{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:27:32Z","timestamp":1792790852405,"version":"4.5.0"},"reference-count":131,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T00:00:00Z","timestamp":1747958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Artificial intelligence (AI) is reshaping precision medicine by revealing diagnostic links between ocular biomarkers and systemic musculoskeletal disorders. This review synthesizes clinical evidence on the associations between optical coherence tomography (OCT)-derived parameters, such as retinal nerve fiber layer (RNFL) thinning and choroidal thickness, and conditions including osteoporosis, cervical spine instability, and inflammatory arthritis. The findings, based on an analysis of studies that integrate AI with ocular and musculoskeletal imaging, highlight consistent correlations between ocular microstructural changes and systemic degenerative pathologies. These results suggest that the eye may serve as a non-invasive window into biomechanical dysfunction. This review also discusses the emerging role of AI-assisted surgical systems informed by ocular metrics. Overall, AI-driven ocular analysis offers a promising avenue for early detection and management of musculoskeletal disease, supporting its clinical relevance and interdisciplinary potential.<\/jats:p>","DOI":"10.3390\/jcm14113669","type":"journal-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T09:58:09Z","timestamp":1747994289000},"page":"3669","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Advancements in Machine Learning for Precision Diagnostics and Surgical Interventions in Interconnected Musculoskeletal and Visual Systems"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8574-2895","authenticated-orcid":false,"given":"Rahul","family":"Kumar","sequence":"first","affiliation":[{"name":"Rush Medical College, Rush University Medical Center, Chicago, IL 60612, 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":"Miller School of Medicine, University of Miami, Miami, FL 33146, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0848-8365","authenticated-orcid":false,"given":"Tejas C.","family":"Sekhar","sequence":"additional","affiliation":[{"name":"Rush Medical College, Rush University Medical Center, Chicago, IL 60612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4982-3708","authenticated-orcid":false,"given":"Swapna","family":"Vaja","sequence":"additional","affiliation":[{"name":"Rush Medical College, Rush University Medical Center, Chicago, IL 60612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tami","family":"Hage","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5707-9009","authenticated-orcid":false,"given":"Kyle","family":"Sporn","sequence":"additional","affiliation":[{"name":"Norton College of Medicine, SUNY Upstate Medical University, Syracuse, NY 13210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ethan","family":"Waisberg","sequence":"additional","affiliation":[{"name":"Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4860-827X","authenticated-orcid":false,"given":"Joshua","family":"Ong","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, MI 48105, 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 and Engineering, University of Nevada, Reno, NV 89557, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9460-1269","authenticated-orcid":false,"given":"Alireza","family":"Tavakkoli","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3921\/joptom.2009.127","article-title":"The Relationship Between Low Vision and Musculoskeletal Complaints. A Case Control Study Between Age-related Macular Degeneration Patients and Age-matched Controls with Normal Vision","volume":"2","author":"Zetterlund","year":"2009","journal-title":"J. Optom."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1111\/nyas.14224","article-title":"Visual system disorders and musculoskeletal neck complaints: A systematic review and meta-analysis","volume":"1457","year":"2019","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e230764","DOI":"10.1148\/radiol.230764","article-title":"How AI May Transform Musculoskeletal Imaging","volume":"310","author":"Guermazi","year":"2024","journal-title":"Radiology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105493","DOI":"10.1016\/j.jbspin.2022.105493","article-title":"Application of artificial intelligence to imaging interpretations in the musculoskeletal area: Where are we? Where are we going?","volume":"90","author":"Bousson","year":"2023","journal-title":"Jt. Bone Spine"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.arthro.2023.06.018","article-title":"Artificial Intelligence Aids Detection of Rotator Cuff Pathology: A Systematic Review","volume":"40","author":"Zhan","year":"2024","journal-title":"Arthroscopy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s13167-022-00292-3","article-title":"Oculomics for sarcopenia prediction: A machine learning approach toward predictive, preventive, and personalized medicine","volume":"13","author":"Kim","year":"2022","journal-title":"EPMA J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/s41746-024-01119-3","article-title":"Optical coherence tomography choroidal enhancement using generative deep learning","volume":"7","author":"Bellemo","year":"2024","journal-title":"npj Digit. