{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:19:12Z","timestamp":1776889152240,"version":"3.51.2"},"reference-count":73,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Multiple sclerosis (MS), a chronic disease of the central nervous system, is known to cause structural and vascular changes in the retina. Although optical coherence tomography (OCT) and fundus photography can detect retinal thinning and circulatory abnormalities, these findings are not specific to MS. This study explores the potential of Infrared Scanning-Laser-Ophthalmoscopy (IR-SLO) imaging to uncover vascular morphological features that may serve as MS-specific biomarkers. Using an age-matched, subject-wise stratified k-fold cross-validation approach, a deep learning model originally designed for color fundus images was adapted to segment optic disc, optic cup, and retinal vessels in IR-SLO images, achieving Dice coefficients of 91%, 94.5%, and 97%, respectively. This process included tailored pre- and post-processing steps to optimize segmentation accuracy. Subsequently, clinically relevant features were extracted. Statistical analyses followed by SHapley Additive exPlanations (SHAP) identified vessel fractal dimension, vessel density in zones B and C (circular regions extending 0.5\u20131 and 0.5\u20132 optic disc diameters from the optic disc margin, respectively), along with vessel intensity and width, as key differentiators between MS patients and healthy controls. These findings suggest that IR-SLO can non-invasively detect retinal vascular biomarkers that may serve as additional or alternative diagnostic markers for MS diagnosis, complementing current invasive procedures.<\/jats:p>","DOI":"10.3390\/bioengineering12080847","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T09:25:52Z","timestamp":1754558752000},"page":"847","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Analyzing Retinal Vessel Morphology in MS Using Interpretable AI on Deep Learning-Segmented IR-SLO Images"],"prefix":"10.3390","volume":"12","author":[{"given":"Asieh","family":"Soltanipour","sequence":"first","affiliation":[{"name":"Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan 817467346, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2356-8603","authenticated-orcid":false,"given":"Roya","family":"Arian","sequence":"additional","affiliation":[{"name":"Department of Engineering, Durham University, South Road, Durham DH1 3LE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4713-0545","authenticated-orcid":false,"given":"Ali","family":"Aghababaei","sequence":"additional","affiliation":[{"name":"Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan 817467346, Iran"},{"name":"School of Medicine, Isfahan University of Medical Sciences, Isfahan 7346181746, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fereshteh","family":"Ashtari","sequence":"additional","affiliation":[{"name":"Isfahan Neurosciences Research Center, Isfahan University of Medical Sciences, Isfahan 7346181746, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yukun","family":"Zhou","sequence":"additional","affiliation":[{"name":"NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London EC1V 2PD, UK"},{"name":"Centre for Medical Image Computing, University College London, London WC1E 6BT, UK"},{"name":"Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pearse A.","family":"Keane","sequence":"additional","affiliation":[{"name":"NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London EC1V 2PD, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0087-9476","authenticated-orcid":false,"given":"Raheleh","family":"Kafieh","sequence":"additional","affiliation":[{"name":"Department of Engineering, Durham University, South Road, Durham DH1 3LE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.1016\/S0140-6736(18)30481-1","article-title":"Multiple sclerosis","volume":"391","author":"Thompson","year":"2018","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/S1474-4422(17)30470-2","article-title":"Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria","volume":"17","author":"Thompson","year":"2018","journal-title":"Lancet Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100938","DOI":"10.1016\/j.preteyeres.2020.100938","article-title":"Past, present and future role of retinal imaging in neurodegenerative disease","volume":"83","author":"Kashani","year":"2021","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1016\/S1474-4422(17)30278-8","article-title":"Retinal layer segmentation in multiple sclerosis: A systematic review and meta-analysis","volume":"16","author":"Petzold","year":"2017","journal-title":"Lancet