{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T20:49:30Z","timestamp":1779914970291,"version":"3.53.1"},"reference-count":55,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100020027","name":"Dr. Rolf M. Schwiete Stiftung","doi-asserted-by":"crossref","award":["2020-024"],"award-info":[{"award-number":["2020-024"]}],"id":[{"id":"10.13039\/501100020027","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Keratoconus is the corneal disease with the highest reported incidence of 1:2000. The treatment\u2019s level of success highly depends on how early it was started. Subsequently, a fast and highly capable diagnostic tool is crucial. While there are many computer-based systems that are capable of the analysis of medical image data, they only provide parameters. These have advanced quite far, though full diagnosis does not exist. Machine learning has provided the capabilities for the parameters, and numerous similar scientific fields have developed full image diagnosis based on neural networks. The Homburg Keratoconus Center has been gathering almost 2000 patient datasets, over 1000 of them over the course of their disease. Backed by this databank, this work aims to develop a convolutional neural network to tackle diagnosis of keratoconus as the major corneal disease.<\/jats:p>","DOI":"10.1371\/journal.pone.0264219","type":"journal-article","created":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T18:36:53Z","timestamp":1645209413000},"page":"e0264219","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":8,"title":["Protocol for the diagnosis of keratoconus using convolutional neural networks"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5544-5507","authenticated-orcid":true,"given":"Jan","family":"Schatteburg","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Achim","family":"Langenbucher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"pone.0264219.ref001","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/S0039-6257(97)00119-7","article-title":"Keratoconus","volume":"42","author":"YS Rabinowitz","year":"1998","journal-title":"Survey of Ophthalmology"},{"key":"pone.0264219.ref002","volume-title":"Augenheilkunde essentials","author":"GK Lang","year":"2015"},{"key":"pone.0264219.ref003","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.ajo.2016.12.015","article-title":"Age-specific Incidence and Prevalence of Keratoconus: A Nationwide Registration Study","volume":"175","author":"DA Godefrooij","year":"2017","journal-title":"American Journal of Ophthalmology"},{"key":"pone.0264219.ref004","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/0039-6257(84)90094-8","article-title":"Keratoconus and related noninflammatory corneal thinning disorders","volume":"28","author":"JH Krachmer","year":"1984","journal-title":"Survey of Ophthalmology"},{"key":"pone.0264219.ref005","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1159\/000299577","article-title":"Le keratocone fruste au javal","volume":"96","author":"M. Amsler","year":"1938","journal-title":"Ophthalmologica"},{"key":"pone.0264219.ref006","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1159\/000300309","article-title":"Keratocone classique et keratocone fruste; arguments unitaires","volume":"111","author":"M. Amsler","year":"1946","journal-title":"Ophthalmologica"},{"key":"pone.0264219.ref007","doi-asserted-by":"crossref","first-page":"400","DOI":"10.3928\/1081-597X-19891101-10","article-title":"Computer-Assisted Corneal Topography in Keratoconus","volume":"5","author":"YS Rabinowitz","year":"1989","journal-title":"Journal of Refractive Surgery"},{"key":"pone.0264219.ref008","first-page":"2749","article-title":"Automated keratoconus screening with corneal topography analysis","author":"N Maeda","year":"1994","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"pone.0264219.ref009","first-page":"1327","article-title":"Neural Network Classification of Corneal Topography. Preliminary Demonstration","author":"N Maeda","year":"1995","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"pone.0264219.ref010","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1016\/S0886-3350(99)00195-9","article-title":"KISA% index: a quantitative videokeratography algorithm embodying minimal topographic criteria for diagnosing keratoconus. Journal of Cataract &","volume":"25","author":"YS Rabinowitz","year":"1999","journal-title":"Refractive Surgery"},{"key":"pone.0264219.ref011","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.clae.2010.04.006","article-title":"Keratoconus: a review","volume":"33","author":"M Romero-Jim\u00e9nez","year":"2010","journal-title":"Cont Lens Anterior Eye"},{"key":"pone.0264219.ref012","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1038\/nm0995-970","article-title":"Optical