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e000237","DOI":"10.1136\/bmjophth-2018-000237","article-title":"Repeatability of choroidal thickness measurements with Spectralis OCT images","volume":"4","author":"Lau","year":"2019","journal-title":"BMJ Open Ophthalmol."},{"key":"ref_9","unstructured":"Clariushd (2025, April 10). MSK AI\u2014Unlock the Power of AI for Enhanced MSK Ultrasound Workflow. Clarius. 17 March 2023. Available online: https:\/\/clarius.com\/technology\/msk-ai\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1186\/s13018-025-05705-3","article-title":"Virtual reality-enhanced rehabilitation for improving musculoskeletal function and recovery after trauma","volume":"20","author":"Paladugu","year":"2025","journal-title":"J. Orthop. Surg. Res."},{"key":"ref_11","first-page":"89","article-title":"Artificial intelligence in musculoskeletal applications: A primer for radiologists","volume":"31","author":"Tong","year":"2025","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41746-021-00438-z","article-title":"Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis","volume":"4","author":"Aggarwal","year":"2021","journal-title":"npj Digit. Med."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kumar, R., Sporn, K., Ong, J., Waisberg, E., Paladugu, P., Vaja, S., Hage, T., Sekhar, T.C., Vadhera, A.S., and Ngo, A. (2025). Integrating Artificial Intelligence in Orthopedic Care: Advancements in Bone Care and Future Directions. BioenGineering., 12.","DOI":"10.20944\/preprints202504.0576.v1"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"506","DOI":"10.2214\/AJR.19.21117","article-title":"Artificial Intelligence in Musculoskeletal Imaging: Current Status and Future Directions","volume":"213","author":"Gyftopoulos","year":"2019","journal-title":"Am. J. Roentgenol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_16","unstructured":"Wikipedia Contributors (2025, May 14). File: Example Architecture of U-Net for Producing K 256-by-256 Image Masks for a 256-by-256 RGB Image.png. Wikipedia. Published 5 August 2019. Available online: https:\/\/en.wikipedia.org\/wiki\/File:Example_architecture_of_U-Net_for_producing_k_256-by-256_image_masks_for_a_256-by-256_RGB_image.png."},{"key":"ref_17","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_18","unstructured":"Wikimedia Commons (2025, May 14). File: Vision Transformer.svg. Published 6 May 2025. Available online: https:\/\/commons.wikimedia.org\/wiki\/File:Vision_Transformer.svg."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"20220890","DOI":"10.1259\/bjr.20220890","article-title":"Federated learning for medical imaging radiology","volume":"96","author":"Rehman","year":"2023","journal-title":"Br. J. Radiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2023\/8686471","article-title":"A Spine Segmentation Method under an Arbitrary Field of View Based on 3D Swin Transformer","volume":"2023","author":"Zhang","year":"2023","journal-title":"Int. J. Intell. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, J., Wang, J., Han, Z., Ma, J., Wang, C., and Qi, M. (2023). An interpretable transformer network for the retinal disease classification using optical coherence tomography. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-30853-z"},{"key":"ref_22","unstructured":"Sammani, F., and Deligiannis, N. (2024). Interpreting and Analysing CLIP\u2019s Zero-Shot Image Classification via Mutual Knowledge. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Shen, Z., and Jiao, R. (2024). Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions. arXiv.","DOI":"10.2139\/ssrn.4495221"},{"key":"ref_24","unstructured":"Wikimedia Commons (2025, May 14). File: Contrastive Language-Image Pretraining.png. Published 6 September 2024. Available online: https:\/\/commons.wikimedia.org\/wiki\/File:Contrastive_Language-Image_Pretraining.png."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.-Y. (2023). Segment Anything. arXiv.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"S\u00fckei, E., Rumetshofer, E., Schmidinger, N., Mayr, A., Schmidt-Erfurth, U., Klambauer, G., and Bogunovi\u0107, H. (2024). Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-78515-y"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kunze, K.N. (2025). Generative Artificial Intelligence and Musculoskeletal Health Care. HSS J., Epub ahead of print.","DOI":"10.1177\/15563316251335334"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Adnan, M., Kalra, S., Cresswell, J.C., Taylor, G.W., and Tizhoosh, H.R. (2022). Federated learning and differential privacy for medical image analysis. