{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T10:05:10Z","timestamp":1780394710846,"version":"3.54.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:00:00Z","timestamp":1756771200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:00:00Z","timestamp":1756771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Lacuna Fund","award":["67"],"award-info":[{"award-number":["67"]}]},{"DOI":"10.13039\/100000936","name":"Gordon and Betty Moore Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000936","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NIH (NIBIB) MIDRC","award":["75N92020C00008"],"award-info":[{"award-number":["75N92020C00008"]}]},{"name":"NIH (NIBIB) MIDRC","award":["75N92020C00021"],"award-info":[{"award-number":["75N92020C00021"]}]},{"DOI":"10.13039\/100000050","name":"NHLBI","doi-asserted-by":"crossref","award":["R01HL167811"],"award-info":[{"award-number":["R01HL167811"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100015326","name":"NIH common fund","doi-asserted-by":"crossref","award":["1R25OD039834-01"],"award-info":[{"award-number":["1R25OD039834-01"]}],"id":[{"id":"10.13039\/100015326","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-025-01636-x","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T17:13:42Z","timestamp":1756833222000},"page":"2136-2149","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative Evaluation of Deep Learning and Foundation Model Embeddings for Osteoarthritis Feature Classification in Knee Radiographs"],"prefix":"10.1007","volume":"39","author":[{"given":"Mohammadreza","family":"Chavoshi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hari","family":"Trivedi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janice","family":"Newsome","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aawez","family":"Mansuri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Theo","family":"Dapamede","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bardia","family":"Khosravi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1097-316X","authenticated-orcid":false,"given":"Judy","family":"Gichoya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,2]]},"reference":[{"key":"1636_CR1","unstructured":"Bommasani R, Hudson DA, Adeli E, Altman R, Arora S, Arx S von, et al. On the Opportunities and Risks of Foundation Models. arXiv; 2022. Available from: http:\/\/arxiv.org\/abs\/2108.07258"},{"key":"1636_CR2","doi-asserted-by":"publisher","unstructured":"Paschali M, Chen Z, Blankemeier L, Varma M, Youssef A, Bluethgen C, et al. Foundation Models in Radiology: What, How, Why, and Why Not. Radiology. https:\/\/doi.org\/10.1148\/radiol.240597, Feb 2025.","DOI":"10.1148\/radiol.240597"},{"key":"1636_CR3","unstructured":"Truong T, Mohammadi S, Lenga M. How Transferable are Self-supervised Features in Medical Image Classification Tasks? In: Proceedings of Machine Learning for Health. PMLR; p. 54\u201374, 2021. Available from: https:\/\/proceedings.mlr.press\/v158\/truong21a.html"},{"key":"1636_CR4","doi-asserted-by":"publisher","unstructured":"Moor M, Banerjee O, Abad ZSH, Krumholz HM, Leskovec J, Topol EJ, et al. Foundation models for generalist medical artificial intelligence. Nature. https:\/\/doi.org\/10.1038\/s41586-023-05881-4, Apr 2023.","DOI":"10.1038\/s41586-023-05881-4"},{"key":"1636_CR5","doi-asserted-by":"publisher","unstructured":"Pai S, Bontempi D, Prudente V, Hadzic I, Soka\u010d M, Chaunzwa TL, et al. Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging. medRxiv. https:\/\/doi.org\/10.1101\/2023.09.04.23294952, Sept 5 2023.","DOI":"10.1101\/2023.09.04.23294952"},{"key":"1636_CR6","unstructured":"Xu L, Ni Z, Sun H, Li H, Zhang S. A foundation model for generalizable disease diagnosis in chest X-ray images. arXiv; 2024. Available from: http:\/\/arxiv.org\/abs\/2410.08861"},{"key":"1636_CR7","unstructured":"Azad B, Azad R, Eskandari S, Bozorgpour A, Kazerouni A, Rekik I, et al. Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision. arXiv; 2023. Available from: http:\/\/arxiv.org\/abs\/2310.18689"},{"key":"1636_CR8","doi-asserted-by":"publisher","unstructured":"Zhang S, Metaxas D. On the challenges and perspectives of foundation models for medical image analysis. Med Image Anal. https:\/\/doi.org\/10.1016\/j.media.2023.102996, Jan 1 2024.","DOI":"10.1016\/j.media.2023.102996"},{"key":"1636_CR9","doi-asserted-by":"publisher","unstructured":"Huang SC, Pareek A, Jensen M, Lungren MP, Yeung S, Chaudhari AS. Self-supervised learning for medical image classification: a systematic review and implementation guidelines. Npj Digit Med. https:\/\/doi.org\/10.1038\/s41746-023-00811-0, Apr 26 2023.","DOI":"10.1038\/s41746-023-00811-0"},{"key":"1636_CR10","doi-asserted-by":"publisher","unstructured":"Huang SC, Jensen M, Yeung-Levy S, Lungren MP, Poon H, Chaudhari AS. Multimodal Foundation Models for Medical Imaging - A Systematic Review and Implementation Guidelines. medRxiv; p. 2024.10.23.24316003, 2024. Available from: https:\/\/www.medrxiv.org\/content\/https:\/\/doi.org\/10.1101\/2024.10.23.24316003v1","DOI":"10.1101\/2024.10.23.24316003v1"},{"key":"1636_CR11","doi-asserted-by":"publisher","unstructured":"Bian Y, Li J, Ye C, Jia X, Yang Q. Artificial intelligence in medical imaging: From task-specific models to large-scale foundation models. Chin Med J (Engl). https:\/\/doi.org\/10.1097\/CM9.0000000000003489, Mar 20 2025.","DOI":"10.1097\/CM9.0000000000003489"},{"key":"1636_CR12","unstructured":"Sun K, Xue S, Sun F, Sun H, Luo Y, Wang L, et al. Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions. arXiv; 2024. Available from: http:\/\/arxiv.org\/abs\/2412.02621"},{"key":"1636_CR13","unstructured":"Sepehri MS, Fabian Z, Soltanolkotabi M, Soltanolkotabi M. MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models. arXiv; 2025. Available from: http:\/\/arxiv.org\/abs\/2409.15477"},{"key":"1636_CR14","doi-asserted-by":"crossref","unstructured":"Xie W, Wu C, Zhang X, Zhang Y, Wang Y. Towards Generalist Foundation Model for Radiology. Research Square; 2023. Available from: https:\/\/www.researchsquare.com\/article\/rs-3324530\/v1","DOI":"10.21203\/rs.3.rs-3324530\/v1"},{"key":"1636_CR15","doi-asserted-by":"publisher","unstructured":"Zheng Q, Zhao W, Wu C, Zhang X, Dai L, Guan H, et al. Large-scale long-tailed disease diagnosis on radiology images. Nat Commun 2024 151. https:\/\/doi.org\/10.1038\/s41467-024-54424-6, Nov 2024.","DOI":"10.1038\/s41467-024-54424-6"},{"key":"1636_CR16","doi-asserted-by":"crossref","unstructured":"Huix JP, Adithya \u2020, Ganeshan R, Haslum JF, S\u00f6derberg M, Matsoukas C, et al. Are Natural Domain Foundation Models Useful for Medical Image Classification? p. 7634\u201343, 2024. Available from: https:\/\/github.com\/joanaapa\/Foundation-Medical.","DOI":"10.1109\/WACV57701.2024.00746"},{"key":"1636_CR17","doi-asserted-by":"publisher","unstructured":"Strotzer QD, Nieberle F, Kupke LS, Napodano G, Muertz AK, Meiler S, et al. Toward Foundation Models in Radiology? Quantitative Assessment of GPT-4V\u2019s Multimodal and Multianatomic Region Capabilities. Radiology. https:\/\/doi.org\/10.1148\/radiol.240955, Nov 