{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:05:45Z","timestamp":1790787945438,"version":"4.1.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T00:00:00Z","timestamp":1722384000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T00:00:00Z","timestamp":1722384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Biomedical Research Foundation of the University Hospital \u201cPr\u00edncipe de Asturias\u201d","award":["FIB-PI19-01"],"award-info":[{"award-number":["FIB-PI19-01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-68489-2","type":"journal-article","created":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T12:25:44Z","timestamp":1722428744000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Hybrid deep learning models for the screening of Diabetic Macular Edema in optical coherence tomography volumes"],"prefix":"10.1038","volume":"14","author":[{"given":"Antonio","family":"Rodr\u00edguez-Miguel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carolina","family":"Arruabarrena","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Germ\u00e1n","family":"Allendes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximiliano","family":"Olivera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier","family":"Zarranz-Ventura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel A.","family":"Teus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,31]]},"reference":[{"key":"68489_CR1","doi-asserted-by":"publisher","first-page":"104","DOI":"10.2174\/1570161117666190405165911","volume":"18","author":"D Lovic","year":"2020","unstructured":"Lovic, D. et al. The growing epidemic of diabetes mellitus. Curr. Vasc. Pharmacol. 18, 104\u2013109 (2020).","journal-title":"Curr. Vasc. Pharmacol."},{"key":"68489_CR2","unstructured":"International Diabetes Federation, 2021. IDF Diabetes, 10th edition. Brussels: Atlas Press. https:\/\/diabetesatlas.org\/. Accessed (28 06 2023)."},{"key":"68489_CR3","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.1016\/j.ophtha.2021.04.027","volume":"128","author":"ZL Teo","year":"2021","unstructured":"Teo, Z. L. et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: Systematic review and meta-analysis. Ophthalmology 128, 1580\u20131591 (2021).","journal-title":"Ophthalmology"},{"key":"68489_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2013.11.041","author":"U Schmidt-Erfurth","year":"2014","unstructured":"Schmidt-Erfurth, U. et al. Three-year outcomes of individualized ranibizumab treatment in patients with diabetic macular edema The restore extension study. Ophthalmology https:\/\/doi.org\/10.1016\/j.ophtha.2013.11.041 (2014).","journal-title":"Ophthalmology"},{"key":"68489_CR5","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s40123-018-0153-7","volume":"7","author":"BJ Fenner","year":"2018","unstructured":"Fenner, B. J. et al. Advances in retinal imaging and applications in diabetic retinopathy screening: A review. Ophthalmol. Ther. 7, 333\u2013346 (2018).","journal-title":"Ophthalmol. Ther."},{"issue":"8","key":"68489_CR6","doi-asserted-by":"publisher","first-page":"1308","DOI":"10.1097\/IAE.0000000000003832","volume":"43","author":"C Arruabarrena","year":"2023","unstructured":"Arruabarrena, C. et al. Evaluation of the inclusion of spectral domain optical coherence tomography in a telemedicine diabetic retinopathy screening program: a real clinical practice. Retina 43(8), 1308\u20131316 (2023).","journal-title":"Retina"},{"issue":"12","key":"68489_CR7","doi-asserted-by":"publisher","first-page":"2959","DOI":"10.2337\/dc17-2612","volume":"43","author":"IYH Wong","year":"2020","unstructured":"Wong, I. Y. H., Wong, R. L. M., Chan, J. C. H., Kawasaki, R. & Chong, V. Incorporating Optical Coherence Tomography Macula Scans Enhances Cost-effectiveness of Fundus Photography-Based Screening for Diabetic Macular Edema. Diabetes Care 43(12), 2959\u20132966 (2020).","journal-title":"Diabetes Care"},{"key":"68489_CR8","doi-asserted-by":"publisher","DOI":"10.1002\/14651858.CD008081.pub3","author":"G Virgili","year":"2015","unstructured":"Virgili, G. et al. Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy. Cochrane Database Syst. Rev. https:\/\/doi.org\/10.1002\/14651858.CD008081.pub3 (2015).","journal-title":"Cochrane Database Syst. Rev."},{"issue":"5","key":"68489_CR9","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1097\/ICU.0000000000000980","volume":"34","author":"C Danese","year":"2023","unstructured":"Danese, C. et al. The impact of artificial intelligence on retinal disease management: Vision Academy retinal expert consensus. Curr. Opin. Ophthalmol. 