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-05539-7"},{"key":"ref_29","unstructured":"Wikimedia Commons (2025, May 14). File: Federated Learning (Centralized vs Decentralized).png. Published 15 July 2024. Available online: https:\/\/commons.wikimedia.org\/wiki\/File:Federated_learning_(centralized_vs_decentralized).png."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1530\/EOR-23-0174","article-title":"Artificial intelligence in diagnosing upper limb musculoskeletal disorders: A systematic review and meta-analysis of diagnostic tests","volume":"9","author":"Droppelmann","year":"2024","journal-title":"EFORT Open Rev."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Guan, H., Yap, P.T., Bozoki, A., and Liu, M. (2024). Federated Learning for Medical Image Analysis: A Survey. arXiv.","DOI":"10.1016\/j.patcog.2024.110424"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dinescu, S.C., Stoica, D., Bita, C.E., Nicoara, A.-I., Cirstei, M., Staiculesc, M.-A., and Vreju, F. (2023). Applications of artificial intelligence in musculoskeletal ultrasound: Narrative review. Front. Med., 10.","DOI":"10.3389\/fmed.2023.1286085"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rani, S., Memoria, M., Almogren, A., Bharany, S., Joshi, K., Altameem, A., Rehman, A.U., and Hamam, H. (2024). Deep learning to combat knee osteoarthritis and severity assessment by using CNN-based classification. BMC Musculoskelet. Disord., 25.","DOI":"10.1186\/s12891-024-07942-9"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mohammed, A.S., Hasanaath, A.A., Latif, G., and Bashar, A. (2023). Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images. Diagnostics, 13.","DOI":"10.3390\/diagnostics13081380"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s41747-024-00422-8","article-title":"AI applications in musculoskeletal imaging: A narrative review","volume":"8","author":"Gitto","year":"2024","journal-title":"Eur. Radiol. Exp."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gilvaz, V.J., and Reginato, A.M. (2023). Artificial intelligence in rheumatoid arthritis: Potential applications and future implications. Front. Med., 10.","DOI":"10.3389\/fmed.2023.1280312"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"67","DOI":"10.13104\/imri.2022.1102","article-title":"Artificial Intelligence and Deep Learning in Musculoskeletal Magnetic Resonance Imaging","volume":"27","author":"Baek","year":"2023","journal-title":"Investig. Magn. Reson. Imaging"},{"key":"ref_38","unstructured":"Wikimedia Commons (2025, May 14). File: Typical Cnn.png. Published 5 July 2022. Available online: https:\/\/commons.wikimedia.org\/wiki\/File:Typical_cnn.png."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1148\/rg.2021200151","article-title":"Generative Adversarial Networks: A Primer for Radiologists","volume":"41","author":"Wolterink","year":"2021","journal-title":"RadioGraphics"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Debs, P., and Fayad, L.M. (2023). The promise and limitations of artificial intelligence in musculoskeletal imaging. Front. Radiol., 3.","DOI":"10.3389\/fradi.2023.1242902"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1016\/j.semarthrit.2020.08.013","article-title":"Microvascular changes of the retina in ankylosing spondylitis, and the association with cardiovascular disease\u2014The eye for a heart study","volume":"50","author":"Baniaamam","year":"2020","journal-title":"Semin. Arthritis Rheum."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Castellino, N., Longo, A., Fallico, M., Russo, A., Bonfiglio, V., Cennamo, G., Fossataro, F., Fabbrocini, G., Balato, A., and Parisi, G. (2021). Retinal Vascular Assessment in Psoriasis: A Multicenter Study. Front. Neurosci., 15.","DOI":"10.3389\/fnins.2021.629401"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"9995546","DOI":"10.1155\/2021\/9995546","article-title":"Choroidal Thickness in Relation to Bone Mineral Density with Swept-Source Optical Coherence Tomography","volume":"2021","author":"Jiang","year":"2021","journal-title":"J. Ophthalmol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"(2021). Sarah Ohrndorf, Anne-Marie Glimm, Mads Ammitzb\u00f8ll-Danielsen, Mikkel Ostergaard, Gerd R Burmester\u2014Fluorescence optical imaging: Ready for prime time?. RMD Open, 7, e001497.","DOI":"10.1136\/rmdopen-2020-001497"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e68","DOI":"10.1111\/aos.12809","article-title":"The choroid and lamina cribrosa is affected in patients with Parkinson\u2019s disease: Enhanced depth imaging optical coherence tomography study","volume":"94","author":"Eraslan","year":"2016","journal-title":"Acta Ophthalmol."},{"key":"ref_46","first-page":"1857","article-title":"Choroidal Thickness Correlates with Clinical and Imaging Metrics of Parkinson\u2019s Disease: A Pilot Study","volume":"11","author":"Brown","year":"2021","journal-title":"J. Park. Dis."