other musculoskeletal disorders. This review focuses on emerging diagnostic strategies by synthesizing evidence specifically from studies that integrate biochemical biomarkers, advanced imaging techniques, and machine learning models relevant to osteoarthritis. We evaluate the diagnostic utility of cartilage degradation markers (e.g., CTX-II, COMP), inflammatory cytokines (e.g., IL-1\u03b2, TNF-\u03b1), and synovial fluid microRNA profiles, and how they correlate with quantitative imaging readouts from T2-mapping MRI, ultrasound elastography, and dual-energy CT. Furthermore, we highlight recent developments in radiomics and AI-driven image interpretation to assess joint space narrowing, osteophyte formation, and subchondral bone changes with high fidelity. The integration of these datasets using multimodal learning approaches offers novel diagnostic phenotypes that stratify patients by disease stage and risk of progression. Finally, we explore the implementation of these tools in point-of-care diagnostics, including portable imaging devices and rapid biomarker assays, particularly in aging and underserved populations. By presenting a unified diagnostic pipeline, this article advances the future of early detection and personalized monitoring in joint degeneration.<\/jats:p>","DOI":"10.3390\/diagnostics15111418","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T08:01:07Z","timestamp":1748937667000},"page":"1418","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Biomarker-Guided Imaging and AI-Augmented Diagnosis of Degenerative Joint Disease"],"prefix":"10.3390","volume":"15","author":[{"given":"Rahul","family":"Kumar","sequence":"first","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, 1011 NW 15th Street, Gautier Building, MC R629, 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":"Department of Medicine, Upstate Medical University Norton College of Medicine, Syracuse, NY 13202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aryan","family":"Borole","sequence":"additional","affiliation":[{"name":"Department of Medicine, Robert Wood Johnson Medical School, New Brunswick, NJ 08901, 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 19107, 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, University of Miami Miller School of Medicine, 1011 NW 15th Street, Gautier Building, MC R629, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phani","family":"Paladugu","sequence":"additional","affiliation":[{"name":"Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA"},{"name":"Brigham and Women\u2019s Hospital, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Ngo","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, 1011 NW 15th Street, Gautier Building, MC R629, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ram","family":"Jagadeesan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Whiting School of Engineering Johns Hopkins University, Baltimore, MD 21218, USA"},{"name":"Cisco AI Systems, Cisco Inc., San Jose, CA 95134, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0120-0939","authenticated-orcid":false,"given":"Nasif","family":"Zaman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Nevada, Reno, NV 89512, 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":"Department of Computer Science, University of Nevada, Reno, NV 89512, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1016\/S0140-6736(19)30417-9","article-title":"Osteoarthritis","volume":"393","author":"Hunter","year":"2019","journal-title":"Lancet"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"16072","DOI":"10.1038\/nrdp.2016.72","article-title":"Osteoarthritis","volume":"2","author":"Barr","year":"2016","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"54","DOI":"10.2106\/JBJS.H.01385","article-title":"Plain Radiography and Magnetic Resonance Imaging Diagnostics in Osteoarthritis: Validated Staging and Scoring","volume":"91","author":"Guermazi","year":"2009","journal-title":"J. Bone Jt. Surg. Am."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e130","DOI":"10.2106\/JBJS.M.01209","article-title":"Osteoarthritis: From Palliation to Prevention: AOA Critical Issues","volume":"96","author":"Chu","year":"2014","journal-title":"J. Bone Jt. Surg. Am."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.joca.2012.09.012","article-title":"Osteoarthritis as a Disease of Mechanics","volume":"21","author":"Felson","year":"2013","journal-title":"Osteoarthr. Cartil."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1038\/nrrheum.2017.50","article-title":"The Role of Metabolism in the Pathogenesis of Osteoarthritis","volume":"13","author":"Mobasheri","year":"2017","journal-title":"Nat. Rev. Rheumatol."},{"key":"ref_9","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_10","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_11","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1016\/j.joca.2006.04.001","article-title":"Classification of Osteoarthritis Biomarkers: A Proposed Approach","volume":"14","author":"Bauer","year":"2006","journal-title":"Osteoarthr. Cartil."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tiulpin, A., Thevenot, J., Rahtu, E., Lehenkari, P., and Saarakkala, S. (2018). Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-20132-7"},{"key":"ref_13","first-page":"686","article-title":"Blood and Urinary Collagen Markers in Osteoarthritis: Markers of Tissue Turnover and Disease Activity","volume":"28","author":"Engstrom","year":"2020","journal-title":"Osteoarthr. Cartil."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kolhe, R., Hunter, M., Liu, S., Jadeja, R.N., Pundkar, C., Mondal, A.K., Mendhe, B., Drewry, M., Rojiani, M.V., and Liu, Y. (2017). Gender-Specific Differential Expression of Exosomal miRNA in Synovial Fluid of Patients with Osteoarthritis. Sci. Rep., 7.","DOI":"10.1038\/s41598-017-01905-y"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1097\/BOR.0000000000000163","article-title":"Conventional and Novel Imaging Modalities in Osteoarthritis: Current State of the Evidence","volume":"27","author":"Demehri","year":"2015","journal-title":"Curr. Opin. Rheumatol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.joca.2012.01.016","article-title":"Diagnostic Performance of Knee Ultrasonography for Detecting Degenerative Changes of Articular Cartilage","volume":"20","author":"Saarakkala","year":"2012","journal-title":"Osteoarthr. Cartil."