{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:30:00Z","timestamp":1770741000700,"version":"3.49.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T00:00:00Z","timestamp":1701907200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T00:00:00Z","timestamp":1701907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["RTI2018-095894-B-I00; PID2019-108435RB-I00; TED2021-131201B-I00; PDC2022-133132-I00"],"award-info":[{"award-number":["RTI2018-095894-B-I00; PID2019-108435RB-I00; TED2021-131201B-I00; PDC2022-133132-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008425","name":"Conseller\u00eda de Cultura, Educaci\u00f3n e Ordenaci\u00f3n Universitaria, Xunta de Galicia","doi-asserted-by":"publisher","award":["ED431C 2020\/24; ED431G 2019\/01"],"award-info":[{"award-number":["ED431C 2020\/24; ED431G 2019\/01"]}],"id":[{"id":"10.13039\/501100008425","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["ED431G 2019\/01"],"award-info":[{"award-number":["ED431G 2019\/01"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["ED481B-2022-025"],"award-info":[{"award-number":["ED481B-2022-025"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:sec>\n                <jats:title>Abstract<\/jats:title>\n                <jats:p>Retinal vascular tortuosity is an excessive bending and twisting of the blood vessels in the retina that is associated with numerous health conditions. We propose a novel methodology for the automated assessment of the retinal vascular tortuosity from color fundus images. Our methodology takes into consideration several anatomical factors to weigh the importance of each individual blood vessel. First, we use deep neural networks to produce a robust extraction of the different anatomical structures. Then, the weighting coefficients that are required for the integration of the different anatomical factors are adjusted using evolutionary computation. Finally, the proposed methodology also provides visual representations that explain the contribution of each individual blood vessel to the predicted tortuosity, hence allowing us to understand the decisions of the model. We validate our proposal in a dataset of color fundus images providing a consensus ground truth as well as the annotations of five clinical experts. Our proposal outperforms previous automated methods and offers a performance that is comparable to that of the clinical experts. Therefore, our methodology demonstrates to be a viable alternative for the assessment of the retinal vascular tortuosity. This could facilitate the use of this biomarker in clinical practice and medical research.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Graphical abstract<\/jats:title>\n                \n              <\/jats:sec>","DOI":"10.1007\/s11517-023-02978-w","type":"journal-article","created":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T07:02:21Z","timestamp":1701932541000},"page":"865-881","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Explainable artificial intelligence for the automated assessment of the retinal vascular tortuosity"],"prefix":"10.1007","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9080-9836","authenticated-orcid":false,"given":"\u00c1lvaro S.","family":"Hervella","sequence":"first","affiliation":[]},{"given":"Luc\u00eda","family":"Ramos","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9","family":"Rouco","sequence":"additional","affiliation":[]},{"given":"Jorge","family":"Novo","sequence":"additional","affiliation":[]},{"given":"Marcos","family":"Ortega","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,7]]},"reference":[{"key":"2978_CR1","doi-asserted-by":"publisher","unstructured":"Multilocal creaseness based on the level-set extrinsic curvature (2000). Comp Vision Image Underst 77(2):111\u2013144. ISSN 1077\u20133142. https:\/\/doi.org\/10.1006\/cviu.1999.0812","DOI":"10.1006\/cviu.1999.0812"},{"key":"2978_CR2","doi-asserted-by":"publisher","unstructured":"Retinal vascular biomarkers for early detection and monitoring of Alzheimer\u2019s disease (2013). Transl Psychiatry 3:1\u20138. ISSN 