ArticleTranslational vision science & technology2022
Prediction of Retinal Ganglion Cell Counts Considering Various Displacement Methods From OCT-Derived Ganglion Cell-Inner Plexiform Layer Thickness.
Article in Translational vision science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Exploring the relationship between 24-2 visual field and widefield optical coherence tomography data across healthy, glaucoma suspect and glaucoma eyes.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2024Article
- Characterisation of the normal human ganglion cell-inner plexiform layer using widefield optical coherence tomography.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2024Article
- Spatial Summation in the Glaucomatous Macula: A Link With Retinal Ganglion Cell Damage.Investigative ophthalmology & visual science · 2023Article
- High sampling resolution optical coherence tomography reveals potential concurrent reductions in ganglion cell-inner plexiform and inner nuclear layer thickness but not in outer retinal thickness in glaucoma.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2023Article
- Author Response: Expected Improvement in Structure-Function Agreement With Macular Displacement Models.Translational vision science & technology · 2022Article
- Letter to the Editor: Expected Improvement in Structure-Function Agreement With Macular Displacement Models.Translational vision science & technology · 2022Article
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Authors and funding
5 authors.
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Abstract
Purpose: To compare various displacement models using midget retinal ganglion cell to cone (mRGC:C) ratios and to determine viability of estimating RGC counts from optical coherence tomography (OCT)-derived ganglion cell-inner plexiform layer (GCIPL) measurements. Methods: Four Drasdo model variations were applied to macular visual field (VF) stimulus locations: (1) using meridian-specific Henle fiber length along the stimulus circumference; (2) using meridian-specific differences in RGC receptive field and counts along the stimulus circumference; (3) per method (2), averaged across principal meridians; and (4) per method (3), with the stimulus center displaced only. The Sjöstrand model was applied (5) along the stimulus circumference and (6) to the stimulus center only. Eccentricity-dependent mRGC:C ratios were computed over displaced areas, with comparisons to previous models using sum of squares of the residuals (SSR) and root mean square error (RMSE). RGC counts estimated from OCT-derived ganglion cell layer (GCL) and GCIPL measurements, from 143 healthy participants, were compared using Bland-Altman analyses. Results: Methods 1, 2, and 5 produced mRGC:C ratios most consistent with previous models (SSR 3.82, 4.07, and 3.02; RMSE 0.22, 0.23, and 0.20), while central mRGC:C ratios were overestimated by method 3 and underestimated by methods 4 and 6. RGC counts predicted from GCIPL measurements were within 16% of GCL-based counts, with no notable bias with increasing RGC counts. Conclusions: Sjöstrand displacement and meridian-specific Drasdo displacement applied to VF stimulus circumferences produce mRGC:C ratios consistent with previous models. RGC counts can be estimated from OCT-derived GCIPL measurements. Translational Relevance: Implementing appropriate displacement methods and deriving RGC estimates from relevant OCT parameters enables calculation of the number of RGCs responding to VF stimuli from commercial instrumentation.
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