2024.","DOI":"10.1148\/radiol.240955"},{"key":"1636_CR18","doi-asserted-by":"publisher","unstructured":"Fox MG, Chang EY, Amini B, Bernard SA, Gorbachova T, Ha AS, et al. ACR Appropriateness Criteria\u00ae Chronic Knee\u00a0Pain. J Am Coll Radiol. https:\/\/doi.org\/10.1016\/j.jacr.2018.09.016, Nov 1 2018.","DOI":"10.1016\/j.jacr.2018.09.016"},{"key":"1636_CR19","doi-asserted-by":"publisher","unstructured":"Leifer VP, Katz JN, Losina E. The burden of OA-health services and economics. Osteoarthritis Cartilage. https:\/\/doi.org\/10.1016\/J.JOCA.2021.05.007, Jan 2022.","DOI":"10.1016\/J.JOCA.2021.05.007"},{"key":"1636_CR20","doi-asserted-by":"publisher","unstructured":"K\u00f6se \u00d6, Acar B, \u00c7ay F, Yilmaz B, G\u00fcler F, Y\u00fcksel HY. Inter- and Intraobserver Reliabilities of Four Different Radiographic Grading Scales of Osteoarthritis of the Knee Joint. J Knee Surg. https:\/\/doi.org\/10.1055\/s-0037-1602249, May 1 2017.","DOI":"10.1055\/s-0037-1602249"},{"key":"1636_CR21","doi-asserted-by":"publisher","unstructured":"Swiecicki A, Li N, O\u2019Donnell J, Said N, Yang J, Mather RC, et al. Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists. Comput Biol Med. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104334, June 2021. Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/33823398\/","DOI":"10.1016\/j.compbiomed.2021.104334"},{"key":"1636_CR22","doi-asserted-by":"publisher","unstructured":"Tariq T, Suhail Z, Nawaz Z. Knee Osteoarthritis Detection and Classification Using X-Rays. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2023.3276810, 2023.","DOI":"10.1109\/ACCESS.2023.3276810"},{"key":"1636_CR23","unstructured":"Zhang S, Xu Y, Usuyama N, Xu H, Bagga J, Tinn R, et al. BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs. 2023. Available from: https:\/\/arxiv.org\/abs\/2303.00915v3"},{"key":"1636_CR24","doi-asserted-by":"publisher","unstructured":"P\u00e9rez-Garc\u00eda F, Sharma H, Bond-Taylor S, Bouzid K, Salvatelli V, Ilse M, et al. Exploring scalable medical image encoders beyond text supervision. Nat Mach Intell. https:\/\/doi.org\/10.1038\/s42256-024-00965-w, Jan 2024. Available from: http:\/\/arxiv.org\/abs\/2401.10815https:\/\/doi.org\/10.1038\/s42256-024-00965-w","DOI":"10.1038\/s42256-024-00965-w"},{"key":"1636_CR25","unstructured":"Nevitt, Felson, Lester M D, G. The osteoarthritis initiative. Protocol for the cohort study. 2006. Available from: http:\/\/nda.nih.gov.s3.amazonaws.com\/cms\/prod\/StudyDesignProtocolAndAppendices.pdf"},{"key":"1636_CR26","unstructured":"He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. arXiv; 2015. Available from: http:\/\/arxiv.org\/abs\/1512.03385"},{"key":"1636_CR27","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu CY, Feichtenhofer C, Darrell T, Xie S. A ConvNet for the 2020s. arXiv; 2022. Available from: http:\/\/arxiv.org\/abs\/2201.03545","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"1636_CR28","doi-asserted-by":"publisher","unstructured":"Sellergren AB, Chen C, Nabulsi Z, Li Y, Maschinot A, Sarna A, et al. Simplified Transfer Learning for Chest Radiography Models Using Less Data. Radiology. https:\/\/doi.org\/10.1148\/radiol.212482, Nov 2022.","DOI":"10.1148\/radiol.212482"},{"key":"1636_CR29","unstructured":"Lu Z, Li H, Parikh NA, Dillman JR, He L. RadCLIP: Enhancing Radiologic Image Analysis through Contrastive Language-Image Pre-training. 2024. Available from: https:\/\/arxiv.org\/abs\/2403.09948v2"},{"key":"1636_CR30","unstructured":"Fay L, Delbrouck JB, K\u00fcstner T, Yang B, Codella NCF, Lungren MP, et al. Beyond the Prompt: Deploying Medical Foundation Models on Diverse Chest X-ray Populations. Proc Mach Learn Res- Rev. :1\u201315, 2025."