2158\u20133188. https:\/\/doi.org\/10.1038\/tp.2012.150","DOI":"10.1038\/tp.2012.150"},{"key":"2978_CR3","doi-asserted-by":"publisher","unstructured":"Vascular retinal biomarkers improves the detection of the likely cerebral amyloid status from hyperspectral retinal images (2019). Alzheimer\u2019s & dementia: translational research & clinical interventions 5:610\u2013617. ISSN 2352\u20138737. https:\/\/doi.org\/10.1016\/j.trci.2019.09.006","DOI":"10.1016\/j.trci.2019.09.006"},{"key":"2978_CR4","doi-asserted-by":"publisher","unstructured":"Abramoff MD, Garvin MK, Sonka M (2010) Retinal imaging and image analysis. IEEE Rev Biomed Eng 3:169\u2013208. ISSN 1937\u20133333.https:\/\/doi.org\/10.1109\/RBME.2010.2084567","DOI":"10.1109\/RBME.2010.2084567"},{"key":"2978_CR5","doi-asserted-by":"publisher","first-page":"89497","DOI":"10.1109\/ACCESS.2020.2990567","volume":"8","author":"J Blank","year":"2020","unstructured":"Blank J, Deb K (2020) Pymoo: multi-objective optimization in python. IEEE Access 8:89497\u201389509. https:\/\/doi.org\/10.1109\/ACCESS.2020.2990567","journal-title":"IEEE Access"},{"issue":"2","key":"2978_CR6","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182\u2013197. https:\/\/doi.org\/10.1109\/4235.996017","journal-title":"IEEE Trans Evol Comput"},{"key":"2978_CR7","doi-asserted-by":"publisher","unstructured":"Deb K, Deb D (2014) Analysing mutation schemes for real-parameter genetic algorithms. Int J Artif Intell Soft Comput 4(1):1\u201328. ISSN 1755-4950. https:\/\/doi.org\/10.1504\/IJAISC.2014.059280","DOI":"10.1504\/IJAISC.2014.059280"},{"key":"2978_CR8","doi-asserted-by":"publisher","unstructured":"Deb K, Sindhya K, Okabe T (2007) Self-adaptive simulated binary crossover for real-parameter optimization. In: Proceedings of the 9th annual conference on genetic and evolutionary computation, GECCO \u201907, New York, NY, USA, pp 1187\u20131194. Association for computing machinery. ISBN 9781595936974. https:\/\/doi.org\/10.1145\/1276958.1277190","DOI":"10.1145\/1276958.1277190"},{"key":"2978_CR9","doi-asserted-by":"publisher","unstructured":"Dougherty G, Johnson MJ, Wiers MD (2009) Measurement of retinal vascular tortuosity and its application to retinal pathologies. Med Biol Eng Comput 48(1):87. ISSN 1741-0444. https:\/\/doi.org\/10.1007\/s11517-009-0559-4","DOI":"10.1007\/s11517-009-0559-4"},{"issue":"17","key":"2978_CR10","doi-asserted-by":"publisher","first-page":"1270","DOI":"10.1093\/eurheartj\/eht023","volume":"34","author":"J Flammer","year":"2013","unstructured":"Flammer J, Konieczka K, Bruno RM, Virdis A, Flammer AJ, Taddei S (2013) The eye and the heart. Eur Heart J 34(17):1270\u20131278","journal-title":"Eur Heart J"},{"key":"2978_CR11","unstructured":"Flynn PJ, Jain AK (1989) On reliable curvature estimation. In: CVPR"},{"key":"2978_CR12","doi-asserted-by":"publisher","first-page":"2737","DOI":"10.1007\/s11517-023-02806-1","volume":"60","author":"F Garc\u00eda-Gutierrez","year":"2022","unstructured":"Garc\u00eda-Gutierrez F, D\u00edaz-\u00c1lvarez J, Matias-Guiu JA, Pytel V, Mat\u00edas-Guiu J, Cabrera-Mart\u00edn MN, Ayala JL (2022) GA-MADRID: design and validation of a machine learning tool for the diagnosis of Alzheimer\u2019s disease and frontotemporal dementia using genetic algorithms. Medical & Biological Engineering & Computing Article 60:2737\u20132756. https:\/\/doi.org\/10.1007\/s11517-023-02806-1","journal-title":"Medical & Biological Engineering & Computing Article"},{"key":"2978_CR13","doi-asserted-by":"publisher","unstructured":"Girard F, Kavalec C, Cheriet F (2019) Joint segmentation and classification of retinal arteries\/veins from fundus images. Artif Intell Med 94:96\u2013109. ISSN 0933-3657. https:\/\/doi.org\/10.1016\/j.artmed.2019.02.004","DOI":"10.1016\/j.artmed.2019.02.004"},{"key":"2978_CR14","doi-asserted-by":"publisher","unstructured":"Gonz\u00e1lez-Gonzalo C, Thee EF, Klaver CCW, Lee AY, Schlingemann RO, Tufail A, Verbraak F, S\u00e1nchez CI (2022) Trustworthy AI: closing the gap between development and integration of AI systems in ophthalmic practice. Prog Retin Eye Res 90:101034. ISSN 