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1055\/s-0039-1695720","article-title":"Cartilage Imaging in Osteoarthritis","volume":"23","author":"Alizai","year":"2019","journal-title":"Semin. Musculoskelet. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mahum, R., Rehman, S.U., Meraj, T., Rauf, H.T., Irtaza, A., El-Sherbeeny, A.M., and El-Meligy, M.A. (2021). A Novel Hybrid Approach Based on Deep CNN to Detect Knee Osteoarthritis. Sensors, 21.","DOI":"10.3390\/s21186189"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9763","DOI":"10.1007\/s00521-023-08214-8","article-title":"Improving Knee Osteoarthritis Classification Using Multimodal Intermediate Fusion of X-ray, MRI, and Clinical Information","volume":"35","author":"Guida","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1016\/j.joca.2018.10.009","article-title":"Cartilage Oligomeric Matrix Protein, C-terminal Cross-linking Telopeptide of Type II Collagen, and Matrix Metalloproteinase-3 as Biomarkers for Knee and Hip Osteoarthritis Diagnosis: A Systematic Review and Meta-analysis","volume":"27","author":"Hao","year":"2019","journal-title":"Osteoarthr. Cartil."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1016\/j.joca.2017.04.004","article-title":"Quantification of Cartilage Oligomeric Matrix Protein (COMP) and a COMP Neoepitope in Synovial Fluid of Patients with Different Joint Disorders by Novel Automated Assays","volume":"25","author":"Lorenzo","year":"2017","journal-title":"Osteoarthr. Cartil."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1002\/art.42376","article-title":"Cartilage Acidic Protein 1 in Plasma Associates with Prevalent Osteoarthritis and Predicts Future Risk as Well as Progression to Joint Replacements:Results from the UK Biobank Resource","volume":"75","author":"Styrkarsdottir","year":"2023","journal-title":"Arthritis Rheumatol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1093\/rheumatology\/keac415","article-title":"Plasma Proteomics Identifies CRTAC1 as a Biomarker for Osteoarthritis Severity and Progression","volume":"62","author":"Szilagyi","year":"2023","journal-title":"Rheumatology"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1016\/j.joca.2018.04.019","article-title":"Associations Between Molecular Biomarkers and MR-based Cartilage Composition and Knee Joint Morphology: Data from the Osteoarthritis Initiative","volume":"26","author":"Joseph","year":"2018","journal-title":"Osteoarthr. Cartil."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1053\/joca.2001.0434","article-title":"Serum Cartilage Oligomeric Matrix Protein Reflects the Presence of Clinically Diagnosed Synovitis in Patients with Knee Osteoarthritis","volume":"9","author":"Kraus","year":"2001","journal-title":"Osteoarthr. Cartil."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"33","DOI":"10.4137\/BMI.S645","article-title":"Cartilage Oligomeric Matrix Protein (COMP): A Biomarker of Arthritis","volume":"4","author":"Tseng","year":"2009","journal-title":"Biomark. Insights"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1136\/ard.60.6.619","article-title":"Cross Sectional Evaluation of Biochemical Markers of Bone, Cartilage, and Synovial Tissue Metabolism in Patients with Knee Osteoarthritis: Relations with Disease Activity and Joint Damage","volume":"60","author":"Garnero","year":"2001","journal-title":"Ann. Rheum. Dis."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1097\/BOR.0b013e32829fb3b5","article-title":"Type II collagen markers in osteoarthritis: What do they indicate?","volume":"19","author":"Henrotin","year":"2007","journal-title":"Curr. Opin. Rheumatol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1038\/ncprheum0508","article-title":"Biological Markers in Osteoarthritis","volume":"3","author":"Rousseau","year":"2007","journal-title":"Nat. Clin. Pract. Rheumatol."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"2905","DOI":"10.1002\/art.39279","article-title":"Low-Grade Inflammation in Symptomatic Knee Osteoarthritis: Prognostic Value of Inflammatory Plasma Lipids and Peripheral Blood Leukocyte Biomarkers","volume":"67","author":"Attur","year":"2015","journal-title":"Arthritis Rheumatol."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1177\/1759720X12467868","article-title":"Role of Inflammation in the Pathogenesis of Osteoarthritis: Latest Findings and Interpretations","volume":"5","author":"Sokolove","year":"2013","journal-title":"Ther. Adv. Musculoskelet. Dis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1038\/nrrheum.2013.14","article-title":"Mechanistic Biomarkers for Clinical Decision Making in Rheumatic Diseases","volume":"9","author":"Robinson","year":"2013","journal-title":"Nat. Rev. Rheumatol."},{"key":"ref_36","unstructured":"Ortona, E., Pagano, M.T., Capossela, L., Malorni, W., and Gagliardi, M.C. (2024). The Role of Sex in Osteoarthritis and Biomechanical Factors. Int. J. Mol. Sci., 25."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"98","DOI":"10.2174\/1573397113666170807122312","article-title":"Novel Insights into the Pathogenesis of Osteoarthritis","volume":"14","author":"Geyer","year":"2018","journal-title":"Curr. Rheumatol. Rev."},{"key":"ref_38","unstructured":"Liu, F., Ding, W., Fang, Y., Yang, J., Zhang, Y., Ding, C., Huang, B., and Wang, K. (2024). Inflammatory Molecular Cluster in Knee Osteoarthritis Derived from Transcriptome and Machine Learning Analysis. Int. J. Mol. Sci., 25."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"18","DOI":"10.7326\/0003-4819-109-1-18","article-title":"Obesity and Knee Osteoarthritis. The Framingham Study","volume":"109","author":"Felson","year":"1988","journal-title":"Ann. Intern. Med."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"561459","DOI":"10.1155\/2014\/561459","article-title":"The Role of Inflammatory and Anti-inflammatory Cytokines in the Pathogenesis of Osteoarthritis","volume":"2014","author":"Wojdasiewicz","year":"2014","journal-title":"Mediat. Inflamm."