Neurol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1001\/jamaophthalmol.2022.4845","article-title":"Ocular Biomarkers for Alzheimer Disease Dementia: An Umbrella Review of Systematic Reviews and Meta-analyses","volume":"141","author":"Costanzo","year":"2023","journal-title":"JAMA Ophthalmol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1111\/ene.14613","article-title":"Optical coherence tomography measurements as potential imaging biomarkers for Parkinson\u2019s disease: A systematic review and meta-analysis","volume":"28","author":"Zhou","year":"2021","journal-title":"Eur. J. Neurol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bille, J.F. (2019). Scanning Laser Ophthalmoscopy (SLO). High Resolution Imaging in Microscopy and Ophthalmology: New Frontiers in Biomedical Optics, Springer.","DOI":"10.1007\/978-3-030-16638-0"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/s40846-016-0152-x","article-title":"An Automated Approach for Localizing Retinal Blood Vessels in Confocal Scanning Laser Ophthalmoscopy Fundus Images","volume":"36","author":"Kromer","year":"2016","journal-title":"J. Med Biol. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1167\/tvst.13.7.13","article-title":"SLO-Net: Enhancing Multiple Sclerosis Diagnosis Beyond Optical Coherence Tomography Using Infrared Reflectance Scanning Laser Ophthalmoscopy Images","volume":"13","author":"Arian","year":"2024","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104846","DOI":"10.1016\/j.msard.2023.104846","article-title":"Discrimination of multiple sclerosis using OCT images from two different centers","volume":"77","author":"Khodabandeh","year":"2023","journal-title":"Mult. Scler. Relat. Disord."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1167\/tvst.11.10.10","article-title":"Synthetic OCT Data Generation to Enhance the Performance of Diagnostic Models for Neurodegenerative Diseases","volume":"11","author":"Danesh","year":"2022","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Garcia-Martin, E., Ortiz, M., Boquete, L., S\u00e1nchez-Morla, E.M., Barea, R., Cavaliere, C., Vilades, E., Orduna, E., and Rodrigo, M.J. (2021). Early diagnosis of multiple sclerosis by OCT analysis using Cohen\u2019s d method and a neural network as classifier. Comput. Biol. Med., 129.","DOI":"10.1016\/j.compbiomed.2020.104165"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Montol\u00edo, A., Mart\u00edn-Gallego, A., Cego\u00f1ino, J., Orduna, E., Vilades, E., Garcia-Martin, E., and Palomar, A.P.D. (2021). Machine learning in diagnosis and disability prediction of multiple sclerosis using optical coherence tomography. Comput. Biol. Med., 133.","DOI":"10.1016\/j.compbiomed.2021.104416"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Estiasari, R., Diwyacitta, A., Sidik, M., Rida Ariarini, N.N., Sitorus, F., Marwadhani, S.S., Maharani, K., Imran, D., Arpandy, R.A., and Pangeran, D. (2021). Evaluation of Retinal Structure and Optic Nerve Function Changes in Multiple Sclerosis: Longitudinal Study with 1-Year Follow-Up. Neurol. Res. Int., 2021.","DOI":"10.1155\/2021\/5573839"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1177\/1352458519845116","article-title":"Alterations in the retinal vasculature occur in multiple sclerosis and exhibit novel correlations with disability and visual function measures","volume":"26","author":"Murphy","year":"2020","journal-title":"Mult. Scler."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1177\/1352458517750009","article-title":"Optical coherence tomography angiography indicates associations of the retinal vascular network and disease activity in multiple sclerosis","volume":"25","author":"Feucht","year":"2019","journal-title":"Mult. Scler."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1080\/01616412.2020.1726585","article-title":"Optical coherence tomography angiography findings of multiple sclerosis with or without optic neuritis","volume":"42","author":"Ulusoy","year":"2020","journal-title":"Neurol. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1136\/bjophthalmol-2013-304547","article-title":"Optical coherence tomography angiography of optic nerve head and parafovea in multiple sclerosis","volume":"98","author":"Wang","year":"2014","journal-title":"Br. J. Ophthalmol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"62975","DOI":"10.1109\/ACCESS.2024.3395995","article-title":"Diagnosis of Multiple Sclerosis by Detecting Asymmetry Within the Retina Using a Similarity-Based Neural Network","volume":"12","author":"Kafieh","year":"2024","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105743","DOI":"10.1016\/j.msard.2024.105743","article-title":"Discrimination of multiple sclerosis using scanning laser ophthalmoscopy images with autoencoder-based feature extraction","volume":"88","author":"Aghababaei","year":"2024","journal-title":"Mult. Scler. Relat. Disord."