{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T14:16:36Z","timestamp":1793888196466,"version":"4.8.0"},"reference-count":40,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2022,3,27]],"date-time":"2022-03-27T00:00:00Z","timestamp":1648339200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100005809","name":"Hyderabad Eye Research Foundation","doi-asserted-by":"publisher","award":["LEC BHR-R-12-20-565"],"award-info":[{"award-number":["LEC BHR-R-12-20-565"]}],"id":[{"id":"10.13039\/501100005809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Ophthalmology"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To investigate a method to identification of early progression of keratoconus using deep learning neural networks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Retrospective evaluation of medical records of patients with progressive keratoconus and had more than one followup visits. Images extracted from the single scheimplug analyzer for analysis were captured during the patient visits. The baseline progression of keratoconus is detected by a change in flat or steep K of \u22651.0D which is labeled as keratometric progression (KP) and progression detected by image based deep learning convolutional neural network (CNN) models, is labeled as latent progression (LP). Patient data consisted of model data (385 eyes of 351patients) to train and test the learning models and prediction data (1331 eyes of 828 patients) to determine the LP based on the learning models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The LP prediction model was able to identify progression at a mean of 11.1 months earlier than KP (p\u2009&lt;\u20090.001). LP prediction model was able to identify progression earlier than KP irrespective of age category, gender, the severity of keratoconus, presenting visual acuity, astigmatism, and spherical equivalent (P\u2009&lt;\u20090.001). When compared to the first visit the corrected distance visual acuity was more stable in 71% of the eyes at LP prediction visit compared to 50% at KP visit (p\u2009&lt;\u20090.001).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Through this study, we propose a possible solution to address the shortcomings noted in the current approaches of detecting progression relying only on KP. Avoiding bias towards feature selection from tomography images as done in the current study aids in identifying very subtle changes on the images between visits.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/11206721221087566","type":"journal-article","created":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T02:52:32Z","timestamp":1648435952000},"page":"2554-2564","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":19,"title":["KEDOP: Keratoconus early detection of progression using tomography images"],"prefix":"10.1177","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9836-0597","authenticated-orcid":false,"given":"Jagadesh C","family":"Reddy","sequence":"first","affiliation":[{"name":"Cataract &amp; Refractive Service, The Cornea Institute, L V Prasad Eye Institute, Hyderabad, India"},{"name":"Pristine Eye Hospitals, HUDA Enclave, Gachibowli, Hyderabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1031-7912","authenticated-orcid":false,"given":"Panini","family":"Bhamidipati","sequence":"additional","affiliation":[{"name":"AI&amp;MLTechsophy Inc, Gutenberg IT Park, Kondapur, Hyderabad, India"},{"name":"Department of Engineering Design, Indian Institute of Technology, Chennai, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shivam","family":"Dwivedi","sequence":"additional","affiliation":[{"name":"AI&amp;MLTechsophy Inc, Gutenberg IT Park, Kondapur, Hyderabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishna Kishore","family":"Dhara","sequence":"additional","affiliation":[{"name":"AI&amp;MLTechsophy Inc, Gutenberg IT Park, Kondapur, Hyderabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vineet","family":"Joshi","sequence":"additional","affiliation":[{"name":"Cataract &amp; Refractive Service, The Cornea Institute, L V Prasad Eye Institute, Hyderabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad.","family":"Hasnat Ali","sequence":"additional","affiliation":[{"name":"Clinical Epidemiology and Bio-Statistics, L V Prasad Eye Institute, Hyderabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pravin K","family":"Vaddavalli","sequence":"additional","affiliation":[{"name":"Cataract &amp; Refractive Service, The Cornea Institute, L V Prasad Eye Institute, Hyderabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2022,3,27]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1097\/ICO.0000000000002150"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1097\/ICL.0000000000000085"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2019.02.029"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2013.10.028"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1097\/ICO.0000000000000460"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.3928\/1081597X-20201021-01"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1177\/1120672119887874"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.3928\/1081597X-20090813-09"},{"key":"e_1_3_3_10_2","article-title":"Corneal cross-linking versus conventional management for keratoconus: a lifetime economic model","volume":"1","author":"Lindstrom RL","unstructured":"Lindstrom RL, Berdahl JP, Donnenfeld ED, et al. Corneal cross-linking versus conventional management for keratoconus: a lifetime economic model. J Med Econ 2020 Nov 19; 1.","journal-title":"J Med Econ"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1097\/ICO.0000000000000408"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1136\/bjo.2010.196493"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1097\/ICO.0b013e31825e8414"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1136\/bjophthalmol-2012-302556"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcrs.2011.08.026"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1097\/j.jcrs.0000000000000475"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajo.2020.06.005"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1167\/tvst.9.2.30"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1167\/tvst.9.2.24"},{"key":"e_1_3_3_20_2","doi-asserted-by":"crossref","first-page":"e031313","DOI":"10.1136\/bmjopen-2019-031313","article-title":"Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study","volume":"27","author":"Kamiya K","year":"2019","unstructured":"Kamiya K, Ayatsuka Y, Kato Y, et al. Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study. BMJ Open 2019; 27: e031313.","journal-title":"BMJ Open"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcrs.2013.08.061"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_3_3_23_2","volume-title":"The handbook of brain theory and neural networks","author":"Lecun Y","year":"1995","unstructured":"Lecun Y, Bengio Y. Convolutional networks for images, speech, and time-series. In: Arbib MA (ed.) The handbook of brain theory and neural networks. Boston, MA: MIT Press, 1995."},{"key":"e_1_3_3_24_2","first-page":"1097","volume-title":"ImageNet Classification with deep convolutional neural networksProceedings of 25th International Conference on Neural Information Processing Systems- Volume 1","author":"Krizhevsky Alex","year":"2012","unstructured":"Krizhevsky Alex, Sutskever Ilya, Geoffrey E., Hilton. ImageNet Classification with deep convolutional neural networks. In: Proceedings of 25th International Conference on Neural Information Processing Systems- Volume 1. Curan Associates Inc., Red Hook, NY, USA, 2012: 1097\u20131105."},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"e_1_3_3_26_2","unstructured":"Kingma DP Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. 2014."},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2018.11.015"},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ogla.2019.03.008"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8162567"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.3928\/1081597X-20140401-01"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcrs.2013.05.022"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1097\/ICO.0b013e31826672e2"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1167\/iovs.11-8118"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1097\/ICL.0000000000000582"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.3928\/1081597X-20121011-01"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaapos.2016.08.006"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1097\/ICL.0000000000000432"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2013.01.012"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clae.2007.03.001"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clae.2008.07.001"},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1167\/iovs.10-6856"}],"container-title":["European Journal of Ophthalmology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/11206721221087566","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/11206721221087566","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/11206721221087566","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T19:46:19Z","timestamp":1777405579000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/11206721221087566"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,27]]},"references-count":40,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["10.1177\/11206721221087566"],"URL":"https:\/\/doi.org\/10.1177\/11206721221087566","relation":{},"ISSN":["1120-6721","1724-6016"],"issn-type":[{"value":"1120-6721","type":"print"},{"value":"1724-6016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,27]]}}}