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1093\/rheumatology\/keab377","article-title":"A novel fluorescence optical imaging scoring system for hand synovitis in rheumatoid arthritis\u2014Validity and agreement with ultrasound","volume":"61","author":"Glinatsi","year":"2022","journal-title":"Rheumatology"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.survophthal.2024.01.002","article-title":"Choroidal thickness in eyes of rheumatoid arthritis patients measured using optical coherence tomography: A systematic review and meta-analysis","volume":"69","author":"Fekrazad","year":"2024","journal-title":"Surv. Ophthalmol."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Marchesi, N., Fahmideh, F., Boschi, F., Pascale, A., and Barbieri, A. (2021). Ocular Neurodegenerative Diseases: Interconnection between Retina and Cortical Areas. Cells, 10.","DOI":"10.3390\/cells10092394"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhao, B., Yan, Y., Wu, X., Geng, Z., Wu, Y., Xiao, G., Wang, L., Zhou, S., Wei, L., and Wang, K. (2023). The correlation of retinal neurodegeneration and brain degeneration in patients with Alzheimer\u2019s disease using optical coherence tomography angiography and MRI. Front. Aging Neurosci., 15.","DOI":"10.3389\/fnagi.2023.1089188"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1038\/s41531-024-00637-x","article-title":"Association of retinal neurodegeneration with the progression of cognitive decline in Parkinson\u2019s disease","volume":"10","author":"Urcola","year":"2024","journal-title":"npj Park. Dis."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1038\/s41746-024-01292-5","article-title":"Early detection of dementia through retinal imaging and trustworthy AI","volume":"7","author":"Hao","year":"2024","journal-title":"npj Digit. Med."},{"key":"ref_53","unstructured":"Vitar, R.L. (2025, April 14). The Role of Retinal Biomarkers in Neurodegenerative Disease Detection. Retinai.com. Published 27 June 2024. Available online: https:\/\/www.retinai.com\/articles\/the-role-of-retinal-biomarkers-in-neurodegenerative-disease-detection."},{"key":"ref_54","unstructured":"Wikimedia Commons (2025, May 15). File: 1413 Structure of the Eye.jpg. Published 25 March 2023. Available online: https:\/\/commons.wikimedia.org\/wiki\/File:1413_Structure_of_the_Eye.jpg."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, M., Tian, Y., Feng, R., Xu, K., Teng, M., Wang, J., Wang, Q., and Xu, P. (2023). Causal associations between common musculoskeletal disorders and dementia: A Mendelian randomization study. Front. Aging Neurosci., 15.","DOI":"10.3389\/fnagi.2023.1253791"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"e209659","DOI":"10.1212\/WNL.0000000000209659","article-title":"Association Between Body Composition Patterns, Cardiovascular Disease, and Risk of Neurodegenerative Disease in the UK Biobank","volume":"103","author":"Xu","year":"2024","journal-title":"Neurology"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Rahmati, M., Joneydi, M.S., Koyanagi, A., Yang, G., Ji, B., Lee, S.W., Yon, D.K., Smith, L., Shin, J.I., and Li, Y. (2023). Resistance training restores skeletal muscle atrophy and satellite cell content in an animal model of Alzheimer\u2019s disease. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-29406-1"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Asanad, S., Bayomi, M., Brown, D., Buzzard, J., Lai, E., Ling, C., Miglani, T., Mohammed, T., Tsai, J., and Uddin, O. (2022). Ehlers-Danlos syndromes and their manifestations in the visual system. Front. Med., 9.","DOI":"10.3389\/fmed.2022.996458"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Comberiati, A.M., Iannetti, L., Migliorini, R., Armentano, M., Graziani, M., Celli, L., Zambrano, A., Celli, M., Gharbiya, M., and Lambiase, A. (2023). Ocular Motility Abnormalities in Ehlers-Danlos Syndrome: An Observational Study. Appl. Sci., 13.","DOI":"10.3390\/app13095240"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yoshikawa, Y., Koto, T., Ishida, T., Uehara, T., Yamada, M., Kosaki, K., and Inoue, M. (2023). Rhegmatogenous Retinal Detachment in Musculocontractural Ehlers-Danlos Syndrome Caused by Biallelic Loss-of-Function Variants of Gene for Dermatan Sulfate Epimerase. J. Clin. Med., 12.","DOI":"10.3390\/jcm12051728"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Zatorski, N., Sun, Y., Elmas, A., Dallago, C., Karl, T., Stein, D., Rost, B., Huang, K.-L., Walsh, M., and Schlessinger, A. (2023). Structural Analysis of Genomic and Proteomic Signatures Reveal Dynamic Expression of Intrinsically Disordered Regions in Breast Cancer and Tissue. bioRxiv, bioRxiv:2023.02.23.529755.","DOI":"10.1101\/2023.02.23.529755"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1186\/s12974-021-02364-z","article-title":"Circulating extracellular vesicles activate the pyroptosis pathway in the brain following ventilation-induced lung injury","volume":"18","author":"Chavez","year":"2021","journal-title":"J. Neuroinflamm."