{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T13:35:19Z","timestamp":1789738519951,"version":"4.0.1"},"reference-count":45,"publisher":"Elsevier BV","issue":"2","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T00:00:00Z","timestamp":1642982400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["K12EY027720"],"award-info":[{"award-number":["K12EY027720"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P30EY10572"],"award-info":[{"award-number":["P30EY10572"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U10EY015114"],"award-info":[{"award-number":["U10EY015114"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U10EY018573"],"award-info":[{"award-number":["U10EY018573"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100001818","name":"Research to Prevent Blindness","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001818","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ophthalmologyscience.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Ophthalmology Science"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1016\/j.xops.2022.100119","type":"journal-article","created":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T19:37:15Z","timestamp":1643485035000},"page":"100119","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":57,"title":["Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks"],"prefix":"10.1016","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9859-0317","authenticated-orcid":false,"given":"Travis K.","family":"Redd","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N. Venkatesh","family":"Prajna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muthiah","family":"Srinivasan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5379-7015","authenticated-orcid":false,"given":"Prajna","family":"Lalitha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiru","family":"Krishnan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Revathi","family":"Rajaraman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anitha","family":"Venugopal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nisha","family":"Acharya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerami D.","family":"Seitzman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas M.","family":"Lietman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7118-1457","authenticated-orcid":false,"given":"Jeremy D.","family":"Keenan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7964-9475","authenticated-orcid":false,"given":"J. Peter","family":"Campbell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xubo","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"12","key":"10.1016\/j.xops.2022.100119_bib1","doi-asserted-by":"crossref","first-page":"e1221","DOI":"10.1016\/S2214-109X(17)30393-5","article-title":"Global causes of blindness and distance vision impairment 1990\u20132020: a systematic review and meta-analysis","volume":"5","author":"Flaxman","year":"2017","journal-title":"Lancet Global Health"},{"key":"10.1016\/j.xops.2022.100119_bib2","first-page":"214","article-title":"Corneal blindness: a global perspective","volume":"79","author":"Whitcher","year":"2001","journal-title":"Bull World Health Organ"},{"issue":"5","key":"10.1016\/j.xops.2022.100119_bib3","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1136\/bjophthalmol-2011-300539","article-title":"Global estimates of visual impairment: 2010","volume":"96","author":"Pascolini","year":"2012","journal-title":"Br J Ophthalmol"},{"issue":"4","key":"10.1016\/j.xops.2022.100119_bib4","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1097\/00003226-200405000-00009","article-title":"Trends in the etiology of infectious corneal ulcers at the F. I. Proctor Foundation","volume":"23","author":"Varaprasathan","year":"2004","journal-title":"Cornea"},{"issue":"11","key":"10.1016\/j.xops.2022.100119_bib5","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1136\/bjo.81.11.965","article-title":"Epidemiology and aetiological diagnosis of corneal ulceration in Madurai, south India","volume":"81","author":"Srinivasan","year":"1997","journal-title":"Br J Ophthalmol"},{"issue":"2","key":"10.1016\/j.xops.2022.100119_bib6","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1080\/09286580601001347","article-title":"Microbial keratitis in South India: influence of risk factors, climate, and geographical variation","volume":"14","author":"Bharathi","year":"2007","journal-title":"Ophthalmic Epidemiol"},{"key":"10.1016\/j.xops.2022.100119_bib7","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/S0161-6420(96)30738-0","article-title":"The role of smears, cultures, and antibiotic sensitivity testing in the management of suspected infectious keratitis","volume":"103","author":"Mcleod","year":"1996","journal-title":"Ophthalmology"},{"issue":"4","key":"10.1016\/j.xops.2022.100119_bib8","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1167\/iovs.11-8478","article-title":"The clinical differentiation of bacterial and fungal keratitis: a photographic