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dc.contributor.authorYoshioka, N
dc.contributor.authorZangerl, B
dc.contributor.authorNivison-Smith, L
dc.contributor.authorKhuu, SK
dc.contributor.authorJones, BW
dc.contributor.authorPfeiffer, RL
dc.contributor.authorMarc, RE
dc.contributor.authorKalloniatis, M
dc.date.accessioned2020-12-21T01:52:54Z
dc.date.available2020-12-21T01:52:54Z
dc.date.issued2017-07-01
dc.identifierpii: 2634369
dc.identifier.citationYoshioka, N., Zangerl, B., Nivison-Smith, L., Khuu, S. K., Jones, B. W., Pfeiffer, R. L., Marc, R. E. & Kalloniatis, M. (2017). Pattern Recognition Analysis of Age-Related Retinal Ganglion Cell Signatures in the Human Eye. INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 58 (7), pp.3086-3099. https://doi.org/10.1167/iovs.17-21450.
dc.identifier.issn0146-0404
dc.identifier.urihttp://hdl.handle.net/11343/256698
dc.description.abstractPurpose: To characterize macular ganglion cell layer (GCL) changes with age and provide a framework to assess changes in ocular disease. This study used data clustering to analyze macular GCL patterns from optical coherence tomography (OCT) in a large cohort of subjects without ocular disease. Methods: Single eyes of 201 patients evaluated at the Centre for Eye Health (Sydney, Australia) were retrospectively enrolled (age range, 20-85); 8 × 8 grid locations obtained from Spectralis OCT macular scans were analyzed with unsupervised classification into statistically separable classes sharing common GCL thickness and change with age. The resulting classes and gridwise data were fitted with linear and segmented linear regression curves. Additionally, normalized data were analyzed to determine regression as a percentage. Accuracy of each model was examined through comparison of predicted 50-year-old equivalent macular GCL thickness for the entire cohort to a true 50-year-old reference cohort. Results: Pattern recognition clustered GCL thickness across the macula into five to eight spatially concentric classes. F-test demonstrated segmented linear regression to be the most appropriate model for macular GCL change. The pattern recognition-derived and normalized model revealed less difference between the predicted macular GCL thickness and the reference cohort (average ± SD 0.19 ± 0.92 and -0.30 ± 0.61 μm) than a gridwise model (average ± SD 0.62 ± 1.43 μm). Conclusions: Pattern recognition successfully identified statistically separable macular areas that undergo a segmented linear reduction with age. This regression model better predicted macular GCL thickness. The various unique spatial patterns revealed by pattern recognition combined with core GCL thickness data provide a framework to analyze GCL loss in ocular disease.
dc.languageEnglish
dc.publisherASSOC RESEARCH VISION OPHTHALMOLOGY INC
dc.titlePattern Recognition Analysis of Age-Related Retinal Ganglion Cell Signatures in the Human Eye
dc.typeJournal Article
dc.identifier.doi10.1167/iovs.17-21450
melbourne.affiliation.departmentAnatomy and Neuroscience
melbourne.source.titleInvestigative Ophthalmology and Visual Science
melbourne.source.volume58
melbourne.source.issue7
melbourne.source.pages3086-3099
dc.rights.licenseCC BY-NC-ND
melbourne.elementsid1228383
melbourne.contributor.authorKalloniatis, Michael
dc.identifier.eissn1552-5783
melbourne.accessrightsOpen Access


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