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1097\/BOR.0b013e328349c2b1","article-title":"Inflammation in Osteoarthritis","volume":"23","author":"Goldring","year":"2011","journal-title":"Curr. Opin. Rheumatol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1016\/j.joca.2010.08.016","article-title":"Circulating Levels of IL-6 and TNF-\u03b1 Are Associated with Knee Radiographic Osteoarthritis and Knee Cartilage Loss in Older Adults","volume":"18","author":"Stannus","year":"2010","journal-title":"Osteoarthr. Cartil."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1002\/art.24598","article-title":"Interleukin-6 Is a Significant Predictor of Radiographic Knee Osteoarthritis: The Chingford Study","volume":"60","author":"Livshits","year":"2009","journal-title":"Arthritis Rheum."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1038\/nm.3260","article-title":"How Cytokine Networks Fuel Inflammation: Toward a Cytokine-Based Disease Taxonomy","volume":"19","author":"Schett","year":"2013","journal-title":"Nat. Med."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.joca.2006.10.010","article-title":"Elevated High-Sensitivity C-Reactive Protein Levels Are Associated with Local Inflammatory Findings in Patients with Osteoarthritis","volume":"15","author":"Pearle","year":"2007","journal-title":"Osteoarthr. Cartil."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1002\/art.1780400419","article-title":"Low-Level Increases in Serum C-Reactive Protein Are Present in Early Osteoarthritis and Increase with Disease Progression","volume":"40","author":"Spector","year":"1997","journal-title":"Arthritis Rheum."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1016\/j.berh.2004.05.010","article-title":"Erosive Osteoarthritis","volume":"18","author":"Punzi","year":"2004","journal-title":"Best Pract. Res. Clin. Rheumatol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Sun, A.R., Wu, X., Liu, B., Chen, Y., Armitage, C.W., Kress, H., Wang, Z., Tian, P., Crawford, R., and Prasadam, I. (2015). Associations Among Inflammatory Biomarkers in the Circulating, Plasmatic, Salivary and Intraluminal Anatomical Compartments in Apparently Healthy Young Adults. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0129158"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.cyto.2015.08.260","article-title":"The role of cytokines in the pathogenesis of rheumatoid arthritis\u2014Practical and potential application of cytokines as biomarkers and targets of personalized therapy","volume":"76","author":"Brzustewicz","year":"2015","journal-title":"Cytokine"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Boffa, A., Merli, G., Andriolo, L., Lattermann, C., Salzmann, G.M., and Filardo, G. (2021). Synovial Fluid Biomarkers in Knee Osteoarthritis: A Systematic Review and Meta-Analysis. J. Clin. Med., 10.","DOI":"10.1177\/1947603520942941"},{"key":"ref_54","first-page":"360","article-title":"Osteoarthritis Biomarkers: Year in Review","volume":"32","author":"Struglics","year":"2024","journal-title":"Osteoarthr. Cartil."},{"key":"ref_55","first-page":"191","article-title":"The Influence of Preanalytical Variables on Soluble CD40 Ligand Levels in Patients with Knee Osteoarthritis","volume":"15","author":"Jayadev","year":"2012","journal-title":"Int. J. Rheum. Dis."},{"key":"ref_56","first-page":"S218","article-title":"Large Scale Meta-Analysis of Urinary C-Telopeptide of Type II Collagen (uCTX-II) as a Biomarker of Osteoarthritis and Joint Diseases","volume":"22","author":"Valdes","year":"2014","journal-title":"Osteoarthr. Cartil."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.semarthrit.2015.06.001","article-title":"Plain radiography or magnetic resonance imaging (MRI): Which is better in assessing outcome in clinical trials of disease-modifying osteoarthritis drugs? Summary of a debate held at the World Congress of Osteoarthritis 2014","volume":"45","author":"Eckstein","year":"2015","journal-title":"Semin. Arthritis Rheum."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1016\/j.joca.2013.07.010","article-title":"Osteoarthritis Year 2013: Imaging","volume":"21","author":"Mosher","year":"2013","journal-title":"Osteoarthr. Cartil."},{"key":"ref_59","first-page":"613","article-title":"Advanced Imaging of Osteoarthritis: Radiography, CT, and Dual Energy CT","volume":"60","author":"Baumgartner","year":"2022","journal-title":"Radiol. Clin. N. Am."},{"key":"ref_60","first-page":"1106","article-title":"Layer-Specific Analysis of Femorotibial Cartilage T2 Relaxation Time in Knees with and without Early Osteoarthritis","volume":"25","author":"Wirth","year":"2017","journal-title":"Osteoarthr. Cartil."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1136\/annrheumdis-2013-204157","article-title":"Early T2 Changes Predict Onset of Radiographic Knee Osteoarthritis: Data from the Osteoarthritis Initiative","volume":"74","author":"Liebl","year":"2015","journal-title":"Ann. Rheum. Dis."},{"key":"ref_62","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_63","first-page":"99","article-title":"Reproducibility of 3D Delayed Gadolinium Enhanced MRI of Cartilage (dGEMRIC) of the Knee at 3T in Patients with Early Stage Osteoarthritis","volume":"21","author":"Bron","year":"2013","journal-title":"Osteoarthr. Cartil."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1007\/s00256-020-03394-z","article-title":"Prevalence of Abnormal Findings in 230 Knees of Asymptomatic Adults Using 3.0 T MRI","volume":"49","author":"Horga","year":"2020","journal-title":"Skelet. Radiol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1148\/radiol.2511081133","article-title":"Vastly undersampled isotropic projection steady-state free precession imaging of the knee: Diagnostic performance compared with conventional MR","volume":"251","author":"Kijowski","year":"2009","journal-title":"Radiology"},{"key":"ref_66","first-page":"1599","article-title":"Ultrasound Imaging in the Diagnosis and Assessment of Knee Osteoarthritis: A Systematic Review and Meta-Analysis","volume":"43","author":"Cao","year":"2024","journal-title":"J. Ultrasound Med."