},{"key":"1636_CR31","doi-asserted-by":"publisher","unstructured":"Zhao H, Ou L, Zhang Z, Zhang L, Liu K, Kuang J. The value of deep learning-based X-ray techniques in detecting and classifying K-L grades of knee osteoarthritis: a systematic review and meta-analysis. Eur Radiol. https:\/\/doi.org\/10.1007\/S00330-024-10928-9\/FIGURES\/6, Jan 2025.","DOI":"10.1007\/S00330-024-10928-9\/FIGURES\/6"},{"key":"1636_CR32","doi-asserted-by":"publisher","unstructured":"Zhong J, Yao Y, Cahill DG, Xiao F, Li S, Lee J, et al. Unsupervised domain adaptation for automated knee osteoarthritis phenotype classification. Quant Imaging Med Surg. https:\/\/doi.org\/10.21037\/QIMS-23-704\/COIF, Nov 2023.","DOI":"10.21037\/QIMS-23-704\/COIF"},{"key":"1636_CR33","doi-asserted-by":"publisher","unstructured":"Blankemeier L, Cohen JP, Kumar A, Veen DV, Gardezi SJS, Paschali M, et al. Merlin: A Vision Language Foundation Model for 3D Computed Tomography. Res Sq. https:\/\/doi.org\/10.21203\/RS.3.RS-4546309\/V1, June 2024.","DOI":"10.21203\/RS.3.RS-4546309\/V1"},{"key":"1636_CR34","unstructured":"Lee C, Park S, Shin CI, Choi WH, Park HJ, Lee JE, et al. Read Like a Radiologist: Efficient Vision-Language Model for 3D Medical Imaging Interpretation. 2024. Available from: https:\/\/arxiv.org\/abs\/2412.13558v1"},{"key":"1636_CR35","doi-asserted-by":"publisher","unstructured":"Lu MY, Chen B, Williamson DFK, Chen RJ, Liang I, Ding T, et al. A visual-language foundation model for computational pathology. Nat Med 2024 303. https:\/\/doi.org\/10.1038\/s41591-024-02856-4, Mar 2024.","DOI":"10.1038\/s41591-024-02856-4"},{"key":"1636_CR36","unstructured":"Shi C, Rezai R, Yang J, Dou Q, Li X. A Survey on Trustworthiness in Foundation Models for Medical Image Analysis. 2024. Available from: http:\/\/arxiv.org\/abs\/2407.15851"},{"key":"1636_CR37","doi-asserted-by":"publisher","unstructured":"Zhu H, Yao Q, Xiao L, Zhou SK. Learning to Localize Cross-Anatomy Landmarks in X-Ray Images with a Universal Model. BME Front. https:\/\/doi.org\/10.34133\/2022\/9765095, Jan 2022.","DOI":"10.34133\/2022\/9765095"},{"key":"1636_CR38","doi-asserted-by":"publisher","unstructured":"VanBerlo B, Hoey J, Wong A. A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound. BMC Med Imaging. https:\/\/doi.org\/10.1186\/S12880-024-01253-0\/TABLES\/9, Dec 2024.","DOI":"10.1186\/S12880-024-01253-0\/TABLES\/9"},{"key":"1636_CR39","doi-asserted-by":"publisher","unstructured":"Mei X, Liu Z, Robson PM, Marinelli B, Huang M, Doshi A, et al. RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning. Radiol Artif Intell. https:\/\/doi.org\/10.1148\/RYAI.210315, Sept 2022.","DOI":"10.1148\/RYAI.210315"},{"key":"1636_CR40","doi-asserted-by":"crossref","unstructured":"Yan B, Pei M. Clinical-BERT: Vision-Language Pre-training for Radiograph Diagnosis and Reports Generation. 2022. Available from: www.aaai.org","DOI":"10.1609\/aaai.v36i3.20204"},{"key":"1636_CR41","unstructured":"Veldhuizen V van, Botha V, Lu C, Cesur ME, Lipman KG, Jong ED de, et al. Foundation Models in Medical Imaging -- A Review and Outlook. arXiv; 2025. Available from: http:\/\/arxiv.org\/abs\/2506.09095"},{"key":"1636_CR42","doi-asserted-by":"publisher","unstructured":"Kitamura FC, Topol EJ. The Initial Steps of Multimodal AI in Radiology. Radiology. https:\/\/doi.org\/10.1148\/RADIOL.232372\/ASSET\/IMAGES\/LARGE\/RADIOL.232372.FIG2.JPEG, Oct 2023. Available from: https:\/\/pubs.rsna.org\/doi\/https:\/\/doi.org\/10.1148\/radiol.232372","DOI":"10.1148\/RADIOL.232372\/ASSET\/IMAGES\/LARGE\/RADIOL.232372.FIG2.JPEG"},{"key":"1636_CR43","doi-asserted-by":"publisher","unstructured":"Nakagawa S, Ono N, Hakamata Y, Ishii T, Saito A, Yanagimoto S, et al. Quantitative evaluation model of variable diagnosis for chest X-ray images using deep learning. PLOS Digit Health. https:\/\/doi.org\/10.1371\/JOURNAL.PDIG.0000460, Mar 2024.","DOI":"10.1371\/JOURNAL.PDIG.0000460"},{"key":"1636_CR44","unstructured":"Wang A, Islam M, Xu M, Zhang Y, Ren H. SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective. 2023. Available from: https:\/\/arxiv.org\/abs\/2304.14674v1"},{"key":"1636_CR45","doi-asserted-by":"publisher","unstructured":"Zhang Z, Wu H, Ji Z, Li C, Zhang E, Sun W, et al. Q-Boost: On Visual Quality Assessment Ability of Low-Level Multi-Modality Foundation Models. 2024 IEEE Int Conf Multimed Expo Workshop ICMEW 2024. https:\/\/doi.org\/10.1109\/ICMEW63481.2024.10645451, 2024.","DOI":"10.1109\/ICMEW63481.2024.10645451"},{"key":"1636_CR46","unstructured":"Wu C, Zhang X, Zhang Y, Wang Y, Xie W. Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data. arXiv; 2023. Available from: http:\/\/arxiv.org\/abs\/2308.02463"},{"key":"1636_CR47","doi-asserted-by":"publisher","unstructured":"Ferreira D, Arnaout R. Are foundation models efficient for medical image segmentation? J Am Soc Echocardiogr. https:\/\/doi.org\/10.1016\/j.echo.2025.02.001, Nov 2023. Available from: http:\/\/arxiv.org\/abs\/2311.04847https:\/\/doi.org\/10.1016\/j.echo.2025.02.001","DOI":"10.1016\/j.echo.2025.02.001"},{"key":"1636_CR48","doi-asserted-by":"publisher","unstructured":"Wang G, Zhang S, Huang X, Vercauteren T, Metaxas D. Editorial for special issue on explainable and generalizable deep learning methods for medical image computing. Med Image Anal. https:\/\/doi.org\/10.1016\/J.MEDIA.2022.102727, Feb 2023.","DOI":"10.1016\/J.MEDIA.2022.102727"},{"key":"1636_CR49","doi-asserted-by":"publisher","unstructured":"Glocker B, Jones C, Roschewitz M, Winzeck S. Risk of Bias in Chest Radiography Deep Learning Foundation Models. Radiol Artif Intell. https:\/\/doi.org\/10.1148\/RYAI.230060\/ASSET\/IMAGES\/LARGE\/RYAI.230060.VA.JPEG, Nov 2023. Available from: https:\/\/pubs.rsna.org\/doi\/https:\/\/doi.org\/10.1148\/ryai.230060","DOI":"10.1148\/RYAI.230060\/ASSET\/IMAGES\/LARGE\/RYAI.230060.VA.JPEG"},{"key":"1636_CR50","unstructured":"Khan MO, Afzal MM, Mirza S, Fang Y. How Fair are Medical Imaging Foundation Models? Vol. 225, Proceedings of Machine Learning Research. PMLR; p. 217\u201331, 2023. Available from: https:\/\/proceedings.mlr.press\/v225\/khan23a.html"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01636-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-025-01636-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01636-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:06:47Z","timestamp":1780391207000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-025-01636-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,2]]},"references-count":50,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["1636"],"URL":"https:\/\/doi.org\/10.1007\/s10278-025-01636-x","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,2]]},"assertion":[{"value":"25 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 July 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This project used retrospective and anonymized patient images of Osteoarthritis Initiative publicly available dataset.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}