biopsy and imaging using optical coherence tomography","volume":"1","author":"JG Fujimoto","year":"1995","journal-title":"Nature medicine"},{"key":"pone.0264219.ref013","doi-asserted-by":"crossref","first-page":"e258","DOI":"10.1111\/aos.12056","article-title":"Biomechanical diagnosis of keratoconus: evaluation of the keratoconus match index and the keratoconus match probability","volume":"91","author":"G Labiris","year":"2013","journal-title":"Acta Ophthalmol"},{"key":"pone.0264219.ref014","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1016\/j.ajo.2015.01.014","article-title":"Staging of keratoconus indices regarding tomography, topography, and biomechanical measurements","volume":"159","author":"S Goebels","year":"2015","journal-title":"American Journal of Ophthalmology"},{"key":"pone.0264219.ref015","doi-asserted-by":"crossref","unstructured":"Fukushima K, Miyake S. Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition. In: Amari S, Arbib MA, editors. Competition and cooperation in neural nets. Proceedings of the U.S.-Japan Joint Seminar held at Kyoto, Japan, Febr. 15\u201319, 1982. Ed. by S[h\u014dgo] Amari and M. A. Arbib. Berlin, Heidelberg usw.: Springer; 1982. pp. 267\u201385.","DOI":"10.1007\/978-3-642-46466-9_18"},{"key":"pone.0264219.ref016","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: an overview","volume":"61","author":"J. Schmidhuber","year":"2015","journal-title":"Neural Netw"},{"key":"pone.0264219.ref017","author":"B Huval","year":"2015","journal-title":"An Empirical Evaluation of Deep Learning on Highway Driving."},{"key":"pone.0264219.ref018","doi-asserted-by":"crossref","unstructured":"Deng L, Hinton G, Kingsbury B. New types of deep neural network learning for speech recognition and related applications: an overview. ICASSP 2013\u20132013 IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver, British Columbia, Canada, 26\u201331 May 2013; [proceedings. Piscataway, NJ: IEEE; 2013.","DOI":"10.1109\/ICASSP.2013.6639344"},{"key":"pone.0264219.ref019","volume-title":"Technologien und Best Practices","author":"P. Gentsch","year":"2017"},{"key":"pone.0264219.ref020","doi-asserted-by":"crossref","unstructured":"Hazelwood K, Bird S, Brooks D, Chintala S, Diril U, Dzhulgakov D, et al. Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective. 24th IEEE International Symposium on High Performance Computer Architecture. Proceedings: 24\u201328 February 2018, Vienna, Austria. Piscataway, NJ: IEEE; 2018.","DOI":"10.1109\/HPCA.2018.00059"},{"key":"pone.0264219.ref021","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1038\/s41586-019-1724-z","article-title":"Grandmaster level in StarCraft II using multi-agent reinforcement learning","volume":"575","author":"O Vinyals","year":"2019","journal-title":"Nature"},{"key":"pone.0264219.ref022","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine Learning in Medicine","volume":"132","author":"RC Deo","year":"2015","journal-title":"Circulation"},{"key":"pone.0264219.ref023","volume-title":"Microsoft\u2019s AI chatbot, gets a crash course in racism from Twitter","author":"Hunt E. Tay","year":"2016"},{"key":"pone.0264219.ref024","unstructured":"BBC. Google apologises for Photos app\u2019s racist blunder. BBC 2015 [cited 13 Feb 2021]. Available from: https:\/\/www.bbc.com\/news\/technology-33347866."},{"key":"pone.0264219.ref025","unstructured":"Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence. 1995; 14:1137\u201345."},{"key":"pone.0264219.ref026","volume-title":"Deep learning","author":"I Goodfellow","year":"2016"},{"key":"pone.0264219.ref027","author":"M Claesen","year":"2015","journal-title":"Hyperparameter Search in Machine Learning."},{"key":"pone.0264219.ref028","volume-title":"Machine learning. A probabilistic perspective","author":"KP Murphy","year":"2012"},{"key":"pone.0264219.ref029","author":"S. Ruder","year":"2016","journal-title":"An overview of gradient descent optimization algorithms."},{"key":"pone.0264219.ref030","doi-asserted-by":"crossref","unstructured":"Rumelhart DE, Hinton GE, Williams RJ. Learning Internal Representations by Error Propagation. Cambridge, Massachusetts; 1985.","DOI":"10.21236\/ADA164453"},{"key":"pone.0264219.ref031","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"DE Rumelhart","year":"1986","journal-title":"Nature"},{"key":"pone.0264219.ref032","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1109\/5.58337","article-title":"Backpropagation through time: what it does and how to do it","volume":"78","author":"PJ Werbos","year":"1990","journal-title":"Proc IEEE"},{"key":"pone.0264219.ref033","volume-title":"A practical information-theoretic approach","author":"KP