{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T13:46:55Z","timestamp":1776520015927,"version":"3.51.2"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,10,2]],"date-time":"2021-10-02T00:00:00Z","timestamp":1633132800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,10,2]],"date-time":"2021-10-02T00:00:00Z","timestamp":1633132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Thai Government Research Fund","award":["33\/2560 and 24\/2561"],"award-info":[{"award-number":["33\/2560 and 24\/2561"]}]},{"DOI":"10.13039\/501100004704","name":"National Research Council of Thailand","doi-asserted-by":"publisher","award":["NRCT5-RSA63010-05"],"award-info":[{"award-number":["NRCT5-RSA63010-05"]}],"id":[{"id":"10.13039\/501100004704","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s11042-021-11364-3","type":"journal-article","created":{"date-parts":[[2021,10,4]],"date-time":"2021-10-04T18:24:19Z","timestamp":1633371859000},"page":"1447-1466","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Diabetic eye sentinel: prescreening of diabetic retinopathy using retinal images obtained by a mobile phone camera"],"prefix":"10.1007","volume":"81","author":[{"given":"Thayanee","family":"Ruennak","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8216-8522","authenticated-orcid":false,"given":"Pakinee","family":"Aimmanee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stanislav","family":"Makhanov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Navapol","family":"Kanchanaranya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sakchai","family":"Vongkittirux","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,2]]},"reference":[{"issue":"3","key":"11364_CR1","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1001\/jamaophthalmol.2013.1743","volume":"131","author":"MD Abr\u00e0moff","year":"2013","unstructured":"Abr\u00e0moff MD, Folk JC, Han DP, Walker JD, Williams DF, Russell SR (2013) Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol 131(3):351\u2013357","journal-title":"JAMA Ophthalmol"},{"key":"11364_CR2","unstructured":"ADCIS Inc (2020) Messidor-2. http:\/\/www.adcis.net\/en\/third-party\/messidor2\/. Accessed 1 Feb 2020"},{"key":"11364_CR3","unstructured":"Bastawrous A (2019) Peek vision. https:\/\/www.peekvision.org. Accessed 9 July 2019"},{"key":"11364_CR4","unstructured":"David V (2018) Volk iNview | iPhone Fundus camera. http:\/\/volk.com\/index.php\/volk-products\/ophthalmic-cameras\/volk-inview.html. Accessed 1 Aug 2018"},{"key":"11364_CR5","unstructured":"Diabetes Prevalence (2020) https:\/\/www.diabetes.co.uk\/diabetes-prevalence.html. Accessed 1 Feb 2020"},{"key":"11364_CR6","unstructured":"Diabetic Retinopathy-IAPB (2020) https:\/\/www.iapb.org\/knowledge\/what-is-avoidable-blindness\/diabetic-retinopathy\/. Accessed 1 Feb 2020"},{"issue":"2013","key":"11364_CR7","first-page":"1389","volume":"9","author":"NK El Abbadi","year":"2013","unstructured":"El Abbadi NK, Al Saadi EH (2013) Blood vessels extraction using mathematical morphology. JCS 9(2013):1389\u20131395","journal-title":"JCS"},{"key":"11364_CR8","unstructured":"Eyenuk Inc (2020) EyeArt AI eye screening system. https:\/\/www.eyenuk.com\/en\/products\/eyeart\/. Accessed 1 Feb 2020"},{"key":"11364_CR9","doi-asserted-by":"publisher","first-page":"e27524","DOI":"10.1371\/journal.pone.0027524","volume":"6","author":"K Goatman","year":"2011","unstructured":"Goatman K, Charnley A, Webster L, Nussey S (2011) Assessment of automated disease detection in diabetic retinopathy screening using two-field photography. PLoS One 6:e27524","journal-title":"PLoS ONE"},{"issue":"22","key":"11364_CR10","first-page":"2402","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA Ophthalmol 316(22):2402\u20132410","journal-title":"JAMA Ophthalmol"},{"key":"11364_CR11","doi-asserted-by":"crossref","unstructured":"Kalpiyapan V,\u00a0Aimmanee P, Makhanov S, Wongsakittirak S, Karnchanaran N (2018) An automatic system to detect exudates in mobile-phone fundus images for DR pre-screening. In: Proceedings of the 13th international conference on knowledge, information and creativity support