},{"key":"ref_67","first-page":"516","article-title":"Ultrasonographic Evaluation of Joint Damage in Knee Osteoarthritis: Feature-Specific Comparisons with Conventional Radiography","volume":"60","author":"Okano","year":"2021","journal-title":"Rheumatology"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Ishibashi, K., Sasaki, E., Ota, S., Chiba, D., Yamamoto, Y., Tsuda, E., Yoshikuni, S., Ihara, K., and Ishibashi, Y. (2020). Detection of synovitis in early knee osteoarthritis by MRI and serum biomarkers in Japanese general population. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-69328-w"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"405","DOI":"10.2214\/AJR.21.26170","article-title":"Imaging of Synovial Inflammation in Osteoarthritis, From the AJR Special Series on Inflammation","volume":"218","author":"Thoenen","year":"2022","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_70","first-page":"155","article-title":"Complexity of Knee Osteoarthritis Pathogenesis: A Comprehensive Review of Clinical, Molecular, and Imaging Insights with a Call for Interdisciplinary Research","volume":"26","author":"Peuna","year":"2024","journal-title":"Arthritis Res. Ther."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1016\/j.joca.2014.06.020","article-title":"An Ultrasound Score for Knee Osteoarthritis: A Cross-Sectional and Longitudinal Study","volume":"22","author":"Riecke","year":"2014","journal-title":"Osteoarthr. Cartil."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1016\/j.joca.2014.05.025","article-title":"Synovial Pathology Detected on Ultrasound Correlates with the Severity of Radiographic Knee Osteoarthritis More Than with Symptoms","volume":"22","author":"Hall","year":"2014","journal-title":"Osteoarthr. Cartil."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.1016\/j.joca.2012.08.021","article-title":"Pain in Patients with Equal Radiographic Grades of Osteoarthritis in Both Knees: The Value of Gray Scale Ultrasound","volume":"20","author":"Wu","year":"2012","journal-title":"Osteoarthr. Cartil."},{"key":"ref_74","first-page":"281","article-title":"Imaging of Lower Limb Cartilage","volume":"103","author":"Omoumi","year":"2022","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_75","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_76","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1007\/s00702-017-1717-8","article-title":"Magnetic resonance imaging for the diagnosis of Parkinson\u2019s disease","volume":"124","author":"Heim","year":"2017","journal-title":"J. Neural Transm."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"718","DOI":"10.2214\/AJR.20.22902","article-title":"Rapid Musculoskeletal MRI in 2021: Clinical Application of Advanced Accelerated Techniques","volume":"216","author":"Fritz","year":"2021","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1016\/j.joca.2018.12.021","article-title":"Marked and Rapid Change of Bone Shape in Acutely ACL Injured Knees: An Exploratory Analysis of the Kanon Study","volume":"27","author":"Bowes","year":"2019","journal-title":"Osteoarthr. Cartil."},{"key":"ref_79","first-page":"2257","article-title":"MRI-Based Radiomic Features of the Tibial Bone Can Discriminate Between Knees Without and With Osteoarthritis: Data from the Osteoarthritis Initiative","volume":"40","author":"Hirvasniemi","year":"2022","journal-title":"J. Orthop. Res."},{"key":"ref_80","unstructured":"Morales, J., Rajaraman, A., Antani, S., Thoma, G., Folio, L., Burks, G., and Folio, L. (2024). Radiomics-Based Detection of Osteoarthritis-Associated Changes in Trabecular Bone Texture Using Machine Learning. Diagnostics, 14."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1016\/j.joca.2018.08.003","article-title":"Modeling and Predicting Osteoarthritis Progression: Data from the Osteoarthritis Initiative","volume":"26","author":"Halilaj","year":"2018","journal-title":"Osteoarthr. Cartil."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1038\/s41584-018-0130-5","article-title":"Machine-Learning-Based Patient-Specific Prediction Models for Knee Osteoarthritis","volume":"15","author":"Jamshidi","year":"2019","journal-title":"Nat. Rev. Rheumatol."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.jot.2023.10.003","article-title":"Radiomics signature of osteoarthritis: Current status and perspective","volume":"45","author":"Jiang","year":"2024","journal-title":"J. Orthop. Translat."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"55","DOI":"10.7326\/M14-0697","article-title":"Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement","volume":"162","author":"Collins","year":"2015","journal-title":"Ann. Intern. Med."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"103226","DOI":"10.1016\/j.jdent.2019.103226","article-title":"Convolutional Neural Networks for Dental Image Diagnostics: A Scoping Review","volume":"91","author":"Schwendicke","year":"2019","journal-title":"J. Dent."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1097\/MOO.0000000000000697","article-title":"Videomics: Bringing Deep Learning to Diagnostic Endoscopy","volume":"29","author":"Paderno","year":"2021","journal-title":"Curr. Opin. Otolaryngol. Head Neck Surg."},{"key":"ref_87","first-page":"1108","article-title":"Multimodal Machine Learning-Based Knee Osteoarthritis Progression Prediction: Results from the OAI Study","volume":"42","author":"Oei","year":"2024","journal-title":"J. Orthop. Res."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1016\/j.joca.2019.02.800","article-title":"Diagnosing osteoarthritis from T2 maps using deep learning: An analysis of the entire Osteoarthritis Initiative baseline cohort","volume":"27","author":"Pedoia","year":"2019","journal-title":"Osteoarthr. Cartil."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.berh.2014.02.002","article-title":"The Role of Imaging in Osteoarthritis","volume":"28","author":"Roemer","year":"2014","journal-title":"Best Pract. Res. Clin. Rheumatol."},{"key":"ref_90","first-page":"e288","article-title":"A Protocol for the Use of DMM\/PTX-Induced Mouse Models of Osteoarthritis and Rheumatoid Arthritis","volume":"1","author":"Krustev","year":"2021","journal-title":"Curr. Protoc."},{"key":"ref_91","unstructured":"Haubruck, P., Pinto, M., Moradi, B., Little, C.B., and Camus, T. (2024). Monocyte Recruitment and Activated Macrophage Functions in a Murine Model of Knee Osteoarthritis. Int. J. Mol. Sci., 25."},{"key":"ref_92","unstructured":"Almhdie-Imjabbar, A., Toumi, H., Harrar, K., Mokrani, K., and Lespessailles, E. (2024). Machine Learning Approaches for Osteoporosis Screening Using Vertebral Bone Quality and Bone Mineral Density from CT Images. Diagnostics, 14."},{"key":"ref_93","first-page":"103","article-title":"Multimodal Machine Learning for Modeling Knee Osteoarthritis Progression Using Radiographic, Metabolic, and Clinical Data","volume":"26","author":"Rastegar","year":"2024","journal-title":"Arthritis Res. Ther."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/s41746-020-0255-1","article-title":"Siamese Neural Networks for Continuous Disease Severity Evaluation and Change Detection in Medical Imaging","volume":"3","author":"Li","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1136\/ard.2010.139618","article-title":"Assessment of synovitis with contrast-enhanced MRI using a whole-joint semiquantitative scoring system in people with, or at high risk of, knee osteoarthritis: The MOST study","volume":"70","author":"Guermazi","year":"2011","journal-title":"Ann. Rheum. Dis."},{"key":"ref_96","first-page":"1388","article-title":"Combining Multimodal Imaging to Improve Diagnosis and Prognosis in Knee Osteoarthritis","volume":"32","author":"Chang","year":"2024","journal-title":"Osteoarthr. Cartil."},{"key":"ref_97","first-page":"138","article-title":"Multimodal Machine Learning Model Based on FLAG and 3D-CNN for Diagnosis and Prognosis Prediction in Knee Osteoarthritis","volume":"26","author":"Xu","year":"2024","journal-title":"Arthritis Res. Ther."},{"key":"ref_98","first-page":"74","article-title":"Phenotypes and Biomarkers in Osteoarthritis: Where Are We Now?","volume":"31","author":"Deveza","year":"2019","journal-title":"Curr. Opin. Rheumatol."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"2014","DOI":"10.1016\/j.joca.2017.09.001","article-title":"A Machine Learning Approach for the Identification of New Biomarkers for Knee Osteoarthritis Development in Overweight and Obese Women","volume":"25","author":"Lazzarini","year":"2017","journal-title":"Osteoarthr. Cartil."},{"key":"ref_100","unstructured":"Wu, J., Li, Y., Li, Y., Wang, Q., Han, Y., Wang, Y., Wang, Y., Zhang, Y., Ding, C., and Huang, B. (2024). Correlation Analysis of Serum Biomarkers with Imaging Features and Knee Osteoarthritis Phenotypes: Data from the Osteoarthritis Initiative. J. Clin. Med., 13."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.joca.2024.12.003","article-title":"Exploring a novel outcome measure of symptom progression in knee osteoarthritis utilizing a large randomized trial","volume":"33","author":"Conaghan","year":"2024","journal-title":"Osteoarthr. Cartil."},{"key":"ref_102","unstructured":"Huang, C., Song, P., Huang, M., Lin, Z., Liu, B., Huang, Y., Wang, Y., Ding, C., and Wang, K. (2024). Machine Learning Uncovers Novel Markers from Single-Cell RNA-Seq Data for Knee Osteoarthritis. Int. J. Mol. Sci., 25."},{"key":"ref_103","first-page":"160","article-title":"Clustering Analysis of Knee Osteoarthritis Patients Based on Synovial Fluid Multi-Omics Data","volume":"26","author":"Guan","year":"2024","journal-title":"Arthritis Res. Ther."},{"key":"ref_104","first-page":"59","article-title":"Prediction of Knee Osteoarthritis Progression Using Epidemiological and Molecular Profiles: A Machine Learning Approach","volume":"26","author":"Thudium","year":"2024","journal-title":"Arthritis Res. Ther."},{"key":"ref_105","unstructured":"Sun, H., Zhang, Y., Wang, Y., Liu, J., Ding, C., Huang, B., and Wang, K. (2024). Integration of Multi-Omics and Clinical Data for Prediction of Knee Osteoarthritis Progression. J. Clin. Med., 13."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1186\/s13075-019-1987-7","article-title":"Associations Between Biomarkers of Bone and Cartilage Turnover, Gender, Pain Categories and Radiographic Severity in Knee Osteoarthritis","volume":"21","author":"Bihlet","year":"2019","journal-title":"Arthritis Res. Ther."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1016\/j.joca.2018.12.027","article-title":"A Machine Learning Approach to Knee Osteoarthritis Phenotyping: Data from the FNIH Biomarkers Consortium","volume":"27","author":"Nelson","year":"2019","journal-title":"Osteoarthr. Cartil."},{"key":"ref_108","first-page":"792","article-title":"Is Pain in One Knee Associated with Isometric Muscle Strength in the Contralateral Limb? Data from the Osteoarthritis Initiative","volume":"93","author":"Driban","year":"2014","journal-title":"Am. J. Phys. Med. Rehabil."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.joca.2014.10.008","article-title":"Knee Extensor Muscle Weakness Is a Risk Factor for Development of Knee Osteoarthritis. A Systematic Review and Meta-Analysis","volume":"23","author":"Juhl","year":"2015","journal-title":"Osteoarthr. Cartil."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1186\/s13075-015-0670-x","article-title":"Prognostic Factors for Progression of Clinical Osteoarthritis of the Knee: A Systematic Review of Observational Studies","volume":"17","author":"Bastick","year":"2015","journal-title":"Arthritis Res. Ther."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s13075-023-03008-8","article-title":"Machine learning identification of thresholds to discriminate osteoarthritis and rheumatoid arthritis synovial inflammation","volume":"25","author":"Mehta","year":"2023","journal-title":"Arthritis Res. Ther."},{"key":"ref_112","first-page":"1","article-title":"A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations","volume":"18","author":"Schiratti","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1002\/jmri.25892","article-title":"Tool for Osteoarthritis Risk Prediction (TOARP) Over 8 Years Using Baseline Clinical Data, X-Ray, and MRI: Data from the Osteoarthritis Initiative","volume":"47","author":"Joseph","year":"2018","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_114","unstructured":"Wang, W., Yan, X., Wei, J., Wang, Y., Zhang, Y., Ding, C., Huang, B., and Wang, K. (2024). Multi-View Learning for Knee Osteoarthritis Severity Classification Using Radiomic Features. J. Clin. Med., 13."