{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T04:40:11Z","timestamp":1745642411240,"version":"3.40.4"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031866500","type":"print"},{"value":"9783031866517","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-3-031-86651-7_20","type":"book-chapter","created":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T04:26:44Z","timestamp":1745641604000},"page":"201-210","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classification and\u00a0Prediction of\u00a0Age-Related Macular Degeneration Progression Using OCT Images and\u00a0Multiple Instance Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5406-1587","authenticated-orcid":false,"given":"Alberto J.","family":"Beltr\u00e1n-Carrero","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javier","family":"Torresano-Rodr\u00edguez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esther","family":"Santos-Vicente","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mar\u00eda J. Aparicio","family":"Hern\u00e1ndez-Lastras","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c1lvaro","family":"Caballero-Sastre","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6846-3923","authenticated-orcid":false,"given":"Mar\u00eda J.","family":"Ledesma-Carbayo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5073-5908","authenticated-orcid":false,"given":"Juan J.","family":"G\u00f3mez-Valverde","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,27]]},"reference":[{"issue":"2","key":"20_CR1","doi-asserted-by":"publisher","first-page":"e106","DOI":"10.1016\/S2214-109X(13)70145-1","volume":"2","author":"WL Wong","year":"2014","unstructured":"Wong, W.L., et al.: Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob. Health 2(2), e106\u2013e116 (2014). https:\/\/doi.org\/10.1016\/S2214-109X(13)70145-1","journal-title":"Lancet Glob. Health"},{"key":"20_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12886-020-01554-2","volume":"20","author":"RP Finger","year":"2020","unstructured":"Finger, R.P., et al.: Anti-vascular endothelial growth factor in neovascular age-related macular degeneration-a systematic review of the impact of anti-VEGF on patient outcomes and healthcare systems. BMC Ophthalmol. 20, 1\u201314 (2020). https:\/\/doi.org\/10.1186\/s12886-020-01554-2","journal-title":"BMC Ophthalmol."},{"key":"20_CR3","doi-asserted-by":"publisher","unstructured":"Ruiz-Moreno, J.M., Arias, L., Abraldes, M.J., Montero, J., Udaondo, P., The RAMDEBURS Study Group: Economic burden of age-related macular degeneration in routine clinical practice: the RAMDEBURS study. Int. Ophthalmol. 41(10), 3427\u20133436 (2021). https:\/\/doi.org\/10.1007\/s10792-021-01906-x","DOI":"10.1007\/s10792-021-01906-x"},{"issue":"7","key":"20_CR4","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1111\/ceo.13837","volume":"48","author":"R Guymer","year":"2020","unstructured":"Guymer, R., Wu, Z.: Age-related macular degeneration (AMD): more than meets the eye. The role of multimodal imaging in today\u2019s management of AMD-a review. Clin. Exp. Ophthalmol. 48(7), 983\u2013995 (2020). https:\/\/doi.org\/10.1111\/ceo.13837","journal-title":"Clin. Exp. Ophthalmol."},{"key":"20_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.preteyeres.2015.07.007","volume":"50","author":"U Schmidt-Erfurth","year":"2016","unstructured":"Schmidt-Erfurth, U., Waldstein, S.M.: A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration. Prog. Retin. Eye Res. 50, 1\u201324 (2016). https:\/\/doi.org\/10.1016\/j.preteyeres.2015.07.007","journal-title":"Prog. Retin. Eye Res."},{"key":"20_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.preteyeres.2021.100972","volume":"86","author":"U Schmidt-Erfurth","year":"2022","unstructured":"Schmidt-Erfurth, U., et al.: Ai-based monitoring of retinal fluid in disease activity and under therapy. Prog. Retin. Eye Res. 86, 100972 (2022). https:\/\/doi.org\/10.1016\/j.preteyeres.2021.100972","journal-title":"Prog. Retin. Eye Res."