{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T18:02:17Z","timestamp":1793988137789,"version":"4.8.0"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>To systematically review and meta-analyze the effectiveness of deep learning algorithms applied to optical coherence tomography (OCT) and retinal images for the detection of diabetic retinopathy (DR).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We conducted a comprehensive literature search in multiple databases including PubMed, Cochrane library, Web of Science, Embase and IEEE Xplore up to July 2024. Studies that utilized deep learning techniques for the detection of DR using OCT and retinal images were included. Data extraction and quality assessment were performed independently by two reviewers. Meta-analysis was conducted to determine pooled sensitivity, specificity, and diagnostic odds ratios.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 47 studies were included in the systematic review, 10 were meta-analyzed, encompassing a total of 188268 retinal images and OCT scans. The meta-analysis revealed a pooled sensitivity of 1.88 (95% CI: 1.45-2.44) and a pooled specificity of 1.33 (95% CI: 0.97-1.84) for the detection of DR using deep learning models. All of the outcome of deep learning-based optical coherence tomography ORs \u22650.785, indicating that all included studies with artificial intelligence assistance produced good boosting results.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Deep learning-based approaches show high accuracy in detecting diabetic retinopathy from OCT and retinal images, supporting their potential as reliable tools in clinical settings. Future research should focus on standardizing datasets, improving model interpretability, and validating performance across diverse populations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Systematic Review Registration<\/jats:title><jats:p><jats:uri>https:\/\/www.crd.york.ac.uk\/PROSPERO\/<\/jats:uri>, identifier CRD42024575847.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fendo.2025.1485311","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T05:21:48Z","timestamp":1742275308000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis"],"prefix":"10.3389","volume":"16","author":[{"given":"Zheng","family":"Bi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinju","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiongyi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaohui","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e1947","DOI":"10.7717\/peerj-cs.1947","article-title":"A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images","volume":"10","author":"Bhulakshmi","year":"2024","journal-title":"PeerJ Comput Sci"},{"key":"B2","doi-asserted-by":"publisher","first-page":"102617","DOI":"10.1016\/j.artmed.2023.102617","article-title":"A systematic literature review of machine learning based risk prediction models for diabetic retinopathy progression","volume":"143","author":"Usman","year":"2023","journal-title":"Artif Intell Med"},{"key":"B3","doi-asserted-by":"publisher","first-page":"614","DOI":"10.3390\/jcm11030614","article-title":"Evaluating a deep learning diabetic retinopathy grading system developed on mydriatic retinal images when applied to non-mydriatic community screening","volume":"11","author":"Nunez do Rio","year":"2022","journal-title":"J Clin Med"},{"key":"B4","doi-asserted-by":"publisher","first-page":"6586157","DOI":"10.1155\/2017\/6586157","article-title":"Bilateral birdshot retinochoroiditis and retinal astrocytoma","volume":"2017","author":"Mamtora","year":"2017","journal-title":"Case Rep Ophthalmol Med"},{"key":"B5","doi-asserted-by":"publisher","first-page":"100377","DOI":"10.1016\/j.imu.2020.100377","article-title":"Diabetic retinopathy detection through deep learning techniques: A review","volume":"20","author":"Alyoubi","year":"2020","journal-title":"Inf Med Unlocked"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1001\/jamaophthalmol.2017.2553","article-title":"Vision-related functional burden of diabetic retinopathy across severity levels in the United States","volume":"135","author":"Willis","year":"2017","journal-title":"JAMA Ophthalmol"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1055\/s-0035-1556731","article-title":"Thrombosis and hemorrhage in diabetic retinopathy: A perspective from an inflammatory standpoint","volume":"41","author":"Murugesan","year":"2015","journal-title":"Semin Thromb Hemost"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1167\/iovs.13-13122","article-title":"Classification of human retinal microaneurysms using adaptive optics scanning light ophthalmoscope fluorescein angiography","volume":"55","author":"Dubow","year":"2014","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"B9","first-page":"19","article-title":"Local