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Bhatt, S., Cohon, A., Rose, J., Majerczyk, N., Cozzi, B., Crenshaw, D., and Myers, G. (2021). Interpretable Machine Learning Models for Clinical Decision-Making in a High-Need, Value-Based Primary Care Setting. NEJM Catal., 2.","DOI":"10.1056\/CAT.21.0008"},{"key":"ref_116","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_117","unstructured":"Almhdie-Imjabbar, A., Toumi, H., and Lespessailles, E. (2024). Machine Learning-Based Classification of Osteoporotic Vertebral Fractures Using Radiomics Features from CT Images. Diagnostics, 14."},{"key":"ref_118","unstructured":"D\u2019Antoni, F., Russo, F., Ambrosio, L., Borthakur, A., Scafoglieri, A., Beckw\u00e9e, D., and Cattrysse, E. (2024). The Diagnostic Accuracy of Conventional MRI and MRI-Based GSI Quantitative Histogram Analysis in Distinguishing Low-Grade from High-Grade Chondrosarcoma. Diagnostics, 14."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1302\/0301-620X.104B4.BJJ-2021-1709.R1","article-title":"The British Orthopaedic Surgery Surveillance Study: Slipped Capital Femoral Epiphysis: The Epidemiology and Two-Year Outcomes from a Prospective UK Cohort","volume":"104-B","author":"Perry","year":"2022","journal-title":"Bone Jt. J."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.3174\/ajnr.A2036","article-title":"Lumbosacral Transitional Vertebrae: Classification, Imaging Findings, and Clinical Relevance","volume":"31","author":"Konin","year":"2010","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1503\/cmaj.101715","article-title":"Overestimation of Risk Ratios by Odds Ratios in Trials and Cohort Studies: Alternatives to Logistic Regression","volume":"184","author":"Knol","year":"2012","journal-title":"CMAJ"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Hosmer, D.W., Lemeshow, S., and May, S. (2009). Applied Survival Analysis: Regression Modeling of Time-to-Event Data, John Wiley & Sons. [2nd ed.].","DOI":"10.1002\/9780470258019"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1136\/bmj.309.6947.102","article-title":"Diagnostic Tests 2: Predictive Values","volume":"309","author":"Altman","year":"1994","journal-title":"BMJ"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1136\/bmj.324.7336.539","article-title":"The Architecture of Diagnostic Research","volume":"324","author":"Sackett","year":"2002","journal-title":"BMJ"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1136\/bmj.317.7154.307","article-title":"Number Needed to Screen: Development of a Statistic for Disease Screening","volume":"317","author":"Rembold","year":"1998","journal-title":"BMJ"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies","volume":"155","author":"Whiting","year":"2011","journal-title":"Ann. Intern. Med."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1136\/bmj.39500.677199.AE","article-title":"Grading Quality of Evidence and Strength of Recommendations for Diagnostic Tests and Strategies","volume":"336","author":"Oxman","year":"2008","journal-title":"BMJ"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1136\/bmj.329.7458.168","article-title":"Diagnostic Tests 4: Likelihood Ratios","volume":"329","author":"Deeks","year":"2004","journal-title":"BMJ"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1136\/ard.2006.062091","article-title":"EULAR evidence based recommendations for the management of hand osteoarthritis: Report of a Task Force of the EULAR Standing Committee for International Clinical Studies Including Therapeutics (ESCISIT)","volume":"66","author":"Zhang","year":"2007","journal-title":"Ann. Rheum. Dis."},{"key":"ref_130","unstructured":"Zikria, B., Rinaldi, J., Guermazi, A., Roemer, F.W., Demehri, S., and Murakami, A.M. (2024). The Role of Imaging in Osteoarthritis Diagnosis and Management. Life, 14."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"20170349","DOI":"10.1259\/bjr.20170349","article-title":"Imaging of osteoarthritis-recent research developments and future perspective","volume":"91","author":"Hayashi","year":"2018","journal-title":"Br. J. Radiol."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Kumar, R., Gowda, C., Sekhar, T.C., Vaja, S., Hage, T., Sporn, K., Waisberg, E., Ong, J., Zaman, N., and Tavakkoli, A. (2025). Advancements in Machine Learning for Precision Diagnostics and Surgical Interventions in Interconnected Musculoskeletal and Visual Systems. J. Clin. Med., 14.","DOI":"10.20944\/preprints202504.2430.v1"},{"key":"ref_133","unstructured":"Kwolek, \u0141., Kruczy\u0144ski, J., Fryzowicz, A., Jurkiewicz, A., Czernicki, K., Czwojdzi\u0144ski, A., Gregori, M., B\u0142a\u017ckiewicz, M., Szulc, A., and Klebaniuk, J. (2024). Radiographic Parameters of Knee Osteoarthritis and Patient-Reported Outcome Measures: A Cross-Sectional Study of a Polish Population. J. Clin. Med., 13."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Waszczykowski, M., Fabi\u015b-Strobin, A., Bednarski, I., Lesiak, A., Narbutt, J., and Fabi\u015b, J. (2020). Serum Biomarkers of Inflammation and Turnover of Joint Cartilage Can Help Differentiate Psoriatic Arthritis (PsA) Patients from Osteoarthritis (OA) Patients. Diagnostics, 11.","DOI":"10.3390\/diagnostics11010052"},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Puts, S., Njemini, R., Bilterys, T., Lefeber, N., Scheerlinck, T., Nijs, J., Beckw\u00e9e, D., and Bautmans, I. (2024). Linking Intra-Articular Inflammatory Biomarkers with Peripheral and Central Sensitization in Late-Stage Knee Osteoarthritis Pain: A Pilot Study. J. Clin. Med., 13.","DOI":"10.3390\/jcm13175212"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/s13075-023-03253-x","article-title":"Association of Biochemical Markers with Bone Marrow Lesion Changes on Imaging\u2014Data from the Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium","volume":"26","author":"Yu","year":"2024","journal-title":"Arthritis Res. Ther."