1350-9462. https:\/\/doi.org\/10.1016\/j.preteyeres.2021.101034","DOI":"10.1016\/j.preteyeres.2021.101034"},{"issue":"3","key":"2978_CR15","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1109\/TMI.2007.904657","volume":"27","author":"E Grisan","year":"2008","unstructured":"Grisan E, Foracchia M, Ruggeri A (2008) A novel method for the automatic grading of retinal vessel tortuosity. IEEE Trans Med Imaging 27(3):310\u2013319. https:\/\/doi.org\/10.1109\/TMI.2007.904657","journal-title":"IEEE Trans Med Imaging"},{"key":"2978_CR16","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1159\/000335123","volume":"49","author":"H-C Han","year":"2012","unstructured":"Han H-C (2012) Twisted blood vessels: symptoms, etiology and biomechanical mechanisms. J Vasc Res 49:185\u2013197. https:\/\/doi.org\/10.1159\/000335123","journal-title":"J Vasc Res"},{"issue":"2\u20133","key":"2978_CR17","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/S1386-5056(98)00163-4","volume":"53","author":"WE Hart","year":"1999","unstructured":"Hart WE, Goldbaum MH, C\u00f4t\u00e9 B, Kube P, Nelson MR (1999) Measurement and classification of retinal vascular tortuosity. I J Medical Informatics 53(2\u20133):239\u2013252","journal-title":"I J Medical Informatics"},{"key":"2978_CR18","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: 2015 IEEE International conference on computer vision (ICCV), pp 1026\u20131034. https:\/\/doi.org\/10.1109\/ICCV.2015.123","DOI":"10.1109\/ICCV.2015.123"},{"key":"2978_CR19","doi-asserted-by":"publisher","unstructured":"Hervella AS, Rouco J, Novo J, Ortega M (2018) Retinal image understanding emerges from self-supervised multimodal reconstruction. In: Medical image computing and computer-assisted intervention (MICCAI). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_37","DOI":"10.1007\/978-3-030-00928-1_37"},{"key":"2978_CR20","doi-asserted-by":"publisher","unstructured":"Hervella AS, Rouco J, Novo J, Ortega M (2020) Learning the retinal anatomy from scarce annotated data using self-supervised multimodal reconstruction. Appl Soft Comput 91:106210. ISSN 1568-4946. https:\/\/doi.org\/10.1016\/j.asoc.2020.106210","DOI":"10.1016\/j.asoc.2020.106210"},{"key":"2978_CR21","doi-asserted-by":"publisher","unstructured":"Hervella \u00c1S, Rouco J, Novo J, Ortega M (2020) Self-supervised multimodal reconstruction of retinal images over paired datasets. Expert Syst Appl, p 113674. https:\/\/doi.org\/10.1016\/j.eswa.2020.113674","DOI":"10.1016\/j.eswa.2020.113674"},{"key":"2978_CR22","doi-asserted-by":"publisher","unstructured":"Hervella AS, Rouco J, Novo J, Penedo MG, Ortega M (2020) Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images. Comput Methods Prog Biomed 186:105201. ISSN 0169-2607. https:\/\/doi.org\/10.1016\/j.cmpb.2019.105201","DOI":"10.1016\/j.cmpb.2019.105201"},{"key":"2978_CR23","doi-asserted-by":"crossref","unstructured":"Hu Q, Abr\u00e0moff MD, Garvin MK (2013) Automated separation of binary overlapping trees in low-contrast color retinal images. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N (eds) Medical image computing and computer-assisted intervention \u2013 MICCAI 2013, pp 436\u2013443. ISBN 978-3-642-40763-5","DOI":"10.1007\/978-3-642-40763-5_54"},{"key":"2978_CR24","doi-asserted-by":"publisher","unstructured":"Kamran Ikram M, Cheung CY, Lorenzi M, Klein R, Jones TLZ, Wong TY (2013) Retinal vascular caliber as a biomarker for diabetes microvascular complications. Diabetes Care 36(3):750\u2013759. ISSN 0149-5992. https:\/\/doi.org\/10.2337\/dc12-1554","DOI":"10.2337\/dc12-1554"},{"key":"2978_CR25","doi-asserted-by":"publisher","unstructured":"Kalitzeos AA, Lip GYH, Heitmar R (2013) Retinal vessel tortuosity measures and their applications. Exp Eye Res 106:40\u201346. ISSN 0014-4835. https:\/\/doi.org\/10.1016\/j.exer.2012.10.015","DOI":"10.1016\/j.exer.2012.10.015"},{"key":"2978_CR26","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: International conference on learning representations (ICLR)"},{"key":"2978_CR27","doi-asserted-by":"publisher","unstructured":"Lim WX, Chen ZY, Ahmed A (2022) The adoption of deep learning interpretability techniques on diabetic retinopathy analysis: a review. Medical & Biological