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Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning

  • Published:2018-09 Issue: Volume:193 Page:71-79
  • ISSN:0002-9394
  • Container-title:American Journal of Ophthalmology
  • language:en
  • Short-container-title:American Journal of Ophthalmology

Author:

Yousefi Siamak,Kiwaki Taichi,Zheng Yuhui,Sugiura Hiroki,Asaoka Ryo,Murata Hiroshi,Lemij Hans,Yamanishi Kenji

Funder

National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology

Ministry of Education, Culture, Sports, Science and Technology

Japan Science and Technology Agency

Publisher

Elsevier BV

Subject

Ophthalmology

Reference27 articles.

1. Structure and function evaluation (SAFE): I. criteria for glaucomatous visual field loss using standard automated perimetry (SAP) and short wavelength automated perimetry (SWAP);Johnson;Am J Ophthalmol,2002

2. Detecting functional changes in the patient’s vision: visual field analysis;Johnson,2014

3. Detectability of glaucomatous changes using SAP, FDT, flicker perimetry, and OCT;Nomoto;J Glaucoma,2009

4. High spatial resolution automated perimetry in glaucoma;Westcott;Br J Ophthalmol,1997

5. Automated Static Perimetry;Anderson,1999

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