{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T23:50:48Z","timestamp":1790380248426,"version":"4.1.0"},"reference-count":51,"publisher":"Elsevier BV","issue":"10","license":[{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T00:00:00Z","timestamp":1663977600000},"content-version":"am","delay-in-days":358,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/100001818","name":"Research to Prevent Blindness","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001818","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000200","name":"United States Agency for International Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000200","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000053","name":"National Eye Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000053","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012712","name":"Helen Keller Foundation for Research and Education","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012712","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Ophthalmology Retina"],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1016\/j.oret.2020.12.013","type":"journal-article","created":{"date-parts":[[2021,2,7]],"date-time":"2021-02-07T03:28:01Z","timestamp":1612668481000},"page":"1027-1035","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":64,"title":["Deep Learning for the Diagnosis of Stage in\u00a0Retinopathy of Prematurity"],"prefix":"10.1016","volume":"5","author":[{"given":"Jimmy S.","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aaron S.","family":"Coyner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan","family":"Ostmo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2816-6438","authenticated-orcid":false,"given":"Kemal","family":"Sonmez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanyam","family":"Bajimaya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6670-5316","authenticated-orcid":false,"given":"Eli","family":"Pradhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nita","family":"Valikodath","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily D.","family":"Cole","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7091-6901","authenticated-orcid":false,"given":"Tala","family":"Al-Khaled","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. V. Paul","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6641-2030","authenticated-orcid":false,"given":"Praveer","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jayashree","family":"Kalpathy-Cramer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael F.","family":"Chiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. Peter","family":"Campbell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.oret.2020.12.013_bib1","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1001\/archopht.123.7.991","article-title":"The international classification of retinopathy of prematurity revisited","volume":"123","year":"2005","journal-title":"Arch Ophthalmol"},{"key":"10.1016\/j.oret.2020.12.013_bib2","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1001\/archopht.1988.01060130517027","article-title":"Multicenter trial of cryotherapy for retinopathy of prematurity: preliminary results","volume":"106","year":"1988","journal-title":"Arch Ophthalmol"},{"key":"10.1016\/j.oret.2020.12.013_bib3","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1001\/archopht.121.12.1684","article-title":"Revised indications for the treatment of retinopathy of prematurity: results of the Early Treatment for Retinopathy of Prematurity randomized trial","volume":"121","year":"2003","journal-title":"Arch Ophthalmol"},{"key":"10.1016\/j.oret.2020.12.013_bib4","first-page":"1","article-title":"Fellowship training in retinopathy of prematurity","volume":"16","author":"Wallace","year":"2012","journal-title":"JAAPOS"},{"key":"10.1016\/j.oret.2020.12.013_bib5","doi-asserted-by":"crossref","first-page":"2644","DOI":"10.1016\/j.ophtha.2012.07.015","article-title":"Retinopathy of prematurity residency training","volume":"119","author":"Nagiel","year":"2012","journal-title":"Ophthalmology"},{"key":"10.1016\/j.oret.2020.12.013_bib6","first-page":"177","article-title":"Training fellows for retinopathy of prematurity care: a web-based survey","volume":"16","author":"Wong","year":"2012","journal-title":"J\u00a0AAPOS"},{"issue":"suppl 1","key":"10.1016\/j.oret.2020.12.013_bib7","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1038\/pr.2013.205","article-title":"Preterm-associated visual impairment and estimates of retinopathy of prematurity at regional and global levels for 2010","volume":"74","author":"Blencowe","year":"2013","journal-title":"Pediatr Res"},{"key":"10.1016\/j.oret.2020.12.013_bib8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1167\/tvst.9.2.5","article-title":"Artificial intelligence in retinopathy of prematurity diagnosis","volume":"9","author":"Scruggs","year":"2020","journal-title":"Transl Vis Sci Technol"},{"key":"10.1016\/j.oret.2020.12.013_bib9","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.3174\/ajnr.A5667","article-title":"Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas","volume":"39","author":"Chang","year":"2020","journal-title":"Am J Neuroradiol"},{"key":"10.1016\/j.oret.2020.12.013_bib10","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2018.00804","article-title":"Glioma grading on conventional MR images: a deep learning study with transfer learning","volume":"12","author":"Yang","year":"2018","journal-title":"Front Neurosci"},{"key":"10.1016\/j.oret.2020.12.013_bib11","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"10.1016\/j.oret.2020.12.013_bib12","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1001\/jamaophthalmol.2019.3501","article-title":"Development