binary CNN for diabetic retinopathy classification on fundus images","volume":"19","author":"Pavlovicova","year":"2022","journal-title":"Acta Polytechnica Hungarica"},{"key":"B10","doi-asserted-by":"crossref","DOI":"10.1002\/9781119968573","volume-title":"Handbook of retinal screening in diabetes: diagnosis and management","author":"Taylor","year":"2012"},{"key":"B11","doi-asserted-by":"crossref","DOI":"10.1145\/3290589.3290596","article-title":"Automatic detection of blood vessels and classification in retinal images for diabetic retinopathy diagnosis with application of convolution neural network","author":"Kazakh-British","year":"2018"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1805144","DOI":"10.1080\/23311916.2020.1805144","article-title":"Comparative analysis of deep learning methods of detection of diabetic retinopathy","volume":"7","author":"Pak","year":"2020","journal-title":"Cogent Eng"},{"key":"B13","doi-asserted-by":"publisher","first-page":"327","DOI":"10.5455\/aim.2019.27.327-332","article-title":"Deep transfer learning models for medical diabetic retinopathy detection","volume":"27","author":"Khalifa","year":"2019","journal-title":"Acta Informatica Med"},{"key":"B14","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1136\/bmj.b2535","article-title":"Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement","volume":"151","author":"Moher","year":"2009","journal-title":"Ann Intern Med"},{"key":"B15","article-title":"Application of Cochrane bias risk assessment tool in meta-analysis of randomized controlled study","volume":"29","author":"Hongqiu","year":"2014","journal-title":"Chin Circ J"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.7507\/1672-2531.20140042","article-title":"software calls JAGS software to realize network meta-analysis","volume":"14","author":"Chao","year":"2014","journal-title":"Chin J Evid Based Med"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.7507\/1672-2531.20090196","article-title":"Heterogeneity and its treatment in meta analysis","volume":"9","author":"Dan","year":"2009","journal-title":"Chin J Evid Based Med"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1002\/sim.v21:11","article-title":"Quantifying heterogeneity in a meta-analysis","volume":"21","author":"Higgins","year":"2002","journal-title":"Stat Med"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2012.10.005","article-title":"ADDIS: a decision support system for evidence-based medicine","volume":"55","author":"Van Valkenhoef","year":"2013","journal-title":"Decision Support Syst"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i03","article-title":"Conducting meta-analyses in R with the metafor package","volume":"36","author":"Viechtbauer","year":"2010","journal-title":"J Stat Softw"},{"key":"B21","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1038\/s41467-023-44676-z","article-title":"Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial","volume":"15","author":"Wolf","year":"2024","journal-title":"Nat Commun"},{"key":"B22","doi-asserted-by":"publisher","first-page":"399","DOI":"10.2337\/dc22-0713","article-title":"Effectiveness of nonmydriatic ultra-widefield retinal imaging to screen for diabetic eye disease: A randomized controlled trial (Clearsight)","volume":"46","author":"Liu","year":"2023","journal-title":"Diabetes Care"},{"key":"B23","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1016\/S0161-6420(01)00990-3","article-title":"Clinical evaluation of patients with diabetic retinopathy: accuracy of the Inoveon diabetic retinopathy-3DT system","volume":"109","author":"Fransen","year":"2002","journal-title":"Ophthalmology"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-022-02688-9","article-title":"An automated unsupervised deep learning-based approach for diabetic retinopathy detection","volume":"60","author":"Naz","year":"2022","journal-title":"Med Biol Eng Comput"},{"key":"B25","doi-asserted-by":"publisher","first-page":"e3445","DOI":"10.1002\/dmrr.v37.4","article-title":"Deep learning-based detection and stage grading for optimising diagnosis of diabetic retinopathy","volume":"37","author":"Wang","year":"2021","journal-title":"Diabetes Metab Res Rev"},{"key":"B26","doi-asserted-by":"publisher","first-page":"863","DOI":"10.3390\/healthcare11060863","article-title":"Deep Learning-based prediction of Diabetic Retinopathy using CLAHE and ESRGAN for Enhancement","author":"Alwakid","year":"2023","journal-title":"Healthc MDPI"},{"key":"B27","doi-asserted-by":"publisher","first-page":"3084","DOI":"10.3390\/diagnostics12123084","article-title":"A deep learning based approach for grading of diabetic retinopathy using large fundus image