},{"issue":"7","key":"20_CR7","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/j.oret.2021.05.001","volume":"5","author":"TE Tan","year":"2021","unstructured":"Tan, T.E., Wong, T.Y., Ting, D.: Artificial intelligence for prediction of anti-VEGF treatment burden in retinal diseases: towards precision medicine. Ophthalmol. Retina 5(7), 601\u2013603 (2021). https:\/\/doi.org\/10.1016\/j.oret.2021.05.001","journal-title":"Ophthalmol. Retina"},{"key":"20_CR8","doi-asserted-by":"publisher","unstructured":"Banerjee, I., et al.: A deep-learning approach for prognosis of age-related macular degeneration disease using SD-OCT imaging biomarkers. arXiv preprint arXiv:1902.10700 (2019). https:\/\/doi.org\/10.48550\/arXiv.1902.10700","DOI":"10.48550\/arXiv.1902.10700"},{"issue":"12","key":"20_CR9","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1136\/bjophthalmol-2019-315338","volume":"104","author":"Y Liu","year":"2020","unstructured":"Liu, Y., et al.: Prediction of OCT images of short-term response to anti-VEGF treatment for neovascular age-related macular degeneration using generative adversarial network. Br. J. Ophthalmol. 104(12), 1735\u20131740 (2020). https:\/\/doi.org\/10.1136\/bjophthalmol-2019-315338","journal-title":"Br. J. Ophthalmol."},{"issue":"7","key":"20_CR10","doi-asserted-by":"publisher","first-page":"3240","DOI":"10.1167\/iovs.16-21053","volume":"58","author":"H Bogunovi\u0107","year":"2017","unstructured":"Bogunovi\u0107, H., et al.: Prediction of anti-VEGF treatment requirements in neovascular AMD using a machine learning approach. Invest. Ophthalmol. Vis. Sci. 58(7), 3240\u20133248 (2017). https:\/\/doi.org\/10.1167\/iovs.16-21053","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"issue":"7","key":"20_CR11","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1016\/j.oret.2021.05.002","volume":"5","author":"M Gallardo","year":"2021","unstructured":"Gallardo, M., et al.: Machine learning can predict anti-VEGF treatment demand in a treat-and-extend regimen for patients with neovascular AMD, DME, and RVO associated macular edema. Ophthalmol. Retina 5(7), 604\u2013624 (2021). https:\/\/doi.org\/10.1016\/j.oret.2021.05.002","journal-title":"Ophthalmol. Retina"},{"issue":"12","key":"20_CR12","doi-asserted-by":"publisher","first-page":"3456","DOI":"10.1109\/JBHI.2020.3000136","volume":"24","author":"D Romo-Bucheli","year":"2020","unstructured":"Romo-Bucheli, D., Erfurth, U.S., Bogunovi\u0107, H.: End-to-end deep learning model for predicting treatment requirements in neovascular AMD from longitudinal retinal oct imaging. IEEE J. Biomed. Health Inform. 24(12), 3456\u20133465 (2020). https:\/\/doi.org\/10.1109\/JBHI.2020.3000136","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"7981","key":"20_CR13","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., et al.: A foundation model for generalizable disease detection from retinal images. Nature 622(7981), 156\u2013163 (2023). https:\/\/doi.org\/10.1038\/s41586-023-06555-x","journal-title":"Nature"},{"key":"20_CR14","unstructured":"Vaswani, A., et al.: Attention is all you need (2023). https:\/\/arxiv.org\/abs\/1706.03762"},{"key":"20_CR15","unstructured":"Jorge Cardoso, M., Li, W., et\u00a0al.: Monai: an open-source framework for deep learning in healthcare (2022). https:\/\/arxiv.org\/abs\/2211.02701"}],"container-title":["Lecture Notes in Computer Science","Image-Based Prediction of Retinal Disease Progression"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-86651-7_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T04:26:46Z","timestamp":1745641606000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-86651-7_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031866500","9783031866517"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-86651-7_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"27 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MARIO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Challenge on Monitoring Age-Related Macular Degeneration Progression in Optical Coherence Tomography","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":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mario2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/youvenz.github.io\/MARIO_challenge.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}