Improving Glaucoma Detection Using Spatially Correspondent Clusters of Damage and by Combining Standard Automated Perimetry and Optical Coherence Tomography | Zendy

Ali S. Raza | Zendy; Xian Zhang | Zendy; Carlos Gustavo V. De Moraes | Zendy; Charles Reisman | Zendy; Jeffrey M. Liebmann | Zendy; Robert Ritch | Zendy; Donald C. Hood | Zendy

Open Access

Improving Glaucoma Detection Using Spatially Correspondent Clusters of Damage and by Combining Standard Automated Perimetry and Optical Coherence Tomography

Author(s) -

Ali S. Raza,

Xian Zhang,

Carlos Gustavo V. De Moraes,

Charles Reisman,

Jeffrey M. Liebmann,

Robert Ritch,

Donald C. Hood

Publication year - 2014

Publication title -

investigative ophthalmology and visual science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.935

H-Index - 218

eISSN - 1552-5783

pISSN - 0146-0404

DOI - 10.1167/iovs.13-12351

Subject(s) - nerve fiber layer , glaucoma , receiver operating characteristic , optical coherence tomography , metric (unit) , computer science , inner plexiform layer , retinal , pattern recognition (psychology) , mathematics , ophthalmology , artificial intelligence , medicine , machine learning , operations management , economics

To improve the detection of glaucoma, techniques for assessing local patterns of damage and for combining structure and function were developed.

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