survey","volume":"53","author":"Dalmon","year":"2012","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"6","key":"10.1016\/j.xops.2022.100119_bib9","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1016\/j.ajo.2007.02.030","article-title":"The clinical diagnosis of microbial keratitis","volume":"143","author":"Dahlgren","year":"2007","journal-title":"Am J Ophthalmol"},{"issue":"2","key":"10.1016\/j.xops.2022.100119_bib10","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.ophtha.2021.09.019","article-title":"Expert performance in visual differentiation of bacterial and fungal keratitis","volume":"129","author":"Redd","year":"2022","journal-title":"Ophthalmology"},{"issue":"7553","key":"10.1016\/j.xops.2022.100119_bib11","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"issue":"10","key":"10.1016\/j.xops.2022.100119_bib12","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A\u00a0survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"9","key":"10.1016\/j.xops.2022.100119_bib13","doi-asserted-by":"crossref","first-page":"1974","DOI":"10.1016\/j.ophtha.2016.05.029","article-title":"Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier","volume":"123","author":"Asaoka","year":"2016","journal-title":"Ophthalmology"},{"issue":"22","key":"10.1016\/j.xops.2022.100119_bib14","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.xops.2022.100119_bib15","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.oret.2016.12.009","article-title":"Deep learning is effective for classifying normal versus age-related macular degeneration OCT images","volume":"1","author":"Lee","year":"2017","journal-title":"Ophthalmol Retina"},{"key":"10.1016\/j.xops.2022.100119_bib16","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.xops.2022.100119_bib17","doi-asserted-by":"crossref","first-page":"14424","DOI":"10.1038\/s41598-020-71425-9","article-title":"A\u00a0deep learning approach in diagnosing fungal keratitis based on corneal photographs","volume":"10","author":"Kuo","year":"2020","journal-title":"Sci Rep"},{"key":"10.1016\/j.xops.2022.100119_bib18","doi-asserted-by":"crossref","unstructured":"Ghosh AK, Thammasudjarit R, Jongkhajornpong P, et\u00a0al. Deep learning for discrimination between fungal keratitis and bacterial keratitis: DeepKeratitis. Cornea. 2021 Sep 29. doi: 10.1097\/ICO.0000000000002830. Online ahead of print.","DOI":"10.1097\/ICO.0000000000002830"},{"issue":"7","key":"10.1016\/j.xops.2022.100119_bib19","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.3390\/diagnostics11071246","article-title":"Using slit-lamp images for deep learning-based identification of bacterial and fungal keratitis: model development and validation with different convolutional neural networks","volume":"11","author":"Hung","year":"2021","journal-title":"Diagnostics"},{"issue":"6","key":"10.1016\/j.xops.2022.100119_bib20","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0252653","article-title":"A\u00a0deep transfer learning framework for the automated assessment of corneal inflammation on in\u00a0vivo confocal microscopy images","volume":"16","author":"Xu","year":"2021","journal-title":"PloS One"},{"issue":"2","key":"10.1016\/j.xops.2022.100119_bib21","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1001\/archophthalmol.2011.315","article-title":"Corticosteroids for bacterial keratitis: the Steroids for Corneal Ulcers Trial (SCUT)","volume":"130","author":"Srinivasan","year":"2012","journal-title":"Arch Ophthalmol"},{"issue":"12","key":"10.1016\/j.xops.2022.100119_bib22","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1001\/jamaophthalmol.2016.4096","article-title":"Effect of oral voriconazole on fungal keratitis in the Mycotic Ulcer Treatment Trial II (MUTT II): a randomized clinical trial","volume":"134","author":"Prajna","year":"2016","journal-title":"JAMA Ophthalmol"},{"issue":"4","key":"10.1016\/j.xops.2022.100119_bib23","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1001\/jamaophthalmol.2013.1497","article-title":"The Mycotic Ulcer Treatment Trial","volume":"131","author":"Prajna","year":"2013","journal-title":"JAMA Ophthalmol"},{"issue":"4","key":"10.1016\/j.xops.2022.100119_bib24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2382577.2382579","article-title":"Leakage in data mining","volume":"6","author":"Kaufman","year":"2012","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"1","key":"10.1016\/j.xops.2022.100119_bib25","first-page":"1","article-title":"A\u00a0survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J\u00a0Big Data"},{"key":"10.1016\/j.xops.2022.100119_bib26","first-page":"4510","article-title":"MobileNetV2: inverted residuals and linear bottlenecks","author":"Sandler","year":"2018","journal-title":"Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit"},{"key":"10.1016\/j.xops.2022.100119_bib27","series-title":"Densely connected convolutional networks. 2017","first-page":"2261","author":"Huang","year":"2017"},{"key":"10.1016\/j.xops.2022.100119_bib28","first-page":"770","article-title":"Deep residual learning for image recognition. 