34(5), 396\u2013402 (2023).","journal-title":"Curr. Opin. Ophthalmol."},{"key":"68489_CR10","doi-asserted-by":"publisher","DOI":"10.22608\/APO.201976","author":"CY Cheung","year":"2019","unstructured":"Cheung, C. Y., Tang, F., Ting, D. S. W., Tan, G. S. W. & Wong, T. Y. Artificial intelligence in diabetic eye disease screening. Asia Pac J. Ophthalmol. https:\/\/doi.org\/10.22608\/APO.201976 (2019).","journal-title":"Asia Pac J. Ophthalmol."},{"issue":"2","key":"68489_CR11","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1167\/tvst.9.2.11","volume":"9","author":"RT Yanagihara","year":"2020","unstructured":"Yanagihara, R. T., Lee, C. S., Ting, D. S. W. & Lee, A. Y. Methodological Challenges of Deep Learning in Optical Coherence Tomography for Retinal Diseases: A Review. Transl. Vis. Sci. Technol. 9(2), 11 (2020).","journal-title":"Transl. Vis. Sci. Technol."},{"issue":"5","key":"68489_CR12","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"D Kermany","year":"2018","unstructured":"Kermany, D. et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. 172(5), 1122-1131.e9 (2018).","journal-title":"Cell."},{"issue":"1","key":"68489_CR13","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1186\/s12938-022-01018-2","volume":"21","author":"R Liu","year":"2022","unstructured":"Liu, R. et al. Application of artificial intelligence-based dual-modality analysis combining fundus photography and optical coherence tomography in diabetic retinopathy screening in a community hospital. Biomed. Eng. Online 21(1), 47 (2022).","journal-title":"Biomed. Eng. Online"},{"key":"68489_CR14","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/s41746-019-0099-8","volume":"2","author":"P Raumviboonsuk","year":"2019","unstructured":"Raumviboonsuk, P. et al. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. NPJ Digit Med. 2, 25 (2019).","journal-title":"NPJ Digit Med."},{"issue":"1","key":"68489_CR15","doi-asserted-by":"publisher","first-page":"6654","DOI":"10.1038\/s41467-021-26990-6","volume":"12","author":"L Lu","year":"2021","unstructured":"Lu, L., Dercle, L., Zhao, B. & Schwartz, L. H. Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging. Nat. Commun. 12(1), 6654 (2021).","journal-title":"Nat. Commun."},{"issue":"11","key":"68489_CR16","doi-asserted-by":"publisher","first-page":"3266","DOI":"10.1158\/1078-0432.CCR-18-2495","volume":"25","author":"Y Xu","year":"2019","unstructured":"Xu, Y. et al. Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging. Clin. Cancer Res. 25(11), 3266\u20133275 (2019).","journal-title":"Clin. Cancer Res."},{"key":"68489_CR17","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.ajo.2009.02.031","volume":"148","author":"S Vujosevic","year":"2009","unstructured":"Vujosevic, S. et al. Screening for diabetic retinopathy: 1 and 3 nonmydriatic 45-degree digital fundus photographs vs 7 standard early treatment diabetic retinopathy study fields. Am. J. Ophthalmol. 148, 111\u2013118 (2009).","journal-title":"Am. J. Ophthalmol."},{"issue":"1\u20132","key":"68489_CR18","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1080\/08820530490519934","volume":"19","author":"G Panozzo","year":"2004","unstructured":"Panozzo, G. et al. Diabetic macular edema: an OCT-based classification. Semin Ophthalmol. 19(1\u20132), 13\u201320 (2004).","journal-title":"Semin Ophthalmol."},{"issue":"5","key":"68489_CR19","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1097\/APO.0000000000000218","volume":"5","author":"S Ruia","year":"2016","unstructured":"Ruia, S., Saxena, S., Gemmy Cheung, C. M., Gilhotra, J. S. & Lai, T. Y. Spectral Domain Optical Coherence Tomography Features and Classification Systems for Diabetic Macular Edema: A Review. Asia Pac J. Ophthalmol. (Phila) 5(5), 360\u2013367 (2016).","journal-title":"Asia Pac J. Ophthalmol. (Phila)"},{"key":"68489_CR20","doi-asserted-by":"publisher","first-page":"1667","DOI":"10.1016\/S0161-6420(03)00475-5","volume":"110","author":"CP Wilkinson","year":"2003","unstructured":"Wilkinson, C. P. et al. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110, 1667\u20131682 (2003).","journal-title":"Ophthalmology"},{"issue":"4","key":"68489_CR21","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s13312-011-0055-4","volume":"48","author":"R Kumar","year":"2011","unstructured":"Kumar, R. & Indrayan, A. Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatr. 48(4), 277\u2013287 (2011).","journal-title":"Indian Pediatr."