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Amann, J., Blasimme, A., Vayena, E., Frey, D., Madai, V.I., and the Precise4Q consortium (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Med. Inform. Decis. Making, 20.","DOI":"10.1186\/s12911-020-01332-6"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Casalicchio, G., Molnar, C., and Bischl, B. (2019). Visualizing the Feature Importance for Black Box Models. arXiv.","DOI":"10.1007\/978-3-030-10925-7_40"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Remeseiro, B., and Bolon-Canedo, V. (2019). A review of feature selection methods in medical applications. Comput. Biol. Med., 112.","DOI":"10.1016\/j.compbiomed.2019.103375"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e003758","DOI":"10.1136\/bmjdrc-2023-003758","article-title":"Ocular microvascular complications in diabetic retinopathy: Insights from machine learning","volume":"12","author":"Ahmed","year":"2024","journal-title":"BMJ Open Diabetes Res. Care"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hassan, D., Gill, H.M., Happe, M., Bhatwadekar, A.D., Hajrasouliha, A.R., and Janga, S.C. (2022). Combining transfer learning with retinal lesion features for accurate detection of diabetic retinopathy. Front. Med., 9.","DOI":"10.3389\/fmed.2022.1050436"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1109\/TNB.2018.2840084","article-title":"Microaneurysm Detection Using Principal Component Analysis and Machine Learning Methods","volume":"17","author":"Cao","year":"2018","journal-title":"IEEE Trans. Nanobioscience"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2573","DOI":"10.2147\/OPTH.S312236","article-title":"Evaluation of Explainable Deep Learning Methods for Ophthalmic Diagnosis","volume":"15","author":"Singh","year":"2021","journal-title":"Clin. Ophthalmol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bhandari, M., Shahi, T.B., and Neupane, A. (2023). Evaluating Retinal Disease Diagnosis with an Interpretable Lightweight CNN Model Resistant to Adversarial Attacks. J. Imaging, 9.","DOI":"10.3390\/jimaging9100219"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2685","DOI":"10.2147\/OPTH.S367722","article-title":"Glaucoma Diagnosis Through the Integration of Optical Coherence Tomography\/Angiography and Machine Learning Diagnostic Models","volume":"16","author":"Kooner","year":"2022","journal-title":"Clin. Ophthalmol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Oh, S., Park, Y., Cho, K.J., and Kim, S.J. (2021). Explainable Machine Learning Model for Glaucoma Diagnosis and Its Interpretation. Diagnostics, 11.","DOI":"10.3390\/diagnostics11030510"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e218758","DOI":"10.1001\/jamanetworkopen.2021.8758","article-title":"Automated Explainable Multidimensional Deep Learning Platform of Retinal Images for Retinopathy of Prematurity Screening","volume":"4","author":"Wang","year":"2021","journal-title":"JAMA Netw. Open"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Aldughayfiq, B., Ashfaq, F., Jhanjhi, N.Z., and Humayun, M. (2023). Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP. Diagnostics, 13.","DOI":"10.3390\/diagnostics13111932"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2206","DOI":"10.1111\/cns.13963","article-title":"Machine learning based on Optical Coherence Tomography images as a diagnostic tool for Alzheimer\u2019s disease","volume":"28","author":"Wang","year":"2022","journal-title":"CNS Neurosci. Ther."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, R., Hui, Y., Zhang, X., Zhang, S., Lv, B., Ni, Y., Li, X., Liang, X., Yang, L., and Lv, H. (2024). Ocular biomarkers of cognitive decline based on deep-learning retinal vessel segmentation. BMC Geriatr., 24.","DOI":"10.1186\/s12877-023-04593-8"},{"key":"ref_35","unstructured":"Lundberg, S.M., and Lee, S.I. (2024, July 31). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems. Curran Associates, Inc., Available online: https:\/\/papers.nips.cc\/paper_files\/paper\/2017\/hash\/8a20a8621978632d76c43dfd28b67767-Abstract.html."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene Selection for Cancer Classification using Support Vector Machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hernandez, M., Ramon-Julvez, U., Vilades, E., Cordon, B., Mayordomo, E., and Garcia-Martin, E. (2023). Explainable artificial intelligence toward usable and trustworthy computer-aided diagnosis of multiple sclerosis from Optical Coherence Tomography. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0289495"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.1177\/13524585221112605","article-title":"Machine learning classification of multiple sclerosis in children using optical coherence tomography","volume":"28","author":"Erdman","year":"2022","journal-title":"Mult. Scler."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"102625","DOI":"10.1016\/j.msard.2020.102625","article-title":"Optical Coherence Tomography in Neuromyelitis Optica spectrum disorder and Multiple Sclerosis: A population-based study","volume":"47","author":"Ashtari","year":"2021","journal-title":"Mult. Scler. Relat. Disord."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1016\/j.dib.2018.12.073","article-title":"Retinal layer parcellation of optical coherence tomography images: Data resource for multiple sclerosis and healthy controls","volume":"22","author":"He","year":"2019","journal-title":"Data Brief"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/gigascience\/gix019","article-title":"The need to approximate the use-case in clinical machine learning","volume":"6","author":"Saeb","year":"2017","journal-title":"Gigascience"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"100355","DOI":"10.1016\/j.xops.2023.100355","article-title":"A Convolutional Neural Network Using Multimodal Retinal Imaging for Differentiation of Mild Cognitive Impairment from Normal Cognition","volume":"4","author":"Wisely","year":"2024","journal-title":"Ophthalmol. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1167\/tvst.11.7.12","article-title":"AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning Pipeline","volume":"11","author":"Zhou","year":"2022","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1364\/BOE.9.000410","article-title":"Multi-modal and multi-vendor retina image registration","volume":"9","author":"Li","year":"2018","journal-title":"Biomed. Opt. Express"},{"key":"ref_45","unstructured":"Lyu, X., Yang, Q., Xia, S., and Zhang, S. (2016, January 15\u201318). Construction of Retinal Vascular Trees via Curvature Orientation Prior. Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, China."},{"key":"ref_46","unstructured":"Falconer, K. (2013). Fractal Geometry: Mathematical Foundations and Applications, Wiley. [3rd ed.]."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/S1386-5056(98)00163-4","article-title":"Measurement and classification of retinal vascular tortuosity","volume":"53","author":"Hart","year":"1999","journal-title":"Int. J. Med Inform."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/TMI.2007.904657","article-title":"A novel method for the automatic grading of retinal vessel tortuosity","volume":"27","author":"Grisan","year":"2008","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1097\/HJH.0b013e328347266c","article-title":"Quantitative and qualitative retinal microvascular characteristics and blood pressure","volume":"29","author":"Cheung","year":"2011","journal-title":"J. Hypertens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Borah, P., Ahmed, H.A., and Bhattacharyya, D.K. (2014). A statistical feature selection technique. Netw. Model. Anal. Health Inform. Bioinforma., 3.","DOI":"10.1007\/s13721-014-0055-0"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"177","DOI":"10.6000\/1929-6029.2012.01.02.11","article-title":"Feature Selection in Statistical Classification","volume":"1","author":"Kohl","year":"2012","journal-title":"Int. J. Stat. Med. Res."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Pudjihartono, N., Fadason, T., Kempa-Liehr, A.W., and O\u2019Sullivan, J.M. (2022). A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction. Front. Bioinform., 2.","DOI":"10.3389\/fbinf.2022.927312"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"13","DOI":"10.20982\/tqmp.04.1.p013","article-title":"The Mann-Whitney U: A Test for Assessing Whether Two Independent Samples Come from the Same Distribution","volume":"4","author":"Nachar","year":"2008","journal-title":"Tutor. Quant. Methods Psychol."},{"key":"ref_54","unstructured":"Bhattacharya, A. (2022). Applied Machine Learning Explainability Techniques: Make ML Models Explainable and Trustworthy for Practical Applications Using LIME, SHAP, and More, Packt Publishing."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","article-title":"A comprehensive survey on support vector machine classification: Applications, challenges and trends","volume":"408","author":"Cervantes","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019). Optuna: A Next-generation Hyperparameter Optimization Framework. arXiv.