2016","author":"He","year":"2016","journal-title":"IEEE Conf Comput Vis Pattern Recognit"},{"key":"10.1016\/j.xops.2022.100119_bib29","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations (ICLR). 2015:1\u201314."},{"key":"10.1016\/j.xops.2022.100119_bib30","doi-asserted-by":"crossref","unstructured":"Chollet F. Xception: deep learning with depthwise separable convolutions. 2017 IEEE Conf Comput Vis Pattern Recognit. 2017:1800\u20131807.","DOI":"10.1109\/CVPR.2017.195"},{"key":"10.1016\/j.xops.2022.100119_bib31","unstructured":"ImageNet. ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Available at: https:\/\/image-net.org\/challenges\/LSVRC\/. Accessed 21.06.21."},{"key":"10.1016\/j.xops.2022.100119_bib32","unstructured":"Abadi M, Agarwal A, Barham P, et al. TensorFlow: large-scale machine learning on heterogenous systems. arXiv. 2016:1603.04467.1-13."},{"key":"10.1016\/j.xops.2022.100119_bib33","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/1471-2105-12-77","article-title":"pROC: an open-source package for R and S+ to analyze and compare ROC curves","volume":"12","author":"Robin","year":"2011","journal-title":"BMC Bioinformatics"},{"issue":"3","key":"10.1016\/j.xops.2022.100119_bib34","doi-asserted-by":"crossref","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"key":"10.1016\/j.xops.2022.100119_bib35","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","article-title":"Index for rating diagnostic tests","volume":"3","author":"Youden","year":"1950","journal-title":"Cancer"},{"issue":"2","key":"10.1016\/j.xops.2022.100119_bib36","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2016","journal-title":"Int J Comput Vis"},{"issue":"7","key":"10.1016\/j.xops.2022.100119_bib37","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1016\/j.eng.2020.04.012","article-title":"Deep sequential feature learning in clinical image classification of infectious keratitis","volume":"7","author":"Xu","year":"2021","journal-title":"Engineering"},{"issue":"12","key":"10.1016\/j.xops.2022.100119_bib38","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1136\/bjo.2005.076315","article-title":"Characteristic clinical features as an aid to the diagnosis of suppurative keratitis caused by filamentous fungi","volume":"89","author":"Thomas","year":"2005","journal-title":"Br J Ophthalmol"},{"issue":"89","key":"10.1016\/j.xops.2022.100119_bib39","first-page":"6","article-title":"Distinguishing fungal and bacterial keratitis on clinical signs","volume":"28","author":"Leck","year":"2015","journal-title":"Community Eye Health"},{"issue":"4","key":"10.1016\/j.xops.2022.100119_bib40","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1097\/00055735-200408000-00008","article-title":"Fungal keratitis","volume":"15","author":"Srinivasan","year":"2004","journal-title":"Curr Opin Ophthalmol"},{"issue":"2","key":"10.1016\/j.xops.2022.100119_bib41","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1076\/ceyr.27.2.111.15949","article-title":"Neural network approach to classify infective keratitis","volume":"27","author":"Saini","year":"2003","journal-title":"Curr Eye Res"},{"issue":"8","key":"10.1016\/j.xops.2022.100119_bib42","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1001\/archophthalmol.2010.144","article-title":"Epidemiology of ulcerative keratitis in Northern California","volume":"128","author":"Jeng","year":"2010","journal-title":"Arch Ophthalmol"},{"issue":"12","key":"10.1016\/j.xops.2022.100119_bib43","doi-asserted-by":"crossref","first-page":"854","DOI":"10.2471\/BLT.19.232660","article-title":"Infectious corneal ulceration: a proposal for neglected tropical disease status","volume":"97","author":"Ung","year":"2019","journal-title":"Bull World Health Org"},{"key":"10.1016\/j.xops.2022.100119_bib44","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1038\/s42256-020-0186-1","article-title":"Secure, privacy-preserving and federated machine learning in medical imaging","volume":"2","author":"Kaissis","year":"2020","journal-title":"Nature Machine Intelligence"},{"key":"10.1016\/j.xops.2022.100119_bib45","author":"Miranda"}],"container-title":["Ophthalmology Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666914522000082?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666914522000082?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T06:23:31Z","timestamp":1763015011000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666914522000082"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6]]},"references-count":45,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["S2666914522000082"],"URL":"https:\/\/doi.org\/10.1016\/j.xops.2022.100119","relation":{},"ISSN":["2666-9145"],"issn-type":[{"value":"2666-9145","type":"print"}],"subject":[],"published":{"date-parts":[[2022,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks","name":"articletitle","label":"Article Title"},{"value":"Ophthalmology Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.xops.2022.100119","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 by the American Academy of Ophthalmology.","name":"copyright","label":"Copyright"}],"article-number":"100119"}}