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Panizzi, L., Vignes, M., Dittmer, K.E., Waterland, M.R., Rogers, C.W., Sano, H., McIlwraith, C.W., and Riley, C.B. (2024). Infrared Spectroscopy of Synovial Fluid Shows Accuracy as an Early Biomarker in an Equine Model of Traumatic Osteoarthritis. Animals, 14.","DOI":"10.3390\/ani14070986"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"100385","DOI":"10.1016\/j.ocarto.2023.100385","article-title":"Knee MRI Biomarkers Associated with Structural, Functional and Symptomatic Changes at Least a Year from ACL Injury\u2014A Systematic Review","volume":"5","author":"Ladlow","year":"2023","journal-title":"Osteoarthr. Cartil. Open"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"ref_140","unstructured":"Han, J., Han, Y., Song, Y., Kim, J., Lee, H., Lee, S., Kim, S.J., Nam, Y., Park, S., and Lee, J. (2024). Deep Learning-Based Outcome Prediction Using Pre- and Post-Treatment MRI in Patients with Lumbar Disc Herniation. Diagnostics, 14."},{"key":"ref_141","first-page":"e67249","article-title":"Transforming Healthcare Cybersecurity: A Prescription for Resilience","volume":"16","author":"Zoad","year":"2024","journal-title":"Cureus"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s10488-010-0327-7","article-title":"Advancing a Conceptual Model of Evidence-Based Practice Implementation in Public Service Sectors","volume":"38","author":"Aarons","year":"2011","journal-title":"Adm. Policy Ment. Health"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1186\/1748-5908-4-50","article-title":"Fostering Implementation of Health Services Research Findings into Practice: A Consolidated Framework for Advancing Implementation Science","volume":"4","author":"Damschroder","year":"2009","journal-title":"Implement. Sci."},{"key":"ref_144","first-page":"228","article-title":"A Practical, Robust Implementation and Sustainability Model (PRISM) for Integrating Research Findings into Practice","volume":"34","author":"Feldstein","year":"2008","journal-title":"Jt. Comm. J. Qual. Patient Saf."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.2105\/AJPH.89.9.1322","article-title":"Evaluating the Public Health Impact of Health Promotion Interventions: The RE-AIM Framework","volume":"89","author":"Glasgow","year":"1999","journal-title":"Am. J. Public Health"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"S70","DOI":"10.1097\/00001888-200410001-00022","article-title":"Deliberate Practice and the Acquisition and Maintenance of Expert Performance in Medicine and Related Domains","volume":"79","author":"Ericsson","year":"2004","journal-title":"Acad. Med."},{"key":"ref_147","first-page":"351","article-title":"Improving Diagnostic Reasoning to Improve Patient Safety","volume":"108","author":"Kneeland","year":"2024","journal-title":"Med. Clin. North Am."},{"key":"ref_148","first-page":"114","article-title":"The Making of an Expert","volume":"85","author":"Ericsson","year":"2007","journal-title":"Harv. Bus. Rev."},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Kumar, R., Sporn, K., Khanna, A., Paladugu, P., Gowda, C., Ngo, A., Jagadeesan, R., Zaman, N., and Tavakkoli, A. (2025). Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care. Diagnostics, 15.","DOI":"10.3390\/diagnostics15111377"},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Neumann, P.J., Sanders, G.D., Russell, L.B., Siegel, J.E., and Ganiats, T.G. (2017). Cost-Effectiveness in Health and Medicine, Oxford University Press. [2nd ed.].","DOI":"10.1093\/acprof:oso\/9780190492939.001.0001"},{"key":"ref_151","unstructured":"(2016). Medical Devices\u2014Quality Management Systems\u2014Requirements for Regulatory Purposes (Standard No. ISO 13485)."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1016\/j.eururo.2018.08.038","article-title":"Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators","volume":"74","author":"Wynants","year":"2018","journal-title":"Eur. Urol."},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Obuchowski, N.A., and Bullen, J.A. (2018). Receiver operating characteristic (ROC) curves: Review of methods with applications in diagnostic medicine. Phys. Med. Biol., 63.","DOI":"10.1088\/1361-6560\/aab4b1"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"W1","DOI":"10.7326\/M14-0698","article-title":"Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and Elaboration","volume":"162","author":"Moons","year":"2015","journal-title":"Ann. Intern. Med."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.joca.2024.10.003","article-title":"Osteoarthritis Year in Review 2024: Molecular Biomarkers of Osteoarthritis","volume":"33","author":"Welhaven","year":"2025","journal-title":"Osteoarthr. Cartil."},{"key":"ref_156","unstructured":"Huang, M., Song, P., Guan, Z., Liu, B., Huang, Y., Wang, Y., Ding, C., and Wang, K. (2024). Single-Cell Transcriptomics Reveals Molecular Characteristics of Synovial Tissue in Knee Osteoarthritis. Int. J. Mol. Sci., 25."},{"key":"ref_157","unstructured":"Song, P., Huang, C., Liu, B., Guan, Z., Wang, Y., Ding, C., Huang, Y., and Wang, K. (2024). Proteomic Analysis Identifies Potential Biomarkers for Knee Osteoarthritis Progression. J. Clin. Med., 13."},{"key":"ref_158","unstructured":"Lin, Z., Huang, M., Song, P., Guan, Z., Liu, B., Huang, Y., Wang, Y., Ding, C., and Wang, K. (2024). Metabolomic Profiling of Synovial Fluid in Knee Osteoarthritis: Identification of Potential Biomarkers. Metabolites, 14."},{"key":"ref_159","first-page":"1087","article-title":"Spatial Transcriptomics Reveals Cellular Heterogeneity in Osteoarthritic Synovial Tissue","volume":"76","author":"Zhang","year":"2024","journal-title":"Arthritis Rheumatol."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"e13517","DOI":"10.1111\/cpr.13517","article-title":"Single-cell RNA sequencing in osteoarthritis","volume":"56","author":"Gu","year":"2023","journal-title":"Cell Prolif."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1097\/BOR.0000000000000467","article-title":"Development and use of biochemical markers in osteoarthritis: Current update","volume":"30","author":"Thudium","year":"2018","journal-title":"Curr. Opin. Rheumatol."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"1756","DOI":"10.1136\/annrheumdis-2013-203726","article-title":"Value of Biomarkers in Osteoarthritis: Current Status and Perspectives","volume":"72","author":"Lotz","year":"2013","journal-title":"Ann. Rheum. Dis."