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e67396","DOI":"10.7554\/eLife.67396","article-title":"Intraocular dendritic cells characterize HLA-B27-associated acute anterior uveitis","volume":"10","author":"Kasper","year":"2021","journal-title":"eLife"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1038\/s41746-022-00694-7","article-title":"Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation","volume":"5","author":"Venkatesh","year":"2022","journal-title":"npj Digit. Med."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Papachristou, K., Katsakiori, P.F., Papadimitroulas, P., Strigari, L., and Kagadis, G.C. (2024). Digital Twins\u2019 Advancements and Applications in Healthcare, Towards Precision Medicine. J. Pers. Med., 14.","DOI":"10.3390\/jpm14111101"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.wneu.2023.12.124","article-title":"Innovations in Spine Surgery: A Narrative Review of Current Integrative Technologies","volume":"184","author":"Bcharah","year":"2024","journal-title":"World Neurosurg."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Sun, T., Wang, J., Suo, M., Liu, X., Huang, H., Zhang, J., Zhang, W., and Li, Z. (2023). The Digital Twin: A Potential Solution for the Personalized Diagnosis and Treatment of Musculoskeletal System Diseases. Bioengineering, 10.","DOI":"10.3390\/bioengineering10060627"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Sun, T., He, X., Song, X., Shu, L., and Li, Z. (2022). The Digital Twin in Medicine: A Key to the Future of Healthcare?. Front. Med., 9.","DOI":"10.3389\/fmed.2022.907066"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Saarinen, A.J., Suominen, E.N., Helenius, L., Syv\u00e4nen, J., Raitio, A., and Helenius, I. (2022). Intraoperative 3D Imaging Reduces Pedicle Screw Related Complications and Reoperations in Adolescents Undergoing Posterior Spinal Fusion for Idiopathic Scoliosis: A Retrospective Study. Children, 9.","DOI":"10.3390\/children9081129"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Kanno, H., Handa, K., Murotani, M., and Ozawa, H. (2024). A Novel Intraoperative CT Navigation System for Spinal Fusion Surgery in Lumbar Degenerative Disease: Accuracy and Safety of Pedicle Screw Placement. J. Clin. Med., 13.","DOI":"10.3390\/jcm13072105"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"101206","DOI":"10.1016\/j.preteyeres.2023.101206","article-title":"Optical coherence tomography angiography in diabetic retinopathy","volume":"97","author":"Waheed","year":"2023","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"4833","DOI":"10.1007\/s00167-023-07523-8","article-title":"Image-free handheld robotic-assisted technology improved the accuracy of implant positioning compared to conventional instrumentation in patients undergoing simultaneous bilateral total knee arthroplasty, without additional benefits in improvement of clinical outcomes","volume":"31","author":"Albelooshi","year":"2023","journal-title":"Knee Surg. Sports Traumatol. Arthrosc."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2267749","DOI":"10.1080\/24699322.2023.2267749","article-title":"Improving alignment in total knee arthroplasty: A cadaveric assessment of a surgical navigation tool with computed tomography imaging","volume":"28","author":"Foley","year":"2023","journal-title":"Comput. Assist. Surg."},{"key":"ref_74","first-page":"17","article-title":"Artificial Intelligence and the State of the Art of Or-thopedic Surgery","volume":"13","author":"Khojastehnezhad","year":"2025","journal-title":"Arch. Bone Jt. Surg."},{"key":"ref_75","first-page":"e50415","article-title":"Advancements in Robotic Surgery: A Comprehensive Overview of Current Utilizations and Upcoming Frontiers","volume":"15","author":"Reddy","year":"2023","journal-title":"Cureus"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Fisher, C., Harty, J., Yee, A.J.M., Li, C.L., Komolibus, K., Grygoryev, K., Lu, H., Burke, R., Wilson, B.C., and Andersson-Engels, S. (2022). Perspective on the integration of optical sensing into orthopedic surgical devices. J. Biomed. Opt., 27.","DOI":"10.1117\/1.JBO.27.1.010601"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Shickel, B., Loftus, T.J., Ruppert, M., Upchurch, G.R., Ozrazgat-Baslanti, T., Rashidi, P., and Bihorac, A. (2023). Dynamic predictions of postoperative complications from explainable, uncertainty-aware, and multi-task deep neural networks. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-27418-5"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"024001","DOI":"10.1088\/1361-6579\/acb4db","article-title":"Building an automated, machine learning-enabled platform for predicting post-operative complications","volume":"44","author":"Ruppert","year":"2023","journal-title":"Physiol. Meas."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Ong, C.J.T., Wong, M.Y.Z., Cheong, K.X., Zhao, J., Teo, K.Y.C., and Tan, T.