Burnham","year":"2010","edition":"2"},{"key":"pone.0264219.ref034","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s00365-006-0663-2","article-title":"On Early Stopping in Gradient Descent Learning","volume":"26","author":"Y Yao","year":"2007","journal-title":"Constr Approx"},{"key":"pone.0264219.ref035","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.2307\/4586294","article-title":"Statistical Problems in Assessing Methods of Medical Diagnosis, with Special Reference to X-Ray Techniques","volume":"62","author":"J. Yerushalmy","year":"1947","journal-title":"Public Health Reports (1896\u20131970)"},{"key":"pone.0264219.ref036","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1167\/tvst.9.2.53","article-title":"Keratoconus Screening Based on Deep Learning Approach of Corneal Topography","volume":"9","author":"B-I Kuo","year":"2020","journal-title":"Transl Vis Sci Technol"},{"key":"pone.0264219.ref037","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ajo.2020.06.005","article-title":"Corneal Topography Raw Data Classification Using a Convolutional Neural Network","volume":"219","author":"P Z\u00e9boulon","year":"2020","journal-title":"American Journal of Ophthalmology"},{"key":"pone.0264219.ref038","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1117\/12.2552478","volume-title":"Opthalmic Technologies XXX","author":"A Elsawy","year":"2020"},{"key":"pone.0264219.ref039","volume-title":"Augenklinik des UKS feiert 10 Jahre Homburger Keratokonuscenter (HKC)","author":"Uniklinikum Saarland","year":"2020"},{"key":"pone.0264219.ref040","volume-title":"Artificial intelligence","author":"PH Winston","year":"1993","edition":"3"},{"key":"pone.0264219.ref041","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/2.485891","article-title":"Artificial neural networks: a tutorial","volume":"29","author":"AK Jain","year":"1996","journal-title":"Computer"},{"key":"pone.0264219.ref042","unstructured":"Orr MJL. Introduction to radial basis function networks.; 1996."},{"key":"pone.0264219.ref043","doi-asserted-by":"crossref","DOI":"10.1561\/9781601982957","volume-title":"Learning deep architectures for AI","author":"Y. Bengio","year":"2009"},{"key":"pone.0264219.ref044","unstructured":"Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. Proceedings of the fourteenth international conference on artificial intelligence and statistics. pp. 315\u201323."},{"key":"pone.0264219.ref045","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/72.554195","article-title":"Face recognition: a convolutional neural-network approach","volume":"8","author":"S Lawrence","year":"1997","journal-title":"IEEE Trans Neural Netw"},{"key":"pone.0264219.ref046","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"A Krizhevsky","year":"2017","journal-title":"Commun ACM"},{"key":"pone.0264219.ref047","first-page":"92","article-title":"Evaluation of pooling operations in convolutional architectures for object recognition","author":"D Scherer","year":"2010","journal-title":"International conference on artificial neural networks"},{"key":"pone.0264219.ref048","author":"K Simonyan","year":"2014","journal-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition."},{"key":"pone.0264219.ref049","author":"DP Kingma","year":"2014","journal-title":"Adam: A Method for Stochastic Optimization."},{"key":"pone.0264219.ref050","volume-title":"CASIA2 Application Manual","author":"Tomey Corporation"},{"key":"pone.0264219.ref051","volume-title":"Fourier Domain OCT CASIA2","author":"Tomey Corporation"},{"key":"pone.0264219.ref052","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1097\/00003226-199101000-00002","article-title":"Corneal topography of keratoconus","volume":"10","author":"SE Wilson","year":"1991","journal-title":"Cornea"},{"key":"pone.0264219.ref053","volume-title":"Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek","author":"F. Chollet","year":"2018","edition":"1"},{"key":"pone.0264219.ref054","unstructured":"Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, et al. TensorFlow: A System for Large-Scale Machine Learning. 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16)."},{"key":"pone.0264219.ref055","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":"RR Selvaraju","year":"2020","journal-title":"Int J Comput Vis"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0264219","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T18:37:25Z","timestamp":1645209445000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0264219"}},"subtitle":[],"editor":[{"given":"Yu-Chi","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2022,2,18]]},"references-count":55,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,2,18]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0264219","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,18]]}}}