Engineering & Computing Article 60:633\u2013642. https:\/\/doi.org\/10.1007\/s11517-021-02487-8","DOI":"10.1007\/s11517-021-02487-8"},{"key":"2978_CR28","doi-asserted-by":"publisher","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, S\u00e1nchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388. ISSN 1361-8415. https:\/\/doi.org\/10.1016\/j.media.2017.07.005","DOI":"10.1016\/j.media.2017.07.005"},{"key":"2978_CR29","doi-asserted-by":"publisher","unstructured":"Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR (2022) Application of explainable artificial intelligence for healthcare: a systematic review of the last decade (2011-2022). Comput Methods Prog Biomed 226:107161. ISSN 0169-2607. https:\/\/doi.org\/10.1016\/j.cmpb.2022.107161","DOI":"10.1016\/j.cmpb.2022.107161"},{"key":"2978_CR30","doi-asserted-by":"publisher","unstructured":"lui Cheung CY, Zheng Y, Hsu W, Lee ML, Lau QP, Mitchell P, Wang JJ, Klein R, Wong TY (2011) Retinal vascular tortuosity, blood pressure, and cardiovascular risk factors. Ophthalmology 118(5):812\u2013818. ISSN 0161-6420. https:\/\/doi.org\/10.1016\/j.ophtha.2010.08.045","DOI":"10.1016\/j.ophtha.2010.08.045"},{"key":"2978_CR31","doi-asserted-by":"publisher","unstructured":"Mookiah MRK, Hogg S, MacGillivray TJ, Prathiba V, Pradeepa R, Mohan V, Anjana RM, Doney AS, Palmer CNA, Trucco E (2021) A review of machine learning methods for retinal blood vessel segmentation and artery\/vein classification. Med Image Anal 68:101905. ISSN 1361-8415. https:\/\/doi.org\/10.1016\/j.media.2020.101905","DOI":"10.1016\/j.media.2020.101905"},{"key":"2978_CR32","doi-asserted-by":"publisher","unstructured":"Morano J, Hervella AS, Novo J, Rouco J (2021) Simultaneous segmentation and classification of the retinal arteries and veins from color fundus images. Artif Intell Med 118:102116. ISSN 0933-3657. https:\/\/doi.org\/10.1016\/j.artmed.2021.102116","DOI":"10.1016\/j.artmed.2021.102116"},{"key":"2978_CR33","doi-asserted-by":"publisher","unstructured":"Onkaew D, Turior R, Uyyanonvara B, Akinori N, Sinthanayothin C (2011) Automatic retinal vessel tortuosity measurement using curvature of improved chain code. In: International conference on electrical, control and computer engineering 2011 (InECCE), pp 183\u2013186. https:\/\/doi.org\/10.1109\/INECCE.2011.5953872","DOI":"10.1109\/INECCE.2011.5953872"},{"key":"2978_CR34","doi-asserted-by":"publisher","unstructured":"Ortega M, Penedo MG, Rouco J, Barreira N, Carreira MJ (2009) Retinal verification using a feature points-based biometric pattern. EURASIP J Adv Sig Proc, 2009. https:\/\/doi.org\/10.1155\/2009\/235746","DOI":"10.1155\/2009\/235746"},{"key":"2978_CR35","doi-asserted-by":"publisher","unstructured":"Patton N, Aslam TM, MacGillivray T, Deary IJ, Dhillon B, Eikelboom RH, Yogesan K, Constable IJ (2006) Retinal image analysis: concepts, applications and potential. Prog Retin Eye Res 25(1):99\u2013127. ISSN 1350-9462. https:\/\/doi.org\/10.1016\/j.preteyeres.2005.07.001","DOI":"10.1016\/j.preteyeres.2005.07.001"},{"key":"2978_CR36","unstructured":"Porwal P, Pachade S, Kamble R, Kokare M, Deshmukh G, Sahasrabuddhe V, Meriaudeau F (2018) Indian diabetic retinopathy image dataset (idrid)"},{"key":"2978_CR37","doi-asserted-by":"publisher","unstructured":"Ramos L, Novo J, Rouco J, Romeo S, \u00c1lvarez MD, Ortega M (2018a) Multi-expert analysis and validation of objective vascular tortuosity measurements. Procedia Computer Science 126:482\u2013489. ISSN 1877-0509. https:\/\/doi.org\/10.1016\/j.procs.2018.07.282. Knowledge-based and intelligent information & engineering systems: proceedings of the 22nd international conference, KES-2018, Belgrade, Serbia","DOI":"10.1016\/j.procs.2018.07.282"},{"key":"2978_CR38","doi-asserted-by":"crossref","unstructured":"Ramos L, Novo J, Rouco J, Romeo S, \u00c1lvarez MD, Ortega M (2019) Computational assessment of the retinal vascular tortuosity integrating domain-related information. Scientific Reports 9","DOI":"10.1038\/s41598-019-56507-7"},{"key":"2978_CR39","doi-asserted-by":"publisher","unstructured":"Ramos L, Novo J, Rouco J, Romeo S, \u00c1lvarez MD, Ortega M (2018) Retinal