-E. (2023). Optical Coherence Tomography Angiography in Retinal Vascular Disorders. Diagnostics, 13.","DOI":"10.3390\/diagnostics13091620"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1038\/s41746-024-01419-8","article-title":"Multimodal machine learning to predict surgical site infection with healthcare workload impact assessment","volume":"8","author":"McLean","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1364\/BOE.551625","article-title":"Development of next generation low-cost OCT towards improved point-of-care retinal imaging","volume":"16","author":"Price","year":"2025","journal-title":"Biomed. Opt. Express"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1016\/j.jagp.2014.01.001","article-title":"Delirium detection based on monitoring of blinks and eye movements","volume":"22","author":"Rots","year":"2014","journal-title":"Am. J. Geriatr. Psychiatry"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Noah, A.M., Spendlove, J., Tu, Z., Proudlock, F., Constantinescu, C.S., Gottlob, I., Auer, D.P., Dineen, R.A., and Moppett, I.K. (2024). Retinal imaging with hand-held optical coherence tomography in older people with or without postoperative delirium after hip fracture surgery: A feasibility study. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0305964"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Mohammadi, I., Rajai Firouzabadi, S., Hosseinpour, M., Akhlaghpasand, M., Hajikarimloo, B., Zeraatian-Nejad, S., and Nia, P.S. (2024). Using artificial intelligence to predict post-operative outcomes in congenital heart surgeries: A systematic review. BMC Cardiovasc. Disord., 24, Erratum in BMC Cardiovasc. Disord. 2025, 25, 14.","DOI":"10.1186\/s12872-024-04336-6"},{"key":"ref_85","unstructured":"Wikimedia Commons (2025, May 15). File: Machine Learning Pipeline in Production.png. Published 4 May 2024. Available online: https:\/\/commons.wikimedia.org\/wiki\/File:Machine_Learning_Pipeline_in_Production.png."},{"key":"ref_86","unstructured":"Green, E., Gilchrist, D., Sen, S., Di Francesco, V., Kaufman, D., and Morris, S. (2025, April 14). Bridge to Artificial Intelligence (Bridge2AI). Genome.gov. Published 11 March 2025, Available online: https:\/\/www.genome.gov\/Funded-Programs-Projects\/Bridge-to-Artificial-Intelligence."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1","DOI":"10.20517\/ais.2022.31","article-title":"Machine learning for prediction of postoperative complications after hepato-biliary and pancreatic surgery","volume":"3","author":"Shapey","year":"2023","journal-title":"Artif. Intell. Surg."},{"key":"ref_88","first-page":"7080","article-title":"Primary total hip arthroplasty failure: Aseptic loosening remains the most common cause of revision","volume":"14","author":"Feng","year":"2022","journal-title":"Am. J. Transl. Res."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1177\/2192568220915718","article-title":"Artificial Intelligence and Robotics in Spine Surgery","volume":"11","author":"Rasouli","year":"2021","journal-title":"Glob. Spine J."},{"key":"ref_90","unstructured":"(2025, April 11). Latest MRI Technology Enables Faster, More Accurate Results. Englewood Health. Available online: https:\/\/www.englewoodhealth.org\/news-and-stories\/latest-mri-technology-enables-faster-more-accurate-results."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1007\/s00330-006-0446-4","article-title":"MR imaging of the ankle at 3 Tesla and 1.5 Tesla: Protocol optimization and application to cartilage, ligament and tendon pathology in cadaver specimens","volume":"17","author":"Barr","year":"2006","journal-title":"Eur. Radiol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1055\/s-0028-1083106","article-title":"3-Tesla imaging of the wrist and hand: Techniques and applications","volume":"12","author":"Amrami","year":"2008","journal-title":"Semin. Musculoskelet. Radiol."},{"key":"ref_93","first-page":"e58364","article-title":"A Review on the Use of Artificial Intelligence in Fracture Detection","volume":"16","author":"Bhatnagar","year":"2024","journal-title":"Cureus"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1148\/radiol.220311","article-title":"Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma","volume":"306","author":"Baffour","year":"2023","journal-title":"Radiology"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Zhao, K., Ma, S., Sun, Z., Liu, X., Zhu, Y., Xu, Y., and Wang, X. (2022). Effect of AI-assisted software on inter- and intra-observer variability for the X-ray bone age assessment of preschool children. BMC Pediatr., 22.","DOI":"10.1186\/s12887-022-03727-y"},{"key":"ref_96","unstructured":"Zeiss (2025, April 14). ZEISS Announces OCT Technology Enhancements to Better Support Growing Era of Data-Driven Patient Care. Ophthalmologyweb.com. Published 4 June 2024. Available online: https:\/\/www.ophthalmologyweb.com\/Specialty\/Retina\/1315-News\/613374-ZEISS-Announces-OCT-Technology-Enhancements-to-Better-Support-Growing-Era-of-Data-Driven-Patient-Care\/."