{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:37:19Z","timestamp":1773247039910,"version":"3.50.1"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030322380","type":"print"},{"value":"9783030322397","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-32239-7_16","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:04:53Z","timestamp":1570662293000},"page":"138-146","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Biological Age Estimated from Retinal Imaging: A Novel Biomarker of Aging"],"prefix":"10.1007","author":[{"given":"Chi","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaotong","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason","family":"Ha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingguang","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","first-page":"338","DOI":"10.3390\/nu8060338","volume":"8","author":"KH Wagner","year":"2016","unstructured":"Wagner, K.H., Cameron-Smith, D., Wessner, B., Franzke, B.: Biomarkers of aging: from function to molecular biology. Nutrients 8, 338 (2016)","journal-title":"Nutrients"},{"key":"16_CR2","doi-asserted-by":"publisher","first-page":"759","DOI":"10.2147\/CIA.S134921","volume":"12","author":"L Jia","year":"2017","unstructured":"Jia, L., Zhang, W., Chen, X.: Common methods of biological age estimation. Clin. Interv. Aging 12, 759\u2013772 (2017)","journal-title":"Clin. Interv. Aging"},{"key":"16_CR3","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1038\/mp.2017.62","volume":"23","author":"JH Cole","year":"2018","unstructured":"Cole, J.H., et al.: Brain age predicts mortality. Mol. Psychiatry 23, 1385\u20131392 (2018)","journal-title":"Mol. Psychiatry"},{"key":"16_CR4","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.neuroimage.2017.07.059","volume":"163","author":"JH Cole","year":"2017","unstructured":"Cole, J.H., et al.: Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage 163, 115\u2013124 (2017)","journal-title":"NeuroImage"},{"key":"16_CR5","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/nrneurol.2012.227","volume":"9","author":"A London","year":"2012","unstructured":"London, A., Benhar, I., Schwartz, M.: The retina as a window to the brain\u2014from eye research to CNS disorders. Nat. Rev. Neurol. 9, 44 (2012)","journal-title":"Nat. Rev. Neurol."},{"key":"16_CR6","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.preteyeres.2017.01.001","volume":"57","author":"CY Cheung","year":"2017","unstructured":"Cheung, C.Y., Ikram, M.K., Chen, C., Wong, T.Y.: Imaging retina to study dementia and stroke. Prog. Retin. Eye Res. 57, 89\u2013107 (2017)","journal-title":"Prog. Retin. Eye Res."},{"key":"16_CR7","first-page":"436","volume":"7","author":"Z Li","year":"2018","unstructured":"Li, Z., Keel, S., Liu, C., He, M.: Can artificial intelligence make screening faster, more accurate, and more accessible? Asia Pac. J. Ophthalmol. (Phila) 7, 436\u2013441 (2018)","journal-title":"Asia Pac. J. Ophthalmol. (Phila)"},{"key":"16_CR8","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","volume":"2","author":"R Poplin","year":"2018","unstructured":"Poplin, R., et al.: Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2, 158\u2013164 (2018)","journal-title":"Nat. Biomed. Eng."},{"key":"16_CR9","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014)"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Gastal, E.S.L., Oliveira, M.M.: Domain transform for edge-aware image and video processing. ACM Trans. Graph. 30(4), 69 (2011)","DOI":"10.1145\/2010324.1964964"},{"key":"16_CR11","doi-asserted-by":"publisher","first-page":"2401","DOI":"10.1109\/TPAMI.2013.51","volume":"35","author":"X Geng","year":"2013","unstructured":"Geng, X., Yin, C., Zhou, Z.: Facial age estimation by learning from label distributions. IEEE Trans. Pattern Anal. Mach. Intell. 35, 2401\u20132412 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"16_CR12","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.patrec.2017.05.018","volume":"105","author":"J de la Torre","year":"2018","unstructured":"de la Torre, J., Puig, D., Valls, A.: Weighted kappa loss function for multi-class classification of ordinal data in deep learning. Pattern Recognit. Lett. 105, 144\u2013154 (2018)","journal-title":"Pattern Recognit. Lett."},{"key":"16_CR13","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1136\/bjophthalmol-2017-310368","volume":"102","author":"G Jin","year":"2018","unstructured":"Jin, G., et al.: Prevalence of age-related macular degeneration in rural southern China: the Yangxi eye study. Br. J. Ophthalmol. 102, 625\u2013630 (2018)","journal-title":"Br. J. Ophthalmol."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32239-7_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:04:44Z","timestamp":1728518684000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32239-7_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322380","9783030322397"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32239-7_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1730","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"539","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.07","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6.31","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}