{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:26:37Z","timestamp":1792081597387,"version":"4.4.0"},"reference-count":44,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Therapeutic Advances in Musculoskeletal"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:p>Knee osteoarthritis (KOA) remains the most prevalent form of osteoarthritis and a major cause of global disability. The Kellgren\u2013Lawrence (KL) grading system, though widely used, suffers from inter- and intra-observer variability, especially in early disease stages. Artificial intelligence (AI) offers a transformative approach to automate KL grading on plain radiographs, providing consistent, reproducible, and scalable diagnostic solutions. This narrative review synthesizes recent advances in AI-based KL grading models, focusing on methodological frameworks, performance, clinical applicability, and limitations. Narrative review of peer-reviewed studies applying AI-based methods for KL grading of KOA on radiographic images. Literature search was conducted across PubMed, Embase, Web of Science, and Google Scholar to identify studies published between 2016 and 2025. Eligible studies satisfied predefined selection criteria, applied AI-based methods to radiographic grading of KOA. The review focused on model architectures, dataset characteristics, validation strategies, performance metrics, and comparisons with expert radiographic assessment. Eighteen eligible studies were included. Convolutional neural networks (CNN) remain the core of automated KL grading, evolving from standard classification models to ensemble and ordinal regression frameworks. Model performance was evaluated against expert-assigned KL grades as reference standard, with reported accuracies ranging from 75% to 98% and area under the curve values up to 0.98. Agreement with expert annotations, Cohen\u2019s kappa (\u03ba), ranged from 0.67 to 0.86. Deep Siamese networks, Faster R-CNNs, and ensemble frameworks have enhanced localization of KOA radiographic features, thereby interpretability relative to human radiologic assessment. Ordinal regression and attention-based visualization (saliency and class activation mappings) reduced misclassification between adjacent KL grades. Persistent challenges included subjective ground-truth labeling, dataset imbalance particularly under-representation of early (KL 0\u20131) and severe (KL 4) disease, and limited external validation. Models trained primarily on Osteoarthritis Initiative and Multicenter Osteoarthritis Study datasets showed reduced generalizability on external hospital datasets. AI-driven KL grading demonstrates near-human accuracy and strong promise for clinical integration. However, addressing labeling subjectivity, dataset diversity, and explainability remains essential for trustworthy deployment. While KL grading is inherently radiograph-based, integration of clinical metadata and longitudinal radiographic data may support more robust disease characterization. Federated learning frameworks offer a pathway to improve generalizability while preserving data privacy.<\/jats:p>","DOI":"10.1177\/1759720x261442408","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T12:16:33Z","timestamp":1777464993000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial intelligence in Kellgren\u2013Lawrence grading of knee osteoarthritis: bridging radiographic tradition with algorithmic precision"],"prefix":"10.1177","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9045-3471","authenticated-orcid":false,"given":"Saumya","family":"Rawat","sequence":"first","affiliation":[{"name":"Department of Rheumatology, Sir Ganga Ram Hospital, New Delhi, Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ved Prakash","family":"Chaturvedi","sequence":"additional","affiliation":[{"name":"Department of Rheumatology, Sir Ganga Ram Hospital, New Delhi, Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4840-8924","authenticated-orcid":false,"given":"Binit","family":"Vaidya","sequence":"additional","affiliation":[{"name":"National Center for Rheumatic Diseases, Kathmandu 44600, Nepal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9756-9124","authenticated-orcid":false,"given":"Hemalatha","family":"Shanmugam","sequence":"additional","affiliation":[{"name":"Department of Research, Panscience AI Healthcare Pvt Ltd, New Delhi, Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhilasha","family":"Shah","sequence":"additional","affiliation":[{"name":"Lokhit Arthritis and Rheumatology Center, Karyabinayak, Nepal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1976-8288","authenticated-orcid":false,"given":"Lavanya","family":"Airen","sequence":"additional","affiliation":[{"name":"Department of Research, Panscience AI Healthcare Pvt Ltd, New Delhi, Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1002\/art.42089"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1177\/1759720X241273009"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ocarto.2025.100580"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2025.08.015"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2024.1323091"},{"issue":"9","key":"e_1_3_4_7_2","article-title":"Global, regional, and national burden of osteoarthritis, 1990\u20132020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021","volume":"5","author":"GBD 2021 Osteoarthritis Collaborators","year":"2023","unstructured":"GBD 2021 Osteoarthritis Collaborators. Global, regional, and national burden of osteoarthritis, 1990\u20132020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Rheumatol 