Evidence map›Paper›PMID 35575777›Full record

ArticleTranslational vision science & technology2022

Prediction of Retinal Ganglion Cell Counts Considering Various Displacement Methods From OCT-Derived Ganglion Cell-Inner Plexiform Layer Thickness.

Janelle Tong, Jack Phu, David Alonso-Caneiro, Sieu K Khuu, Michael Kalloniatis

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. 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) · 2024
    Article
  2. 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) · 2024
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Janelle TongCentre for Eye Health, University of New South Wales (UNSW), Sydney, NSW Australia.
Jack PhuCentre for Eye Health, University of New South Wales (UNSW), Sydney, NSW Australia.
David Alonso-CaneiroQueensland University of Technology, Contact Lens and Visual Optics Laboratory, Centre for Vision and Eye Research, School of Optometry and Vision Science, QLD, