{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T16:02:34Z","timestamp":1792598554994,"version":"4.5.0"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T00:00:00Z","timestamp":1572825600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T00:00:00Z","timestamp":1572825600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Graefes Arch Clin Exp Ophthalmol"],"published-print":{"date-parts":[[2020,1]]},"DOI":"10.1007\/s00417-019-04493-x","type":"journal-article","created":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T18:03:42Z","timestamp":1572890622000},"page":"17-21","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Utility of a public-available artificial intelligence in diagnosis of polypoidal choroidal vasculopathy"],"prefix":"10.1007","volume":"258","author":[{"given":"Jingyuan","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenxi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erqian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7231-5058","authenticated-orcid":false,"given":"Youxin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,11,4]]},"reference":[{"key":"4493_CR1","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1016\/j.ophtha.2017.11.019","volume":"125","author":"CMG Cheung","year":"2018","unstructured":"Cheung CMG, Lai TYY, Ruamviboonsuk P, Chen SJ, Chen Y, Freund KB, Gomi F, Koh AH, Lee WK, Wong TY (2018) Polypoidal choroidal vasculopathy: definition, pathogenesis, diagnosis, and management. Ophthalmology 125:708\u2013724. \nhttps:\/\/doi.org\/10.1016\/j.ophtha.2017.11.019","journal-title":"Ophthalmology"},{"key":"4493_CR2","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41433-018-0158-4","volume":"33","author":"CMG Cheung","year":"2019","unstructured":"Cheung CMG, Lee WK, Koizumi H, Dansingani K, Lai TYY, Freund KB (2019) Pachychoroid disease. Eye (London, England) 33:14\u201333. \nhttps:\/\/doi.org\/10.1038\/s41433-018-0158-4","journal-title":"Eye (London, England)"},{"key":"4493_CR3","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1097\/IAE.0b013e3182852446","volume":"33","author":"AH Koh","year":"2013","unstructured":"Koh AH, Chen LJ, Chen SJ, Chen Y, Giridhar A, Iida T, Kim H, Yuk Yau Lai T, Lee WK, Li X, Han Lim T, Ruamviboonsuk P, Sharma T, Tang S, Yuzawa M (2013) Polypoidal choroidal vasculopathy: evidence-based guidelines for clinical diagnosis and treatment. Retina 33:686\u2013716. \nhttps:\/\/doi.org\/10.1097\/IAE.0b013e3182852446","journal-title":"Retina"},{"key":"4493_CR4","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1038\/eye.2016.71","volume":"30","author":"A Ozkaya","year":"2016","unstructured":"Ozkaya A, Alagoz C, Garip R, Alkin Z, Perente I, Yazici AT, Taskapili M (2016) The role of indocyanine green angiography imaging in further differential diagnosis of patients with nAMD who are morphologically poor responders to ranibizumab in a real-life setting. Eye (Lond) 30:958\u2013965. \nhttps:\/\/doi.org\/10.1038\/eye.2016.71","journal-title":"Eye (Lond)"},{"key":"4493_CR5","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1111\/aos.12463","volume":"92","author":"GK Broadhead","year":"2014","unstructured":"Broadhead GK, Hong T, Chang AA (2014) Treating the untreatable patient: current options for the management of treatment-resistant neovascular age-related macular degeneration. Acta Ophthalmol 92:713\u2013723. \nhttps:\/\/doi.org\/10.1111\/aos.12463","journal-title":"Acta Ophthalmol"},{"key":"4493_CR6","doi-asserted-by":"publisher","first-page":"1923","DOI":"10.1007\/s00417-016-3333-y","volume":"254","author":"CS Tan","year":"2016","unstructured":"Tan CS, Ngo WK, Lim LW, Tan NW, Lim TH (2016) EVEREST study report 3: diagnostic challenges of polypoidal choroidal vasculopathy. Lessons learnt from screening failures in the EVEREST study. Graefes Arch Clin Exp ophthalmol 254:1923\u20131930. \nhttps:\/\/doi.org\/10.1007\/s00417-016-3333-y","journal-title":"Graefes Arch Clin Exp ophthalmol"},{"key":"4493_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.preteyeres.2018.07.004","volume":"67","author":"U Schmidt-Erfurth","year":"2018","unstructured":"Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunovic H (2018) Artificial intelligence in retina. Prog Retin Eye Res 67:1\u201329. \nhttps:\/\/doi.org\/10.1016\/j.preteyeres.2018.07.004","journal-title":"Prog Retin Eye Res"},{"key":"4493_CR8","doi-asserted-by":"publisher","first-page":"5278196","DOI":"10.1155\/2018\/5278196","volume":"2018","author":"W Lu","year":"2018","unstructured":"Lu W, Tong Y, Yu Y, Xing Y, Chen C, Shen Y (2018) Applications of artificial intelligence in ophthalmology: general overview. J Ophthalmol 2018:5278196. \nhttps:\/\/doi.org\/10.1155\/2018\/5278196","journal-title":"J Ophthalmol"},{"key":"4493_CR9","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"L Yann","year":"1998","unstructured":"Yann L, Leon B, Yoshua B, Patrick H (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278\u20132324","journal-title":"Proc IEEE"},{"key":"4493_CR10","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1007\/s10916-018-1088-1","volume":"42","author":"SM Anwar","year":"2018","unstructured":"Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK (2018) Medical image analysis using convolutional neural networks: a review. J Med Syst 42:226. \nhttps:\/\/doi.org\/10.1007\/s10916-018-1088-1","journal-title":"J Med Syst"},{"key":"4493_CR11","unstructured":"Zoph B, Le QV (2016) Neural architecture search with reinforcement learning. \nhttps:\/\/arxiv.org\/abs\/1611.01578"},{"key":"4493_CR12","doi-asserted-by":"crossref","unstructured":"Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8697\u20138710","DOI":"10.1109\/CVPR.2018.00907"},{"key":"4493_CR13","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:2402\u20132410. \nhttps:\/\/doi.org\/10.1001\/jama.2016.17216","journal-title":"JAMA"},{"key":"4493_CR14","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1001\/jamaophthalmol.2017.3782","volume":"135","author":"PM Burlina","year":"2017","unstructured":"Burlina PM, Joshi N, Pekala M, Pacheco KD, Freund DE, Bressler NM (2017) Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA ophthalmology 135:1170\u20131176. \nhttps:\/\/doi.org\/10.1001\/jamaophthalmol.2017.3782","journal-title":"JAMA ophthalmology"},{"key":"4493_CR15","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1007\/s00417-017-3850-3","volume":"256","author":"M Treder","year":"2018","unstructured":"Treder M, Lauermann JL, Eter N (2018) Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning. Graefes Arch Clin Exp Ophthalmol 256:259\u2013265. \nhttps:\/\/doi.org\/10.1007\/s00417-017-3850-3","journal-title":"Graefes Arch Clin Exp Ophthalmol"},{"key":"4493_CR16","doi-asserted-by":"publisher","first-page":"3998","DOI":"10.1167\/iovs.15-17207","volume":"56","author":"M Yuzawa","year":"2015","unstructured":"Yuzawa M (2015) Two subtypes of polypoidal choroidal vasculopathy: idiopathic disease or age-related macular degeneration. Invest Ophthalmol Vis Sci 56:3998. \nhttps:\/\/doi.org\/10.1167\/iovs.15-17207","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"4493_CR17","doi-asserted-by":"publisher","first-page":"2053","DOI":"10.1007\/s00417-018-4098-2","volume":"256","author":"M Treder","year":"2018","unstructured":"Treder M, Lauermann JL, Eter N (2018) Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier. Graefes Arch Clin Exp Ophthalmol 256:2053\u20132060. \nhttps:\/\/doi.org\/10.1007\/s00417-018-4098-2","journal-title":"Graefes Arch Clin Exp Ophthalmol"},{"key":"4493_CR18","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.artmed.2017.09.006","volume":"85","author":"E Saleh","year":"2018","unstructured":"Saleh E, Blaszczynski J, Moreno A, Valls A, Romero-Aroca P, de la Riva-Fernandez S, Slowinski R (2018) Learning ensemble classifiers for diabetic retinopathy assessment. Artif Intell Med 85:50\u201363. \nhttps:\/\/doi.org\/10.1016\/j.artmed.2017.09.006","journal-title":"Artif Intell Med"},{"key":"4493_CR19","doi-asserted-by":"publisher","first-page":"3240","DOI":"10.1167\/iovs.16-21053","volume":"58","author":"H Bogunovic","year":"2017","unstructured":"Bogunovic H, Waldstein SM, Schlegl T, Langs G, Sadeghipour A, Liu X, Gerendas BS, Osborne A, Schmidt-Erfurth U (2017) Prediction of anti-VEGF treatment requirements in neovascular AMD using a machine learning approach. Invest Ophthalmol Vis Sci 58:3240\u20133248. \nhttps:\/\/doi.org\/10.1167\/iovs.16-21053","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"4493_CR20","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.1016\/j.ophtha.2017.12.034","volume":"125","author":"M Rohm","year":"2018","unstructured":"Rohm M, Tresp V, Muller M, Kern C, Manakov I, Weiss M, Sim DA, Priglinger S, Keane PA, Kortuem K (2018) Predicting visual acuity by using machine learning in patients treated for neovascular age-related macular degeneration. Ophthalmology 125:1028\u20131036. \nhttps:\/\/doi.org\/10.1016\/j.ophtha.2017.12.034","journal-title":"Ophthalmology"},{"key":"4493_CR21","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1089\/heq.2018.0037","volume":"2","author":"J Guo","year":"2018","unstructured":"Guo J, Li B (2018) The application of medical artificial intelligence technology in rural areas of developing countries. Health Equity 2:174\u2013181. \nhttps:\/\/doi.org\/10.1089\/heq.2018.0037","journal-title":"Health Equity"}],"container-title":["Graefe's Archive for Clinical and Experimental Ophthalmology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00417-019-04493-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00417-019-04493-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00417-019-04493-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:23:01Z","timestamp":1604362981000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00417-019-04493-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,4]]},"references-count":21,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,1]]}},"alternative-id":["4493"],"URL":"https:\/\/doi.org\/10.1007\/s00417-019-04493-x","relation":{},"ISSN":["0721-832X","1435-702X"],"issn-type":[{"value":"0721-832X","type":"print"},{"value":"1435-702X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,4]]},"assertion":[{"value":"2 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2019","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All applicable international, national, and\/or institutional guidelines for the care and use of animals were followed. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study formal consent is not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}