systems (KICSS 2018), 15\u201317 November 2018, Pattaya City, pp 287\u2013292","DOI":"10.1109\/KICSS45055.2018.8950581"},{"key":"11364_CR12","doi-asserted-by":"crossref","unstructured":"Khaing T, Aimmanee P (2017) Optic disk segmentation in retinal images using active contour model based on extended feature projection. In: Proceedings of the 8th international conference of information and communication technology for embedded systems (IC-ICTES)","DOI":"10.1109\/ICTEmSys.2017.7958764"},{"key":"11364_CR13","doi-asserted-by":"publisher","first-page":"e192923","DOI":"10.1001\/jamaophthalmol.2019.2923","volume":"137","author":"S Natarajan","year":"2019","unstructured":"Natarajan S, Jain A, Krishnan R, Rogye A, Sivaprasad S (2019) Diagnostic accuracy of community-based diabetic retinopathy screening with an offline artificial intelligence system on a smartphone. JAMA Ophthalmol 137:e192923. https:\/\/doi.org\/10.1001\/jamaophthalmol.2019.2923","journal-title":"JAMA Ophthalmol"},{"key":"11364_CR14","unstructured":"Optomed Inc (2020) Optomed\u2019s Handheld Fundus Camera Products. https:\/\/www.optomed.com\/products-and-solutions\/fundus-cameras\/. Accessed 1 Feb 2020"},{"key":"11364_CR15","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1038\/s41433-018-0064-9","volume":"32","author":"R Rajalakshmi","year":"2018","unstructured":"Rajalakshmi R, Subashini R, Anjana R, Mohan V (2018) Automated diabetic retinopathy detection in smart phone-based fundus photography using artificial intelligence. Eye 32:1138\u20131144. https:\/\/doi.org\/10.1038\/s41433-018-0064-9","journal-title":"Eye"},{"key":"11364_CR16","doi-asserted-by":"publisher","first-page":"641","DOI":"10.2147\/OPTH.S195617","volume":"13","author":"T Ratanapakorn","year":"2019","unstructured":"Ratanapakorn T, Daengphoonphol A, Eua-Anant N, Yospaiboon Y (2019) Digital image processing software for diagnosing diabetic retinopathy from fundus photograph. Clin Ophthalmol 13:641\u2013648","journal-title":"Clin Ophthalmol"},{"key":"11364_CR17","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1136\/bjophthalmol-2019-314336","volume":"104","author":"S Resnikoff","year":"2020","unstructured":"Resnikoff S, Lansingh VC, Washburn L (2020) Estimated number of ophthalmologists worldwide (International Council of Ophthalmology update): will we meet the needs? Br J Ophthalmol 104:588\u2013592","journal-title":"Br J Ophthalmol"},{"key":"11364_CR18","unstructured":"Retmarker Inc (2020) Retmarker diabetic retinopathy screening. http:\/\/www.retmarker.com\/docs\/Retmarker-Screening.pdf. Accessed 1 Feb 2020"},{"key":"11364_CR19","unstructured":"Remidio Inc (2020) NM Fundus on Phone. https:\/\/www.remidio.us\/index.php. Accessed 1 Feb 2020"},{"key":"11364_CR20","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1159\/000368426","volume":"233","author":"L Ribeiro","year":"2014","unstructured":"Ribeiro L, Oliveira CM, Neves C, Ramos JD, Ferreira H, Cunha-Vaz J (2014) Screening for diabetic retinopathy in the central region of Portugal. Added value of automated disease\/no disease grading. Ophthalmologica 233:96\u2013103. https:\/\/doi.org\/10.1159\/000368426","journal-title":"Ophthalmologica"},{"key":"11364_CR21","doi-asserted-by":"crossref","unstructured":"Ruennark T, Aimmanee P (2019) Alternative deflation\u2013inflation gradient vector flow snakes for prescreening glaucoma in mobile phone retinal images. In: Proceedings of the 23rd international computer science and engineering conference (ICSEC)","DOI":"10.1109\/ICSEC47112.2019.8974840"},{"key":"11364_CR22","volume-title":"Ophthalmic photography: retinal photography, angiography, and electronic imaging","author":"PJ Saine","year":"2001","unstructured":"Saine PJ, Tyler ME (2001) Ophthalmic photography: retinal photography, angiography, and electronic imaging. Butterworth-Heinemann Medical, Oxford"},{"key":"11364_CR23","unstructured":"Scarpa A (2018) D-eye, Retinal screening system for smartphones. https:\/\/www.d-eyecare.com. 2014, Accessed 1 Aug 2018"},{"key":"11364_CR24","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1016\/j.procs.2016.07.237","volume":"93","author":"PN