},{"key":"ref_97","unstructured":"Hutton, D. (2025, April 10). Zeiss Announces OCT Technology Enhancements to Better Support Growing Era of Data-Driven Patient Care. Modern Retina. Published 5 June 2024. Available online: https:\/\/www.modernretina.com\/view\/zeiss-announces-oct-technology-enhancements-to-better-support-growing-era-of-data-driven-patient-care."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"629","DOI":"10.5312\/wjo.v12.i9.629","article-title":"Hip prosthetic loosening: A very personal review","volume":"12","year":"2021","journal-title":"World J. Orthop."},{"key":"ref_99","unstructured":"Lum, F., and Lee, A.Y. (2025, April 14). WG-09 Ophthalmology. DICOM. Published 23 October 2023. Available online: https:\/\/www.dicomstandard.org\/activity\/wgs\/wg-09."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1016\/j.ajo.2015.07.027","article-title":"Age, Sex, and Ethnic Variations in Inner and Outer Retinal and Choroidal Thickness on Spectral-Domain Optical Coherence Tomography","volume":"160","author":"Bafiq","year":"2015","journal-title":"Am. J. Ophthalmol."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"e70181","DOI":"10.1002\/jeo2.70181","article-title":"Exploring artificial intelligence in orthopaedics: A collaborative survey from the ISAKOS Young Professional Task Force","volume":"12","author":"Familiari","year":"2025","journal-title":"J. Exp. Orthop."},{"key":"ref_102","unstructured":"O\u2019Sullivan, C. (2025, April 14). Grad-CAM for Explaining Computer Vision Models. A Data Odyssey. Published 31 January 2025. Available online: https:\/\/adataodyssey.com\/grad-cam\/."},{"key":"ref_103","unstructured":"Papastratis, I. (2025, April 14). Explainable AI (XAI): A Survey of Recents Methods, Applications and Frameworks. AI Summer. Published 4 March 2021. Available online: https:\/\/theaisummer.com\/xai\/."},{"key":"ref_104","first-page":"2399","article-title":"Pixel Snow and Differential Privacy in Retinal fundus photos de-identification","volume":"64","author":"Nakayama","year":"2023","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1038\/s43856-024-00462-6","article-title":"Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging","volume":"4","author":"Arasteh","year":"2024","journal-title":"Commun. Med."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"251","DOI":"10.2471\/BLT.19.237487","article-title":"Artificial intelligence in health care: Accountability and safety","volume":"98","author":"Habli","year":"2020","journal-title":"Bull. World Health Organ."},{"key":"ref_107","unstructured":"Digital Health Insights (2025, May 12). The Rise of Healthcare AI Governance: Building Ethical and Sustainable Data Strategies. Digital Health Insights. Published 14 April 2025. Available online: https:\/\/dhinsights.org\/news\/the-rise-of-healthcare-ai-governance-building-ethical-and-sustainable-data-strategies."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Cross, J.L., Choma, M.A., and Onofrey, J.A. (2024). Bias in medical AI: Implications for clinical decision-making. PLoS Digit. Health, 3.","DOI":"10.1371\/journal.pdig.0000651"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Palaniappan, K., Lin, E.Y.T., and Vogel, S. (2024). Global Regulatory Frameworks for the Use of Artificial Intelligence (AI) in the Healthcare Services Sector. Healthcare, 12.","DOI":"10.3390\/healthcare12050562"},{"key":"ref_110","unstructured":"World Health Organization (2025, May 12). Ethics and Governance of Artificial Intelligence for Health. www.who.int. Published 28 June 2021. Available online: https:\/\/www.who.int\/publications\/i\/item\/9789240029200."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1057\/s41599-024-02894-w","article-title":"Shaping the future of AI in healthcare through ethics and governance","volume":"11","author":"Bouderhem","year":"2024","journal-title":"Humanit. Soc. Sci. Commun."},{"key":"ref_112","unstructured":"MedTech Europe (2025, May 12). Trustworthy Artificial Intelligence (AI) in Healthcare MedTech Europe\u2019s Response to the Pilot on the Trustworthy AI Assessment List 2.0.; 2019. Available online: https:\/\/www.medtecheurope.org\/wp-content\/uploads\/2019\/11\/MTE_Nov19_Trustworthy-Artificial-Intelligence-in-healthcare-1.pdf."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"e1312","DOI":"10.1002\/jsp2.1312","article-title":"The role of the complement system in disc degeneration and Modic changes","volume":"7","author":"Heggli","year":"2024","journal-title":"JOR Spine"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Dong, J., Li, Q., Wang, X., and Fan, Y. (2022). A Review of the Methods of Non-Invasive Assessment of Intracranial Pressure through Ocular Measurement. Bioengineering, 9.","DOI":"10.3390\/bioengineering9070304"},{"key":"ref_115","unstructured":"Kaur, S.M., Bhatia, A.S., Isaiah, M., Gowher, H., and Kais, S. (2024). Multi-Omic and Quantum Machine Learning Integration for Lung Subtypes Classification. arXiv."