vascular tortuosity assessment: inter-intra expert analysis and correlation with computational measurements. BMC Med Res Methodol 18(1):144. ISSN 1471-2288. https:\/\/doi.org\/10.1186\/s12874-018-0598-3","DOI":"10.1186\/s12874-018-0598-3"},{"key":"2978_CR40","doi-asserted-by":"publisher","first-page":"1745","DOI":"10.1007\/s11517-023-02806-1","volume":"61","author":"Y Rong","year":"2023","unstructured":"Rong Y, Xiong Y, Li C, Chen Y, Wei P, Wei C, Fan Z (2023) Segmentation of retinal vessels in fundus images based on u-net with self-calibrated convolutions and spatial attention modules. Medical & Biological Engineering & Computing Article 61:1745\u20131755. https:\/\/doi.org\/10.1007\/s11517-023-02806-1","journal-title":"Medical & Biological Engineering & Computing Article"},{"key":"2978_CR41","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted\u00a0intervention \u2013 MICCAI 2015, pp 234\u2013241. Springer International Publishing, Cham. ISBN 978-3-319-24574-4","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"9","key":"2978_CR42","doi-asserted-by":"publisher","first-page":"2409","DOI":"10.1007\/s00125-011-2200-y","volume":"54","author":"MB Sasongko","year":"2011","unstructured":"Sasongko MB, Wong TY, Nguyen TT, Cheung CY, Shaw JE, Wang JJ (2011) Retinal vascular tortuosity in persons with diabetes and diabetic retinopathy. Diabetologia 54(9):2409\u20132416","journal-title":"Diabetologia"},{"issue":"4","key":"2978_CR43","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal J, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501\u2013509. https:\/\/doi.org\/10.1109\/TMI.2004.825627","journal-title":"IEEE Trans Med Imaging"},{"key":"2978_CR44","doi-asserted-by":"publisher","unstructured":"Trucco E, Azegrouz H, Dhillon B (2010) Modeling the tortuosity of retinal vessels: does caliber play a role? IEEE Trans Biomed Eng 57(9):2239\u20132247. ISSN 0018\u20139294. https:\/\/doi.org\/10.1109\/TBME.2010.2050771","DOI":"10.1109\/TBME.2010.2050771"},{"key":"2978_CR45","doi-asserted-by":"publisher","first-page":"57757","DOI":"10.1109\/ACCESS.2021.3070634","volume":"9","author":"S Verma","year":"2021","unstructured":"Verma S, Pant M, Snasel V (2021) A comprehensive review on NSGA-II for multi-objective combinatorial optimization problems. IEEE Access 9:57757\u201357791. https:\/\/doi.org\/10.1109\/ACCESS.2021.3070634","journal-title":"IEEE Access"},{"key":"2978_CR46","doi-asserted-by":"publisher","unstructured":"Witt N, Wong TY, Hughes AD, Chaturvedi N, Klein BE, Evans R, McNamara M, McG Thom SA, Klein R (2006) Abnormalities of retinal microvascular structure and risk of mortality from ischemic heart disease and stroke. Hypertension 47(5):975\u2013981. ISSN 0194-911X. https:\/\/doi.org\/10.1161\/01.HYP.0000216717.72048.6c","DOI":"10.1161\/01.HYP.0000216717.72048.6c"},{"key":"2978_CR47","doi-asserted-by":"publisher","unstructured":"Wong TY, Klein R, Klein BEK, Tielsch JM, Hubbard L, Nieto FJ (2001) Retinal microvascular abnormalities and their relationship with hypertension, cardiovascular disease, and mortality. Surv Ophthalmol 46(1):59\u201380. ISSN 0039-6257. https:\/\/doi.org\/10.1016\/S0039-6257(01)00234-X","DOI":"10.1016\/S0039-6257(01)00234-X"},{"key":"2978_CR48","doi-asserted-by":"publisher","unstructured":"Zhang TY, Suen CY (1984) A fast parallel algorithm for thinning digital patterns. Commun ACM 27(3):236\u2013239. ISSN 0001-0782. https:\/\/doi.org\/10.1145\/357994.358023","DOI":"10.1145\/357994.358023"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02978-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02978-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02978-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T05:14:56Z","timestamp":1708492496000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02978-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,7]]},"references-count":48,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["2978"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02978-w","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,7]]},"assertion":[{"value":"11 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}