and validation of a deep learning system to detect glaucomatous optic neuropathy using fundus photographs","volume":"137","author":"Liu","year":"2019","journal-title":"JAMA Ophthalmol"},{"key":"10.1016\/j.oret.2020.12.013_bib13","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1016\/j.ophtha.2018.01.023","article-title":"Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs","volume":"125","author":"Li","year":"2018","journal-title":"Ophthalmology"},{"key":"10.1016\/j.oret.2020.12.013_bib14","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/s41746-018-0040-6","article-title":"Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices","volume":"1","author":"Abr\u00e0moff","year":"2018","journal-title":"Npj Digit Med"},{"key":"10.1016\/j.oret.2020.12.013_bib15","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.ophtha.2017.02.008","article-title":"Automated identification of diabetic retinopathy using deep learning","volume":"124","author":"Gargeya","year":"2017","journal-title":"Ophthalmology"},{"key":"10.1016\/j.oret.2020.12.013_bib16","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"10.1016\/j.oret.2020.12.013_bib17","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1001\/jamaophthalmol.2018.4118","article-title":"Use of deep learning for detailed severity characterization and estimation of 5-year risk among patients with age-related macular degeneration","volume":"136","author":"Burlina","year":"2018","journal-title":"JAMA Ophthalmol"},{"key":"10.1016\/j.oret.2020.12.013_bib18","doi-asserted-by":"crossref","first-page":"1410","DOI":"10.1016\/j.ophtha.2018.02.037","article-title":"A\u00a0deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography","volume":"125","author":"Grassmann","year":"2018","journal-title":"Ophthalmology"},{"key":"10.1016\/j.oret.2020.12.013_bib19","doi-asserted-by":"crossref","first-page":"103530","DOI":"10.1109\/ACCESS.2019.2930120","article-title":"A\u00a0deep learning framework for identifying zone I in RetCam images","volume":"7","author":"Zhao","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.oret.2020.12.013_bib20","first-page":"01582","article-title":"Retinopathy of prematurity stage diagnosis using object segmentation and convolutional neural networks","author":"Ding","year":"2004","journal-title":"Preprint. arXiv"},{"key":"10.1016\/j.oret.2020.12.013_bib21","series-title":"Ophthalmic Medical Image Analysis","first-page":"173","article-title":"Automated stage analysis of retinopathy of prematurity using joint segmentation and multi-instance learning","author":"Chen","year":"2019"},{"key":"10.1016\/j.oret.2020.12.013_bib22","first-page":"1","article-title":"Early detection of retinopathy of prematurity stage using deep learning approach","volume":"10950","author":"Mulay","year":"2019","journal-title":"SPIE Proc"},{"key":"10.1016\/j.oret.2020.12.013_bib23","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1109\/TMI.2018.2863562","article-title":"Automated analysis for retinopathy of prematurity by deep neural networks","volume":"38","author":"Hu","year":"2019","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.oret.2020.12.013_bib24","article-title":"Automated detection of early-stage ROP using a deep convolutional neural network","author":"Huang","year":"2020","journal-title":"Br J Ophthalmol"},{"key":"10.1016\/j.oret.2020.12.013_bib25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1167\/tvst.8.6.23","article-title":"Deep learning algorithm for automated diagnosis of retinopathy of prematurity plus disease","volume":"8","author":"Tan","year":"2019","journal-title":"Transl Vis Sci Technol"},{"key":"10.1016\/j.oret.2020.12.013_bib26","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1001\/jamaophthalmol.2018.1934","article-title":"Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks","volume":"136","author":"Brown","year":"2018","journal-title":"JAMA Ophthalmol"},{"key":"10.1016\/j.oret.2020.12.013_bib27","first-page":"1902","article-title":"Development and evaluation of reference standards for image-based telemedicine diagnosis and clinical research studies in ophthalmology","volume":"2014","author":"Ryan","year":"2014","journal-title":"AMIA Annu Symp Proc"},{"key":"10.1016\/j.oret.2020.12.013_bib28","series-title":"Dr Dobb\u2019s Journal of Software Tools","author":"Bradski","year":"2000"},{"key":"10.1016\/j.oret.2020.12.013_bib29","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat Methods"},{"key":"10.1016\/j.oret.2020.12.013_bib30","series-title":"Python Reference Manual","author":"Van Rossum","year":"1995"},{"key":"10.1016\/j.oret.2020.12.013_bib31","article-title":"Automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis","volume":"2015","author":"Raja","year":"2015","journal-title":"Comput Math Methods Med"},{"key":"10.1016\/j.oret.2020.12.013_bib32","doi-asserted-by":"crossref","first-page":"6821","DOI":"10.1016\/j.ijleo.2016.05.002","article-title":"Image denoising using local Wiener filter and its method noise","volume":"127","author":"Zhang","year":"2016","journal-title":"Optik"},{"key":"10.1016\/j.oret.2020.12.013_bib33","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 770-778, https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.oret.2020.12.013_bib34","series-title":"Advances in Neural Information Processing Systems","first-page":"8024","article-title":"PyTorch: an imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019"},{"key":"10.1016\/j.oret.2020.12.013_bib35","doi-asserted-by":"crossref","unstructured":"McInnes L, Healy J, Saul N, Gro\u00dfberger L. UMAP: Uniform Manifold Approximation and Projection. Journal of Open Source Software, 3 861, Available at