2023; 5(9): e508\u2013e522.","journal-title":"Lancet Rheumatol"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.1136\/ard-2023-225324"},{"key":"e_1_3_4_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11999-016-4732-4"},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-10557-0"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2474-11-269"},{"key":"e_1_3_4_12_2","doi-asserted-by":"publisher","DOI":"10.15766\/mep_2374-8265.10503"},{"key":"e_1_3_4_13_2","doi-asserted-by":"publisher","DOI":"10.1136\/ard.16.4.494"},{"key":"e_1_3_4_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ocarto.2025.100566"},{"key":"e_1_3_4_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2023.09.011"},{"key":"e_1_3_4_16_2","first-page":"197","article-title":"Rheumatism in miners. II. X-ray study","volume":"9","author":"Kellgren JH","year":"1952","unstructured":"Kellgren JH, Lawrence JS. Rheumatism in miners. II. X-ray study. Br J Ind Med 1952; 9: 197\u2013207.","journal-title":"Br J Ind Med"},{"key":"e_1_3_4_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104334"},{"key":"e_1_3_4_18_2","doi-asserted-by":"publisher","DOI":"10.2106\/JBJS.M.00929"},{"key":"e_1_3_4_19_2","doi-asserted-by":"publisher","DOI":"10.1002\/art.1780300811"},{"key":"e_1_3_4_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2014.05.007"},{"key":"e_1_3_4_21_2","doi-asserted-by":"publisher","DOI":"10.1097\/00004424-199306000-00005"},{"key":"e_1_3_4_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2008.02.021"},{"key":"e_1_3_4_23_2","doi-asserted-by":"publisher","DOI":"10.1002\/art.10822"},{"key":"e_1_3_4_24_2","doi-asserted-by":"publisher","DOI":"10.1136\/ard.50.7.467"},{"key":"e_1_3_4_25_2","doi-asserted-by":"publisher","DOI":"10.1002\/art.20537"},{"key":"e_1_3_4_26_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12891-021-04722-7"},{"key":"e_1_3_4_27_2","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020190065"},{"key":"e_1_3_4_28_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12102362"},{"key":"e_1_3_4_29_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-50210-4"},{"key":"e_1_3_4_30_2","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2025.1413820"},{"key":"e_1_3_4_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3276810"},{"key":"e_1_3_4_32_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13081380"},{"key":"e_1_3_4_33_2","unstructured":"Kondal S Kulkarni V Kharat A et al. Automatic grading of knee osteoarthritis on the Kellgren\u2013Lawrence Scale from radiographs using convolutional neural networks. ArXiv abs\/20048572 2020."},{"key":"e_1_3_4_34_2","doi-asserted-by":"publisher","DOI":"10.1186\/s43019-024-00228-3"},{"issue":"5","key":"e_1_3_4_35_2","first-page":"1549","article-title":"Artificial intelligence assistance in radiographic detection and classification of knee osteoarthritis and its severity: a cross-sectional diagnostic study","volume":"26","author":"Pongsakonpruttikul N","year":"2022","unstructured":"Pongsakonpruttikul N, Angthong C, Kittichai V, et al. Artificial intelligence assistance in radiographic detection and classification of knee osteoarthritis and its severity: a cross-sectional diagnostic study. Eur Rev Med Pharmacol Sci 2022; 26(5): 1549\u20131558.","journal-title":"Eur Rev Med Pharmacol Sci"},{"key":"e_1_3_4_36_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14212451"},{"key":"e_1_3_4_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2022.110249"},{"key":"e_1_3_4_38_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics10110932"},{"key":"e_1_3_4_39_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12891-023-06951-4"},{"key":"e_1_3_4_40_2","doi-asserted-by":"publisher","DOI":"10.1002\/jor.25415"},{"key":"e_1_3_4_41_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-20132-7"},{"key":"e_1_3_4_42_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-024-10928-9"},{"issue":"2","key":"e_1_3_4_43_2","first-page":"4193","article-title":"Metaheuristic optimization through deep learning classification of COVID-19 in chest X-ray images","volume":"73","author":"Samee NA","year":"2022","unstructured":"Samee NA, El-Kenawy EM, Atteia G, et al. Metaheuristic optimization through deep learning classification of COVID-19 in chest X-ray images. Comput Mater Contin 2022; 73(2): 4193\u20134210.","journal-title":"Comput Mater Contin"},{"key":"e_1_3_4_44_2","first-page":"15","article-title":"A validated framework for responsible AI in healthcare autonomous systems","volume":"2025","author":"Alelyani T.","unstructured":"Alelyani T. A validated framework for responsible AI in healthcare autonomous systems. Sci Rep 2025; 15: 44432.","journal-title":"Sci Rep"},{"key":"e_1_3_4_45_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-1034-x"}],"container-title":["Therapeutic Advances in Musculoskeletal Disease"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1759720X261442408","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/1759720X261442408","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1759720X261442408","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:45:38Z","timestamp":1777707938000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/1759720X261442408"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":44,"alternative-id":["10.1177\/1759720X261442408"],"URL":"https:\/\/doi.org\/10.1177\/1759720x261442408","relation":{},"ISSN":["1759-720X","1759-7218"],"issn-type":[{"value":"1759-720X","type":"print"},{"value":"1759-7218","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"article-number":"1759720X261442408"}}