{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T19:43:57Z","timestamp":1772135037333,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,7,2]],"date-time":"2022-07-02T00:00:00Z","timestamp":1656720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"National Funds through the Portuguese funding agency, FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/50014\/2020"],"award-info":[{"award-number":["UIDB\/50014\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"National Funds through the Portuguese funding agency, FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/50008\/2020"],"award-info":[{"award-number":["UIDB\/50008\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT\/MEC through national funds","doi-asserted-by":"publisher","award":["UIDB\/50014\/2020"],"award-info":[{"award-number":["UIDB\/50014\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT\/MEC through national funds","doi-asserted-by":"publisher","award":["UIDB\/50008\/2020"],"award-info":[{"award-number":["UIDB\/50008\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Public databases for glaucoma studies contain color images of the retina, emphasizing the optic papilla. These databases are intended for research and standardized automated methodologies such as those using deep learning techniques. These techniques are used to solve complex problems in medical imaging, particularly in the automated screening of glaucomatous disease. The development of deep learning techniques has demonstrated potential for implementing protocols for large-scale glaucoma screening in the population, eliminating possible diagnostic doubts among specialists, and benefiting early treatment to delay the onset of blindness. However, the images are obtained by different cameras, in distinct locations, and from various population groups and are centered on multiple parts of the retina. We can also cite the small number of data, the lack of segmentation of the optic papillae, and the excavation. This work is intended to offer contributions to the structure and presentation of public databases used in the automated screening of glaucomatous papillae, adding relevant information from a medical point of view. The gold standard public databases present images with segmentations of the disc and cupping made by experts and division between training and test groups, serving as a reference for use in deep learning architectures. However, the data offered are not interchangeable. The quality and presentation of images are heterogeneous. Moreover, the databases use different criteria for binary classification with and without glaucoma, do not offer simultaneous pictures of the two eyes, and do not contain elements for early diagnosis.<\/jats:p>","DOI":"10.3390\/jcm11133850","type":"journal-article","created":{"date-parts":[[2022,7,3]],"date-time":"2022-07-03T22:50:46Z","timestamp":1656888646000},"page":"3850","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2207-0897","authenticated-orcid":false,"given":"Jos\u00e9","family":"Camara","sequence":"first","affiliation":[{"name":"Departamento de Ci\u00eancias e Tecnologia, Universidade Aberta, 1250-100 Lisboa, Portugal"},{"name":"Escola de Ci\u00eancias e Tecnologia, University of Tr\u00e1s-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal"}]},{"given":"Roberto","family":"Rezende","sequence":"additional","affiliation":[{"name":"Departamento de Ci\u00eancias e Tecnologia, Universidade Aberta, 1250-100 Lisboa, Portugal"},{"name":"Escola de Ci\u00eancias e Tecnologia, University of Tr\u00e1s-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3394-6762","authenticated-orcid":false,"given":"Ivan Miguel","family":"Pires","sequence":"additional","affiliation":[{"name":"Escola de Ci\u00eancias e Tecnologia, University of Tr\u00e1s-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade da Beira Interior, 6200-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3458-7693","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Cunha","sequence":"additional","affiliation":[{"name":"Escola de Ci\u00eancias e Tecnologia, University of Tr\u00e1s-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal"},{"name":"Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci\u00eancia, 3200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1016\/j.ophtha.2014.05.013","article-title":"Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis","volume":"121","author":"Tham","year":"2014","journal-title":"Ophthalmology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1159\/000329603","article-title":"Epidemiology of major eye diseases leading to blindness in Europe: A literature review","volume":"47","author":"Prokofyeva","year":"2012","journal-title":"Ophthalmic Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1974","DOI":"10.1016\/j.ophtha.2016.05.029","article-title":"Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier","volume":"123","author":"Asaoka","year":"2016","journal-title":"Ophthalmology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7556","DOI":"10.7150\/thno.38065","article-title":"Current status and future trends of clinical diagnoses via image-based deep learning","volume":"9","author":"Xu","year":"2019","journal-title":"Theranostics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1109\/RBME.2010.2084567","article-title":"Retinal Imaging and Image Analysis","volume":"3","author":"Garvin","year":"2010","journal-title":"IEEE Rev. 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