Sharath Kumar","year":"2016","unstructured":"Sharath Kumar PN, Deepak RU, Sathar A, Sahasranamam V, Rajesh Kumar R (2016) Automated detection system for diabetic retinopathy using two field fundus photography. Procedia Comput Sci 93:486\u2013494. https:\/\/doi.org\/10.1016\/j.procs.2016.07.237","journal-title":"Procedia Comput Sci"},{"issue":"7","key":"11364_CR25","first-page":"2331130","volume":"56","author":"K Solanki","year":"2015","unstructured":"Solanki K, Ramachandra C, Bhat S, Bhaskaranand M, Nittala MG, Sadda SR (2015) EyeArt automated, high-throughput, image analysis for diabetic retinopathy screening. Investig Ophthalmol Vis Sci 56(7):2331130","journal-title":"Investig Ophthalmol Vis Sci"},{"key":"11364_CR26","doi-asserted-by":"publisher","first-page":"e52","DOI":"10.1111\/aos.12481","volume":"93","author":"E Soto-Pedre","year":"2015","unstructured":"Soto-Pedre E, Navea A, Millan S, Hernaez-Ortega MC, Morales J, Desco MC, Perez P (2015) Evaluation of automated image analysis software for the detection of diabetic retinopathy to reduce the ophthalmologists\u2019 workload. Acta Ophthalmol 93:e52\u2013e56","journal-title":"Acta Ophthalmol"},{"key":"11364_CR27","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.ophtha.2016.11.014","volume":"124","author":"A Tufail","year":"2016","unstructured":"Tufail A, Rudisill C, Egan C, Kapetanakis V, Salas-Vega S, Owen C, Lee A, Louw V, Anderson J, Liew G, Bolter L, Srinivas S, Nittala M, Sadda S, Taylor P, Rudnicka A (2016) Automated diabetic retinopathy image assessment software. Ophthalmology 124:343\u2013351. https:\/\/doi.org\/10.1016\/j.ophtha.2016.11.014","journal-title":"Ophthalmology"},{"issue":"22","key":"11364_CR28","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.1001\/jama.2017.18152","volume":"318","author":"DSW Ting","year":"2017","unstructured":"Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, Hamzah H (2017) Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318(22):2211\u20132223. https:\/\/doi.org\/10.1001\/jama.2017.18152","journal-title":"JAMA"},{"key":"11364_CR29","unstructured":"Volk Optical Inc (2020) Volk Optical Pictor Plus-Fundus Camera. https:\/\/www.volk.com\/products\/pictor-plus-fundus-camera. Accessed 1 Feb 2020"},{"issue":"2","key":"11364_CR30","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1001\/jamaophthalmol.2015.5083","volume":"134","author":"OB Walton","year":"2016","unstructured":"Walton OB, Garoon RB, Weng CY, Gross J, Young AK, Camero KA, Jin H, Carvounis PE, Coffee R, Chu YI (2016) Evaluation of automated teleretinal screening program for diabetic retinopathy. JAMA Ophthalmol 134(2):204\u2013209. https:\/\/doi.org\/10.1001\/jamaophthalmol.2015.5083","journal-title":"JAMA Ophthalmol"},{"issue":"1","key":"11364_CR31","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1186\/s12886-019-1196-9","volume":"19","author":"Y Xu","year":"2019","unstructured":"Xu Y, Wang Y, Liu B, Tang L, Lv L, Ke X, Ling S, Lu L, Zou H (2019) The diagnostic accuracy of an intelligent and automated fundus disease image assessment system with lesion quantitative function (SmartEye) in diabetic patients. BMC Ophthalmol 19(1):184. https:\/\/doi.org\/10.1186\/s12886-019-1196-9","journal-title":"BMC Ophthalmol"},{"key":"11364_CR32","unstructured":"ZEISS Inc (2020) ZEISS VISUSCOUT 100 Handheld Fundus Camera. https:\/\/www.zeiss.com\/meditec\/int\/product-portfolio\/retinal-cameras\/visuscout-100-handheld-fundus-camera. Accessed 1 Feb 2020"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11364-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-11364-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11364-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T20:40:23Z","timestamp":1642711223000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-11364-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,2]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["11364"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-11364-3","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,2]]},"assertion":[{"value":"30 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}