{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T19:29:01Z","timestamp":1771788541088,"version":"3.50.1"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031731181","type":"print"},{"value":"9783031731198","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73119-8_8","type":"book-chapter","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T13:02:24Z","timestamp":1728478944000},"page":"73-82","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Coral-CVDs: A Consistent Ordinal Regression Model for\u00a0Cardiovascular Diseases Grading"],"prefix":"10.1007","author":[{"given":"Zhuangzhi","family":"Gao","sequence":"first","affiliation":[]},{"given":"He","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Zhongli","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Yuankai","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Gregory Yoke Hong","family":"Lip","sequence":"additional","affiliation":[]},{"given":"Alena","family":"Shantsila","sequence":"additional","affiliation":[]},{"given":"Eduard","family":"Shantsila","sequence":"additional","affiliation":[]},{"given":"Yalin","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,10]]},"reference":[{"key":"8_CR1","unstructured":"UK Biobank - UK Biobank (2024), https:\/\/www.ukbiobank.ac.uk"},{"key":"8_CR2","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.patrec.2020.11.008","volume":"140","author":"W Cao","year":"2020","unstructured":"Cao, W., Mirjalili, V., Raschka, S.: Rank consistent ordinal regression for neural networks with application to age estimation. Pattern Recognition Letters 140, 325\u2013331 (2020)","journal-title":"Pattern Recognition Letters"},{"issue":"1","key":"8_CR3","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.ophtha.2020.06.036","volume":"128","author":"J Chang","year":"2021","unstructured":"Chang, J., Lee, J., Ha, A., Han, Y.S., Bak, E., Choi, S., Yun, J.M., Kang, U., Shin, I.H., Shin, J.Y., et\u00a0al.: Explaining the rationale of deep learning glaucoma decisions with adversarial examples. Ophthalmology 128(1), 78\u201388 (2021)","journal-title":"Ophthalmology"},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Chen, S., Zhang, C., Dong, M., Le, J., Rao, M.: Using ranking-cnn for age estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5183\u20135192 (2017)","DOI":"10.1109\/CVPR.2017.86"},{"issue":"6","key":"8_CR5","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1038\/s41551-020-00626-4","volume":"5","author":"CY Cheung","year":"2021","unstructured":"Cheung, C.Y., Xu, D., Cheng, C.Y., Sabanayagam, C., Tham, Y.C., Yu, M., Rim, T.H., Chai, C.Y., Gopinath, B., Mitchell, P., et\u00a0al.: A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nature biomedical engineering 5(6), 498\u2013508 (2021)","journal-title":"Nature biomedical engineering"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Cho, S., Song, S.J., Lee, J., Song, J., Kim, M.S., Lee, M., Lee, J.: Predicting coronary artery calcium score from retinal fundus photographs using convolutional neural networks. In: Artificial Intelligence and Soft Computing: 19th International Conference, ICAISC 2020, Zakopane, Poland, October 12-14, 2020, Proceedings, Part I 19. pp. 599\u2013612. Springer (2020)","DOI":"10.1007\/978-3-030-61401-0_56"},{"key":"8_CR7","unstructured":"Contributors, W.E.: Cardiovascular diseases, https:\/\/www.webmd.com\/heart-disease\/diseases-cardiovascular, accessed: 2023-08-17"},{"issue":"1","key":"8_CR8","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/s42256-021-00427-7","volume":"4","author":"A Diaz-Pinto","year":"2022","unstructured":"Diaz-Pinto, A., Ravikumar, N., Attar, R., Suinesiaputra, A., Zhao, Y., Levelt, E., Dall\u2019Armellina, E., Lorenzi, M., Chen, Q., Keenan, T.D., et\u00a0al.: Predicting myocardial infarction through retinal scans and minimal personal information. Nature Machine Intelligence 4(1), 55\u201361 (2022)","journal-title":"Nature Machine Intelligence"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Frank, E., Hall, M.: A simple approach to ordinal classification. In: Machine Learning: ECML 2001: 12th European Conference on Machine Learning Freiburg, Germany, September 5\u20137, 2001 Proceedings 12. pp. 145\u2013156. Springer (2001)","DOI":"10.1007\/3-540-44795-4_13"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Fu, H., Wang, B., Shen, J., Cui, S., Xu, Y., Liu, J., Shao, L.: Evaluation of retinal image quality assessment networks in different color-spaces. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part I 22. pp. 48\u201356. Springer (2019)","DOI":"10.1007\/978-3-030-32239-7_6"},{"issue":"12","key":"8_CR11","doi-asserted-by":"publisher","first-page":"1796","DOI":"10.1161\/CIRCRESAHA.118.314318","volume":"124","author":"R G\u00fcnthner","year":"2019","unstructured":"G\u00fcnthner, R., Hanssen, H., Hauser, C., Angermann, S., Lorenz, G., Kemmner, S., Matschkal, J., Braunisch, M.C., K\u00fcchle, C., Renders, L., et\u00a0al.: Impaired retinal vessel dilation predicts mortality in end-stage renal disease. Circulation research 124(12), 1796\u20131807 (2019)","journal-title":"Circulation research"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Hippisley-Cox, J., Coupland, C., Brindle, P.: Development and validation of qrisk3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. bmj 357 (2017)","DOI":"10.1136\/bmj.j2099"},{"key":"8_CR13","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Li, L., Lin, H.T.: Ordinal regression by extended binary classification. Advances in neural information processing systems 19 (2006)","DOI":"10.7551\/mitpress\/7503.003.0113"},{"issue":"1","key":"8_CR15","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.scib.2021.08.016","volume":"67","author":"Y Ma","year":"2022","unstructured":"Ma, Y., Xiong, J., Zhu, Y., Ge, Z., Hua, R., Fu, M., Li, C., Wang, B., Dong, L., Zhao, X., et\u00a0al.: Deep learning algorithm using fundus photographs for 10-year risk assessment of ischemic cardiovascular diseases in china. Science bulletin 67(1), 17\u201320 (2022)","journal-title":"Science bulletin"},{"issue":"1","key":"8_CR16","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1093\/ije\/dyaa238","volume":"50","author":"S Majithia","year":"2021","unstructured":"Majithia, S., Tham, Y.C., Chee, M.L., Nusinovici, S., Teo, C.L., Chee, M.L., Thakur, S., Soh, Z.D., Kumari, N., Lamoureux, E., et\u00a0al.: Cohort profile: the singapore epidemiology of eye diseases study (seed). International journal of epidemiology 50(1), 41\u201352 (2021)","journal-title":"International journal of epidemiology"},{"issue":"2","key":"8_CR17","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1111\/j.2517-6161.1980.tb01109.x","volume":"42","author":"P McCullagh","year":"1980","unstructured":"McCullagh, P.: Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological) 42(2), 109\u2013127 (1980)","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"issue":"6","key":"8_CR18","doi-asserted-by":"publisher","first-page":"404","DOI":"10.7326\/0003-4819-151-6-200909150-00005","volume":"151","author":"K McGeechan","year":"2009","unstructured":"McGeechan, K., Liew, G., Macaskill, P., Irwig, L., Klein, R., Klein, B.E., Wang, J.J., Mitchell, P., Vingerling, J.R., DeJong, P.T., et\u00a0al.: Meta-analysis: retinal vessel caliber and risk for coronary heart disease. Annals of internal medicine 151(6), 404\u2013413 (2009)","journal-title":"Annals of internal medicine"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Niu, Z., Zhou, M., Wang, L., Gao, X., Hua, G.: Ordinal Regression with Multiple Output CNN for Age Estimation. pp. 4920\u20134928. Las Vegas, NV, USA (2016)","DOI":"10.1109\/CVPR.2016.532"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Niu, Z., Zhou, M., Wang, L., Gao, X., Hua, G.: Ordinal regression with multiple output cnn for age estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4920\u20134928 (2016)","DOI":"10.1109\/CVPR.2016.532"},{"key":"8_CR21","doi-asserted-by":"publisher","first-page":"1690","DOI":"10.1136\/heartjnl-2022-321231","volume":"109","author":"RE Parsons","year":"2023","unstructured":"Parsons, R.E., Liu, X., Collister, J.A., Clifton, D.A., Cairns, B.J., Clifton, L.: Independent external validation of the QRISK3 cardiovascular disease risk prediction model using UK Biobank. Heart 109, 1690\u20131697 (2023)","journal-title":"Heart"},{"issue":"5","key":"8_CR22","doi-asserted-by":"publisher","first-page":"e306","DOI":"10.1016\/S2589-7500(21)00043-1","volume":"3","author":"TH