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1002\/hep.31733","article-title":"Discovery and Opportunities With Integrative Analytics Using Multiple-Omics Data","volume":"74","author":"Athreya","year":"2021","journal-title":"Hepatology"},{"key":"ref_117","unstructured":"Khalid, M. (2025, April 14). LinkedIn. Linkedin.com. Published 12 April 2025. Available online: https:\/\/www.linkedin.com\/pulse\/quantum-machine-learning-revolutionizing-precision-khalid-rph--gx2af\/."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Theodorakis, N., Feretzakis, G., Tzelves, L., Paxinou, E., Hitas, C., Vamvakou, G., Verykios, V.S., and Nikolaou, M. (2024). Integrating Machine Learning with Multi-Omics Technologies in Geroscience: Towards Personalized Medicine. J. Pers. Med., 14.","DOI":"10.3390\/jpm14090931"},{"key":"ref_119","unstructured":"Jasarevic, T. (2025, April 14). WHO Issues First Global Report on Artificial Intelligence (AI) in Health and Six Guiding Principles for Its Design and Use; www.who.int; Published 28 June 2021. Available online: https:\/\/www.who.int\/news\/item\/28-06-2021-who-issues-first-global-report-on-ai-in-health-and-six-guiding-principles-for-its-design-and-use."},{"key":"ref_120","unstructured":"Writer, G. (2025, April 14). WHO Guidance: Ethics and Governance of Artificial Intelligence for Health. ICTworks. Published 27 October 2021. Available online: https:\/\/www.ictworks.org\/who-guidance-artificial-intelligence-health\/."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"102114","DOI":"10.1016\/j.eclinm.2023.102114","article-title":"A paradigm shift in point-of-care imaging in low-income and middle-income countries","volume":"62","author":"Frija","year":"2023","journal-title":"EClinicalMedicine"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Shaw, J., Ali, J., Atuire, C.A., Cheah, P.Y., Espa\u00f1ol, A.G., Gichoya, J.W., Hunt, A., Jjingo, D., Littler, K., and Paolotti, D. (2024). Research ethics and artificial intelligence for global health: Perspectives from the global forum on bioethics in research. BMC Med. Ethics., 25.","DOI":"10.1186\/s12910-024-01044-w"},{"key":"ref_123","unstructured":"National Institutes of Health (2025, May 12). Bridge to Artificial Intelligence (Bridge2AI). commonfund.nih.gov. Published 4 April 2025, Available online: https:\/\/commonfund.nih.gov\/bridge2ai."},{"key":"ref_124","unstructured":"Medrio (2025, May 12). How the EU AI Act Affects Clinical Trials. Medrio. Published 24 February 2025. Available online: https:\/\/medrio.com\/blog\/eu-ai-act-clinical-trials\/."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Khanna, P., Dhillon, G., Buddhavarapu, V., Verma, R., Kashyap, R., and Grewal, H. (2024). Artificial Intelligence in Multilingual Interpretation and Radiology Assessment for Clinical Language Evaluation (AI-MIRACLE). J. Pers. Med., 14.","DOI":"10.2139\/ssrn.4892182"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"2328","DOI":"10.4103\/IJO.IJO_1478_23","article-title":"Eye of the AI storm: Exploring the impact of AI tools in ophthalmology","volume":"71","author":"Honavar","year":"2023","journal-title":"Indian J. Ophthalmol."},{"key":"ref_127","unstructured":"Stevenson, S., McDonnell, P.J., and Periman, L.M. (2025, April 14). Innovation Series: AI\u2019s Impact on Managing Ocular Surface Disease with Peter J. McDonnell, MD, and Laura M. Periman, MD. Ophthalmology Times. Published 11 October 2024. Available online: https:\/\/www.ophthalmologytimes.com\/view\/innovation-series-ai-s-impact-on-managing-ocular-surface-disease-with-peter-j-mcdonnell-md-and-laura-m-periman-md."},{"key":"ref_128","first-page":"15","article-title":"Artificial Intelligence in Musculoskeletal Medicine: A Scoping Review","volume":"4","author":"Cooray","year":"2024","journal-title":"touchREVIEWS RMD"},{"key":"ref_129","unstructured":"Asamaka Industries Ltd (2025, April 14). Investigating the Role of Robotics in Non-Invasive Medical Diagnostics and Screening. Automate. Published 2024. Available online: https:\/\/www.automate.org\/robotics\/news\/-67."},{"key":"ref_130","first-page":"e52243","article-title":"Autonomous Robotic Surgery: Has the Future Arrived?","volume":"16","author":"Rodriguez","year":"2024","journal-title":"Cureus"},{"key":"ref_131","first-page":"5","article-title":"Emerging trends in robotic breast surgery in the era of artificial intelligence","volume":"12","author":"Li","year":"2025","journal-title":"Plast. Aesthet. Res."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/11\/3669\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:39:19Z","timestamp":1760031559000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/11\/3669"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,23]]},"references-count":131,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["jcm14113669"],"URL":"https:\/\/doi.org\/10.3390\/jcm14113669","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,23]]}}}