dataset","volume":"12","author":"Mehboob","year":"2022","journal-title":"Diagnostics"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1038\/s41433-021-01552-8","article-title":"Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs","volume":"36","author":"Li","year":"2022","journal-title":"Eye (Lond)"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.4103\/IJO.IJO_11_23","article-title":"Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma","volume":"71","author":"Surya","year":"2023","journal-title":"Indian J Ophthalmol"},{"key":"B30","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s13534-017-0047-y","article-title":"Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy","volume":"8","author":"Mansour","year":"2018","journal-title":"BioMed Eng Lett"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-01963-7","article-title":"Deep neural networks to predict diabetic retinopathy","volume":"14","author":"Gadekallu","year":"2023","journal-title":"J Ambient Intell Humanized Comput"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1038\/s41433-018-0064-9","article-title":"Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence","volume":"32","author":"Rajalakshmi","year":"2018","journal-title":"Eye (Lond)"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.07.102","article-title":"A deep learning interpretable classifier for diabetic retinopathy disease grading","volume":"396","author":"de La Torre","year":"2020","journal-title":"Neurocomputing"},{"key":"B34","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2018.11.016","article-title":"Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy","volume":"126","author":"Sayres","year":"2019","journal-title":"Ophthalmology"},{"key":"B36","doi-asserted-by":"publisher","first-page":"101715","DOI":"10.1016\/j.media.2020.101715","article-title":"DR|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images","volume":"63","author":"Araujo","year":"2020","journal-title":"Med Image Anal"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.1038\/s41433-019-0562-4","article-title":"Artificial intelligence-based screening for diabetic retinopathy at community hospital","volume":"34","author":"He","year":"2020","journal-title":"Eye"},{"key":"B38","doi-asserted-by":"crossref","DOI":"10.1109\/ICIT52682.2021.9491645","article-title":"Diabetic retinopathy detection using ensemble machine learning","author":"Odeh","year":"2021"},{"key":"B39","doi-asserted-by":"crossref","DOI":"10.1109\/CAIDA51941.2021.9425129","article-title":"A comparison of two-stage classifier algorithm with ensemble techniques on detection of diabetic retinopathy","author":"Nagi","year":"2021"},{"key":"B40","first-page":"78","article-title":"Detection and prognosis evaluation of diabetic retinopathy using ensemble deep convolutional neural networks","author":"Sridhar","year":"2020"},{"key":"B41","doi-asserted-by":"publisher","first-page":"9469","DOI":"10.1038\/s41598-021-89027-4","article-title":"Deep learning for gradability classification of handheld, non-mydriatic retinal images","volume":"11","author":"Nderitu","year":"2021","journal-title":"Sci Rep"},{"key":"B42","doi-asserted-by":"publisher","first-page":"130e","DOI":"10.1093\/annonc\/mdy520","article-title":"Reply to the letter to the Editor \u201cReply to \u2018Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists\u2019 by H. A. Haenssle et\u00a0al. \u201c by L. Oakden-Rayner","volume":"30","author":"Haenssle","year":"2019","journal-title":"Ann Oncol"},{"key":"B43","doi-asserted-by":"publisher","first-page":"423","DOI":"10.3390\/e21040423","article-title":"Optimizing deep CNN architectures for face liveness detection","volume":"21","author":"Koshy","year":"2019","journal-title":"Entropy (Basel)"},{"key":"B44","doi-asserted-by":"publisher","first-page":"18054","DOI":"10.1038\/s41598-022-22939-x","article-title":"Automatic identification of early ischemic lesions on non-contrast CT with deep learning approach","volume":"12","author":"Sahoo","year":"2022","journal-title":"Sci Rep"},{"key":"B45","doi-asserted-by":"publisher","first-page":"19442","DOI":"10.1038\/s41598-023-45573-7","article-title":"Localization of early infarction on non-contrast CT images in acute ischemic stroke with deep learning approach","volume":"13","author":"Mohapatra","year":"2023","journal-title":"Sci Rep"}],"container-title":["Frontiers in Endocrinology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2025.1485311\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T05:21:58Z","timestamp":1742275318000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2025.1485311\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,18]]},"references-count":45,"alternative-id":["10.3389\/fendo.2025.1485311"],"URL":"https:\/\/doi.org\/10.3389\/fendo.2025.1485311","relation":{},"ISSN":["1664-2392"],"issn-type":[{"value":"1664-2392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,18]]},"article-number":"1485311"}}