https:\/\/doi.org\/10.21105\/joss.00861.","DOI":"10.21105\/joss.00861"},{"key":"10.1016\/j.oret.2020.12.013_bib36","doi-asserted-by":"crossref","first-page":"31","DOI":"10.2147\/EB.S94436","article-title":"Retinopathy of prematurity blindness worldwide: phenotypes in the third epidemic","volume":"8","author":"Quinn","year":"2016","journal-title":"Eye Brain"},{"key":"10.1016\/j.oret.2020.12.013_bib37","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.18240\/ijo.2019.08.15","article-title":"Incidence and risk factors of retinopathy of prematurity and utility of the national screening criteria in a tertiary center in Iran","volume":"12","author":"Khorshidifar","year":"2019","journal-title":"Int J Ophthalmol"},{"key":"10.1016\/j.oret.2020.12.013_bib38","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.ajo.2009.04.018","article-title":"Incidence of retinopathy of prematurity in the United States: 1997 through 2005","volume":"148","author":"Lad","year":"2009","journal-title":"Am J Ophthalmol"},{"key":"10.1016\/j.oret.2020.12.013_bib39","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000000867","article-title":"Incidence and risk factors for retinopathy of prematurity in multiple gestations: a Chinese population study","volume":"94","author":"Yau","year":"2015","journal-title":"Medicine (Balt)"},{"key":"10.1016\/j.oret.2020.12.013_bib40","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1136\/bjophthalmol-2018-313156","article-title":"Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity","volume":"103","author":"Redd","year":"2019","journal-title":"Br J Ophthalmol"},{"key":"10.1016\/j.oret.2020.12.013_bib41","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"10.1016\/j.oret.2020.12.013_bib42","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.1016\/j.jacr.2020.05.015","article-title":"Multi-institutional assessment and crowdsourcing evaluation of deep learning for automated classification of breast density","volume":"17","author":"Chang","year":"2020","journal-title":"J\u00a0Am Coll Radiol"},{"key":"10.1016\/j.oret.2020.12.013_bib43","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41746-019-0105-1","article-title":"Deep learning predicts hip fracture using confounding patient and healthcare variables","volume":"2","author":"Badgeley","year":"2019","journal-title":"Npj Digit Med"},{"key":"10.1016\/j.oret.2020.12.013_bib44","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1002683","article-title":"Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study","volume":"15","author":"Zech","year":"2018","journal-title":"PLOS Med"},{"key":"10.1016\/j.oret.2020.12.013_bib45","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1002\/mp.12752","article-title":"Deep learning for segmentation of brain tumors: impact of cross-institutional training and testing","volume":"45","author":"AlBadawy","year":"2018","journal-title":"Med Phys"},{"key":"10.1016\/j.oret.2020.12.013_bib46","doi-asserted-by":"crossref","first-page":"12592","DOI":"10.1073\/pnas.1919012117","article-title":"Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis","volume":"117","author":"Larrazabal","year":"2020","journal-title":"Proc Natl Acad Sci U S A"},{"key":"10.1016\/j.oret.2020.12.013_bib47","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1001\/jamadermatol.2018.2348","article-title":"Machine learning and health care disparities in dermatology","volume":"154","author":"Adamson","year":"2018","journal-title":"JAMA Dermatol"},{"key":"10.1016\/j.oret.2020.12.013_bib49","series-title":"An adversarial approach for the robust classification of pneumonia from chest radiographs. In Proceedings of the ACM Conference on Health, Inference, and Learning (CHIL '20)","first-page":"69","author":"Janizek","year":"2020"},{"key":"10.1016\/j.oret.2020.12.013_bib50","doi-asserted-by":"crossref","first-page":"2579","DOI":"10.1007\/s00417-019-04470-4","article-title":"Smartphone-based fundus photography for screening of plus-disease retinopathy of prematurity","volume":"257","author":"Patel","year":"2019","journal-title":"Graefes Arch Clin Exp Ophthalmol"},{"key":"10.1016\/j.oret.2020.12.013_bib51","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1001\/jamaophthalmol.2020.2769","article-title":"Model-to-data approach for deep learning in optical coherence tomography intraretinal fluid segmentation","volume":"138","author":"Mehta","year":"2020","journal-title":"JAMA Ophthalmol"},{"key":"10.1016\/j.oret.2020.12.013_bib52","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","article-title":"Clinically applicable deep learning for diagnosis and referral in retinal disease","volume":"24","author":"De Fauw","year":"2018","journal-title":"Nat Med"}],"container-title":["Ophthalmology Retina"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2468653020305005?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2468653020305005?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T09:30:13Z","timestamp":1762853413000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2468653020305005"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10]]},"references-count":51,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["S2468653020305005"],"URL":"https:\/\/doi.org\/10.1016\/j.oret.2020.12.013","relation":{},"ISSN":["2468-6530"],"issn-type":[{"value":"2468-6530","type":"print"}],"subject":[],"published":{"date-parts":[[2021,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep Learning for the Diagnosis of Stage in\u00a0Retinopathy of Prematurity","name":"articletitle","label":"Article Title"},{"value":"Ophthalmology Retina","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.oret.2020.12.013","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 by the American Academy of Ophthalmology","name":"copyright","label":"Copyright"}]}}