},{"key":"ref_163","first-page":"1596","article-title":"Radiomics Analysis of Cartilage and Subchondral Bone to Differentiate Knees Predisposed to Posttraumatic Osteoarthritis","volume":"31","author":"Xie","year":"2023","journal-title":"Osteoarthr. Cartil."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"352","DOI":"10.21037\/qims-22-368","article-title":"Prediction model for knee osteoarthritis using magnetic resonance-based radiomic features from the infrapatellar fat pad: Data from the osteoarthritis initiative","volume":"13","author":"Yu","year":"2023","journal-title":"Quant. Imaging Med Surg."},{"key":"ref_165","unstructured":"Li, Y., Wang, Y., Zhao, X., Zhang, Y., Ding, C., Huang, B., and Wang, K. (2023). Automatic Grading Model for Knee Osteoarthritis Using Plain Radiograph Radiomics. J. Clin. Med., 12."},{"key":"ref_166","unstructured":"Teoh, Y.J., Lai, K.W., Usman, J., Goh, S.L., Mohafez, H., Hasikin, K., Su, K., Chu, H.C., Wu, X., and Dhanalakshmi, S. (2024). Exploring the Biomechanical and Biochemical Signatures of Knee Osteoarthritis: Towards Personalized Diagnostics and Targeted Interventions. Diagnostics, 14."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.joca.2024.12.008","article-title":"Raman Spectroscopic Probe Provides Optical Biomarkers of Cartilage Composition Predictive of Tissue Function","volume":"33","author":"Kazemi","year":"2025","journal-title":"Osteoarthr. Cartil."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1136\/annrheumdis-2013-204308","article-title":"The Spatial Organisation of Joint Surface Chondrocytes: Review of Its Potential Roles in Tissue Functioning, Disease and Early, Preclinical Diagnosis of Osteoarthritis","volume":"73","author":"Aicher","year":"2014","journal-title":"Ann. Rheum. Dis."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"8171","DOI":"10.1002\/anie.200700700","article-title":"Collagen-Targeted MRI Contrast Agent for Molecular Imaging of Fibrosis","volume":"46","author":"Caravan","year":"2007","journal-title":"Angew. Chem. Int. Ed. Engl."},{"key":"ref_170","first-page":"100346","article-title":"Photoacoustic Imaging for Non-Invasive Assessment of Knee Osteoarthritis: A Preliminary Study","volume":"26","author":"Lee","year":"2022","journal-title":"Photoacoustics"},{"key":"ref_171","unstructured":"Juras, V., Szomolanyi, P., Serfaty, J.M., Chang, G., Regatte, R.R., and Trattnig, S. (2019). Sodium Imaging at Ultra-High Field MRI: A New Frontier in Musculoskeletal Imaging. NMR Biomed., 32."},{"key":"ref_172","first-page":"1081","article-title":"Dynamic Contrast-Enhanced MRI in Murine Arthritis Models: Evaluation of Perfusion and Inflammation","volume":"47","author":"Menendez","year":"2018","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_173","first-page":"1669","article-title":"Quantitative Evaluation of Bone and Cartilage Changes Using Multispectral MR Imaging Following Bone Marrow Stimulation in a Porcine Model","volume":"41","author":"Subburaj","year":"2015","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"de Vries, B.M., Zwezerijnen, G.J.C., Burchell, G.L., van Velden, F.H.P., Menke-van der Houven van Oordt, C.W., and Boellaard, R. (2023). Explainable artificial intelligence (XAI) in radiology and nuclear medicine: A literature review. Front. Med., 10.","DOI":"10.3389\/fmed.2023.1180773"},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"e190043","DOI":"10.1148\/ryai.2020190043","article-title":"On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities","volume":"2","author":"Reyes","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Weng, Y., and Lund, J. (2022). Applications of Explainable Artificial Intelligence in Diagnosis and Surgery. Diagnostics, 12.","DOI":"10.3390\/diagnostics12020237"},{"key":"ref_177","first-page":"5338","article-title":"Concept Bottleneck Models","volume":"119","author":"Koh","year":"2020","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Amann, J., Blasimme, A., Vayena, E., Frey, D., Madai, V.I., and Precise4Q Consortium (2020). Explainability for Artificial Intelligence in Healthcare: A Multidisciplinary Perspective. BMC Med. Inform. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-01332-6"},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1038\/s41746-020-00323-1","article-title":"The Future of Digital Health with Federated Learning","volume":"3","author":"Rieke","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1038\/s41591-021-01506-3","article-title":"Federated Learning for Predicting Clinical Outcomes in Patients with COVID-19","volume":"27","author":"Dayan","year":"2021","journal-title":"Nat. Med."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1038\/s42256-020-0186-1","article-title":"Secure, Privacy-Preserving and Federated Machine Learning in Medical Imaging","volume":"2","author":"Kaissis","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_182","doi-asserted-by":"crossref","unstructured":"Sheller, M.J., Edwards, B., Reina, G.A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., and Colen, R.R. (2020). Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-69250-1"},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3412357","article-title":"Federated Learning in a Medical Context: A Systematic Literature Review","volume":"21","author":"Pfitzner","year":"2021","journal-title":"ACM Trans. Internet Technol."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"852","DOI":"10.21037\/qims-20-595","article-title":"Federated Learning: A Collaborative Effort to Achieve Better Medical Imaging Models for Individual Sites That Have Small Labelled Datasets","volume":"11","author":"Ng","year":"2021","journal-title":"Quant. Imaging Med. Surg."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/11\/1418\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:46:34Z","timestamp":1760031994000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/11\/1418"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,3]]},"references-count":184,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["diagnostics15111418"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15111418","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,3]]}}}