Table of Contents Author Guidelines Submit a Manuscript
Journal of Ophthalmology
Volume 2013, Article ID 789129, 7 pages
http://dx.doi.org/10.1155/2013/789129
Clinical Study

Glaucoma Diagnostic Accuracy of Machine Learning Classifiers Using Retinal Nerve Fiber Layer and Optic Nerve Data from SD-OCT

1Faculty of Medical Sciences, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil
2Department of Engineering, University of São Paulo (USP), São Paulo, SP, Brazil

Received 29 August 2013; Accepted 13 October 2013

Academic Editor: David A. Wilkie

Copyright © 2013 Kleyton Arlindo Barella et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [10 citations]

The following is the list of published articles that have cited the current article.

  • Simona Vlad, Sorina Demea, Horea Demea, and Rodica Holonec, “Neural network classifier for glaucoma diagnosis,” 2015 E-Health and Bioengineering Conference (EHB), pp. 1–4, . View at Publisher · View at Google Scholar
  • Manuele Michelessi, Ersilia Lucenteforte, Francesco Oddone, Miriam Brazzelli, Mariacristina Parravano, Sara Franchi, Sueko M Ng, and Gianni VirgiliCochrane Database of Systematic Reviews, 2015. View at Publisher · View at Google Scholar
  • Heeyoon Cho, Parvathy Pillai, Laura Nicholson, and Lucia Sobrin, “Inflammatory Papillitis in Uveitis: Response to Treatment and Use of Optic Nerve Optical Coherence Tomography for Monitoring,” Ocular Immunology And Inflammation, vol. 24, no. 2, pp. 194–206, 2016. View at Publisher · View at Google Scholar
  • Heeyoon Cho, Lucia Sobrin, Parvathy Pillai, and Laura Nicholson, “Inflammatory Papillitis in Uveitis: Response to Treatment and Use of Optic Nerve Optical Coherence Tomography for Monitoring,” Ocular Immunology and Inflammation, vol. 24, no. 2, pp. 194–206, 2016. View at Publisher · View at Google Scholar
  • Neda Baniasadi, Eleftherios I. Paschalis, Tobias Elze, Mufeed Mahd, Mahdi Haghzadeh, Pallavi Ojha, and Teresa C. Chen, “Patterns of retinal nerve fiber layer loss in different subtypes of open angle glaucoma using spectral domain optical coherence tomography,” Journal of Glaucoma, vol. 25, no. 10, pp. 865–872, 2016. View at Publisher · View at Google Scholar
  • Pradeep M. Patil, and Medha V. Wyawahare, “Machine learning classifiers based on structural ONH measurements for glaucoma diagnosis,” International Journal of Biomedical Engineering and Technology, vol. 21, no. 4, pp. 343–360, 2016. View at Publisher · View at Google Scholar
  • Asaf Achiron, Zvi Gur, Uri Aviv, Assaf Hilely, Michael Mimouni, Lily Karmona, Lior Rokach, and Igor Kaiserman, “Predicting Refractive Surgery Outcome: Machine Learning Approach With Big Data,” Journal of Refractive Surgery, vol. 33, no. 9, pp. 592–597, 2017. View at Publisher · View at Google Scholar
  • Seong Jae Kim, Kyong Jin Cho, and Sejong Oh, “Development of machine learning models for diagnosis of glaucoma,” Plos One, vol. 12, no. 5, pp. e0177726, 2017. View at Publisher · View at Google Scholar
  • Hassan Muhammad, Thomas J. Fuchs, Nicole De Cuir, Carlos G. De Moraes, Dana M. Blumberg, Jeffrey M. Liebmann, Robert Ritch, and Donald C. Hood, “Hybrid Deep Learning on Single Wide-field Optical Coherence Tomography Scans Accurately Classifies Glaucoma Suspects,” Journal of Glaucoma, pp. 1, 2017. View at Publisher · View at Google Scholar
  • Donald L. Budenz, Joshua L. Warren, Michael Wall, Thomas M. Callan, John G. Flanagan, Gary Lee, Jean-Claude Mwanza, and Paul H. Artes, “Validation of the UNC OCT index for the diagnosis of early glaucoma,” Translational Vision Science and Technology, vol. 7, no. 2, 2018. View at Publisher · View at Google Scholar