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_57","unstructured":"Louppe, G. (2015). Understanding Random Forests: From Theory to Practice. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_59","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Lemmens, S., Devulder, A., Van Keer, K., Bierkens, J., De Boever, P., and Stalmans, I. (2020). Systematic Review on Fractal Dimension of the Retinal Vasculature in Neurodegeneration and Stroke: Assessment of a Potential Biomarker. Front. Neurosci., 14.","DOI":"10.3389\/fnins.2020.00016"},{"key":"ref_61","first-page":"91","article-title":"The application of retinal fundus camera imaging in dementia: A systematic review","volume":"6","author":"McGrory","year":"2017","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1161\/CIRCULATIONAHA.121.057709","article-title":"Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature","volume":"145","author":"Zekavat","year":"2022","journal-title":"Circulation"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1812","DOI":"10.1177\/1352458516631035","article-title":"Impaired retinal microcirculation in multiple sclerosis","volume":"22","author":"Jiang","year":"2016","journal-title":"Mult. Scler."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1080\/02713683.2019.1612444","article-title":"Retinal Tissue Perfusion in Patients with Multiple Sclerosis","volume":"44","author":"Liu","year":"2019","journal-title":"Curr. Eye Res."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Drobnjak Nes, D., Berg-Hansen, P., de Rodez Benavent, S.A., H\u00f8gest\u00f8l, E.A., Beyer, M.K., Rinker, D.A., Veiby, N., Karabeg, M., Petrovski, B.\u00c9., and Celius, E.G. (2022). Exploring Retinal Blood Vessel Diameters as Biomarkers in Multiple Sclerosis. J. Clin. Med., 11.","DOI":"10.3390\/jcm11113109"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Kallab, M., Hommer, N., Schlatter, A., Bsteh, G., Altmann, P., Popa-Cherecheanu, A., Pfister, M., Werkmeister, R.M., Schmidl, D., and Schmetterer, L. (2021). Retinal Oxygen Metabolism and Haemodynamics in Patients With Multiple Sclerosis and History of Optic Neuritis. Front. Neurosci., 15.","DOI":"10.3389\/fnins.2021.761654"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1111\/j.1755-3768.2009.01587.x","article-title":"Use of the retinal vessel analyzer in ocular blood flow research","volume":"88","author":"Garhofer","year":"2010","journal-title":"Acta Ophthalmol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1111\/aos.13682","article-title":"Retinal oximetry is affected in multiple sclerosis","volume":"96","author":"Einarsdottir","year":"2018","journal-title":"Acta Ophthalmol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1111\/aos.14463","article-title":"Changes in oxygen saturation and the retinal nerve fibre layer in patients with optic neuritis associated with multiple sclerosis in a 6-month follow-up","volume":"98","author":"Hok","year":"2020","journal-title":"Acta Ophthalmol."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Hammer, M., Vilser, W., Riemer, T., and Schweitzer, D. (2008). Retinal vessel oximetry-calibration, compensation for vessel diameter and fundus pigmentation, and reproducibility. J. Biomed. Opt., 13.","DOI":"10.1117\/1.2976032"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1177\/1352458518791683","article-title":"Multiple sclerosis disease progression: Contributions from a hypoxia\u2013inflammation cycle","volume":"25","author":"Yang","year":"2019","journal-title":"Mult. Scler."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1093\/brain\/awaa427","article-title":"Hypoxia in multiple sclerosis; is it the chicken or the egg?","volume":"144","author":"Halder","year":"2021","journal-title":"Brain"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Arian, R., and Mahmoudi, E. (2023). Automatic choroid vascularity index calculation in optical coherence tomography images with low-contrast sclerochoroidal junction using deep learning. Photonics, 10.","DOI":"10.3390\/photonics10030234"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/8\/847\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:24:44Z","timestamp":1760034284000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/8\/847"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,6]]},"references-count":73,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["bioengineering12080847"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering12080847","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,6]]}}}