},{"issue":"1","key":"68489_CR22","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"JA Hanley","year":"1982","unstructured":"Hanley, J. A. & McNeil, B. J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29\u201336 (1982).","journal-title":"Radiology"},{"issue":"1","key":"68489_CR23","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.survophthal.2022.08.004","volume":"68","author":"MH Shahriari","year":"2023","unstructured":"Shahriari, M. H., Sabbaghi, H., Asadi, F., Hosseini, A. & Khorrami, Z. Artificial intelligence in screening, diagnosis, and classification of diabetic macular edema: A systematic review. Surv. Ophthalmol. 68(1), 42\u201353 (2023).","journal-title":"Surv. Ophthalmol."},{"issue":"8","key":"68489_CR24","doi-asserted-by":"publisher","first-page":"e486","DOI":"10.1016\/S2589-7500(21)00086-8","volume":"3","author":"D Lin","year":"2021","unstructured":"Lin, D. et al. Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study. Lancet Digit Health 3(8), e486\u2013e495 (2021).","journal-title":"Lancet Digit Health"},{"key":"68489_CR25","doi-asserted-by":"crossref","unstructured":"Beede E, Baylor E, Hersch F, et al. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computer Machinery; Honolulu, (2020).","DOI":"10.1145\/3313831.3376718"},{"key":"68489_CR26","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1038\/s41746-018-0040-6","volume":"1","author":"MD Abr\u00e0moff","year":"2018","unstructured":"Abr\u00e0moff, M. D., Lavin, P. T., Birch, M., Shah, N. & Folk, J. C. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit. Med. 1, 39 (2018).","journal-title":"NPJ Digit. Med."},{"key":"68489_CR27","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","volume":"24","author":"J De Fauw","year":"2018","unstructured":"De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342\u20131350 (2018).","journal-title":"Nat. Med."},{"key":"68489_CR28","doi-asserted-by":"publisher","first-page":"2509","DOI":"10.2337\/dc18-0147","volume":"41","author":"Z Li","year":"2018","unstructured":"Li, Z. et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care 41, 2509\u20132516 (2018).","journal-title":"Diabetes Care"},{"issue":"10","key":"68489_CR29","doi-asserted-by":"publisher","first-page":"1937","DOI":"10.1016\/j.ophtha.2009.03.012","volume":"116","author":"R Klein","year":"2009","unstructured":"Klein, R., Lee, K. E., Knudtson, M. D., Gangnon, R. E. & Klein, B. E. Changes in visual impairment prevalence by period of diagnosis of diabetes: the Wisconsin Epidemiologic Study of Diabetic Retinopathy. Ophthalmology 116(10), 1937\u20131942 (2009).","journal-title":"Ophthalmology"},{"key":"68489_CR30","first-page":"837","volume":"1352","author":"UKPDS Group","year":"1998","unstructured":"UKPDS Group. Intensive blood-glucose control with sulphonylurea or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 1352, 837\u2013853 (1998).","journal-title":"Lancet"},{"key":"68489_CR31","volume-title":"Epidemiology by design. A causal approach to the health sciences","author":"D Westreich","year":"2020","unstructured":"Westreich, D. Epidemiology by design. A causal approach to the health sciences (Oxford University Press, 2020)."},{"key":"68489_CR32","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1016\/j.ophtha.2017.02.008","volume":"124","author":"R Gargeya","year":"2017","unstructured":"Gargeya, R. & Leng, T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124, 962\u2013969 (2017).","journal-title":"Ophthalmology"},{"issue":"9","key":"68489_CR33","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1001\/jamaophthalmol.2019.1997","volume":"137","author":"DSW Ting","year":"2019","unstructured":"Ting, D. S. W., Carin, L. & Abramoff, M. D. Observations and Lessons Learned From the Artificial Intelligence Studies for Diabetic Retinopathy Screening. JAMA Ophthalmol. 137(9), 994\u2013995 (2019).","journal-title":"JAMA Ophthalmol."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-68489-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-68489-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-68489-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T12:30:37Z","timestamp":1722429037000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-68489-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,31]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["68489"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-68489-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,31]]},"assertion":[{"value":"17 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests. Javier Zarranz-Ventura receives consultant fees from Topcon, although for other research purposes.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17633"}}