{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:01:18Z","timestamp":1792789278321,"version":"4.5.0"},"reference-count":64,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon Europe research and innovation programme","award":["101137074"],"award-info":[{"award-number":["101137074"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Artificial intelligence has transformed medical diagnostic capabilities, particularly through medical image analysis. AI algorithms perform well in detecting abnormalities with a strong performance, enabling computer-aided diagnosis by analyzing the extensive amounts of patient data. The data serve as a foundation upon which algorithms learn and make predictions. Thus, the importance of data cannot be underestimated, and clinically corresponding datasets are required. Many researchers face a lack of medical data due to limited access, privacy concerns, or the absence of available annotations. One of the most widely used diagnostic tools in ophthalmology is Optical Coherence Tomography (OCT). Addressing the data availability issue is crucial for enhancing AI applications in the field of OCT diagnostics. This review aims to provide a comprehensive analysis of all publicly accessible retinal OCT datasets. Our main objective is to compile a list of OCT datasets and their properties, which can serve as an accessible reference, facilitating data curation for medical image analysis tasks. For this review, we searched through the Zenodo repository, Mendeley Data repository, MEDLINE database, and Google Dataset search engine. We systematically evaluated all the identified datasets and found 23 open-access datasets containing OCT images, which significantly vary in terms of size, scope, and ground-truth labels. Our findings indicate the need for improvement in data-sharing practices and standardized documentation. Enhancing the availability and quality of OCT datasets will support the development of AI algorithms and ultimately improve diagnostic capabilities in ophthalmology. By providing a comprehensive list of accessible OCT datasets, this review aims to facilitate better utilization and development of AI in medical image analysis.<\/jats:p>","DOI":"10.3390\/diagnostics14151668","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T11:34:20Z","timestamp":1722512060000},"page":"1668","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Exploring Publicly Accessible Optical Coherence Tomography Datasets: A Comprehensive Overview"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6912-2565","authenticated-orcid":false,"given":"Anastasiia","family":"Rozhyna","sequence":"first","affiliation":[{"name":"Informatics Institute, University of Applied Sciences Western Switzerland (HES-SO), 3960 Sierre, Switzerland"},{"name":"Medical Informatics, University of Geneva, 1205 Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6329-442X","authenticated-orcid":false,"given":"G\u00e1bor M\u00e1rk","family":"Somfai","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Stadtspital Z\u00fcrich, 8063 Zurich, Switzerland"},{"name":"Spross Research Institute, 8063 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5397-2063","authenticated-orcid":false,"given":"Manfredo","family":"Atzori","sequence":"additional","affiliation":[{"name":"Informatics Institute, University of Applied Sciences Western Switzerland (HES-SO), 3960 Sierre, Switzerland"},{"name":"Department of Neuroscience, University of Padua, 35121 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4726-894X","authenticated-orcid":false,"given":"Delia Cabrera","family":"DeBuc","sequence":"additional","affiliation":[{"name":"Bascom Palmer Eye Institute, Miller School of Medicine, University of Miami, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0574-6739","authenticated-orcid":false,"given":"Amr","family":"Saad","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Stadtspital Z\u00fcrich, 8063 Zurich, Switzerland"},{"name":"Spross Research Institute, 8063 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jay","family":"Zoellin","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Stadtspital Z\u00fcrich, 8063 Zurich, Switzerland"},{"name":"Spross Research Institute, 8063 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6800-9878","authenticated-orcid":false,"given":"Henning","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"Informatics Institute, University of Applied Sciences Western Switzerland (HES-SO), 3960 Sierre, Switzerland"},{"name":"Medical Informatics, University of Geneva, 1205 Geneva, Switzerland"},{"name":"The Sense Research and Innovation Center, 1007 Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/TTS.2023.3234203","article-title":"Challenges of deep learning in medical image analysis\u2014Improving explainability and trust","volume":"4","author":"Dhar","year":"2023","journal-title":"IEEE Trans. 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