Rim","year":"2021","unstructured":"Rim, T.H., Lee, C.J., Tham, Y.C., Cheung, N., Yu, M., Lee, G., Kim, Y., Ting, D.S., Chong, C.C.Y., Choi, Y.S., et\u00a0al.: Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs. The Lancet Digital Health 3(5), e306\u2013e316 (2021)","journal-title":"The Lancet Digital Health"},{"key":"8_CR23","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. International journal of computer vision 128, 336\u2013359 (2020)","journal-title":"International journal of computer vision"},{"issue":"3","key":"8_CR24","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1109\/TMI.2020.3043495","volume":"40","author":"Z Shen","year":"2020","unstructured":"Shen, Z., Fu, H., Shen, J., Shao, L.: Modeling and enhancing low-quality retinal fundus images. IEEE transactions on medical imaging 40(3), 996\u20131006 (2020)","journal-title":"IEEE transactions on medical imaging"},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI conference on artificial intelligence. vol.\u00a031 (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"8_CR26","unstructured":"Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. pp. 6105\u20136114. PMLR (2019)"},{"issue":"3","key":"8_CR27","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1038\/s41551-018-0210-5","volume":"2","author":"DSW Ting","year":"2018","unstructured":"Ting, D.S.W., Wong, T.Y.: Eyeing cardiovascular risk factors. Nature Biomedical Engineering 2(3), 140\u2013141 (2018)","journal-title":"Nature Biomedical Engineering"},{"issue":"1","key":"8_CR28","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s12916-022-02684-8","volume":"21","author":"RMWW Tseng","year":"2023","unstructured":"Tseng, R.M.W.W., Rim, T.H., Shantsila, E., Yi, J.K., Park, S., Kim, S.S., Lee, C.J., Thakur, S., Nusinovici, S., Peng, Q., et\u00a0al.: Validation of a deep-learning-based retinal biomarker (reti-cvd) in the prediction of cardiovascular disease: data from uk biobank. BMC medicine 21(1), \u00a028 (2023)","journal-title":"BMC medicine"},{"issue":"2","key":"8_CR29","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1167\/tvst.9.2.6","volume":"9","author":"SK Wagner","year":"2020","unstructured":"Wagner, S.K., Fu, D.J., Faes, L., Liu, X., Huemer, J., Khalid, H., Ferraz, D., Korot, E., Kelly, C., Balaskas, K., et\u00a0al.: Insights into systemic disease through retinal imaging-based oculomics. Translational vision science & technology 9(2), \u00a06\u20136 (2020)","journal-title":"Translational vision science & technology"},{"issue":"3","key":"8_CR30","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1093\/ehjdh\/ztad023","volume":"4","author":"JK Yi","year":"2023","unstructured":"Yi, J.K., Rim, T.H., Park, S., Kim, S.S., Kim, H.C., Lee, C.J., Kim, H., Lee, G., Lim, J.S.G., Tan, Y.Y., et\u00a0al.: Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores. European Heart Journal-Digital Health 4(3), 236\u2013244 (2023)","journal-title":"European Heart Journal-Digital Health"},{"issue":"7981","key":"8_CR31","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1038\/s41586-023-06555-x","volume":"622","author":"Y Zhou","year":"2023","unstructured":"Zhou, Y., Chia, M.A., Wagner, S.K., Ayhan, M.S., Williamson, D.J., Struyven, R.R., Liu, T., Xu, M., Lozano, M.G., Woodward-Court, P., et\u00a0al.: A foundation model for generalizable disease detection from retinal images. Nature 622(7981), 156\u2013163 (2023)","journal-title":"Nature"}],"container-title":["Lecture Notes in Computer Science","Ophthalmic Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73119-8_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T13:03:36Z","timestamp":1728479016000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73119-8_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,10]]},"ISBN":["9783031731181","9783031731198"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73119-8_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,10]]},"assertion":[{"value":"10 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"OMIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Ophthalmic Medical Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"omia2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/workshops.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}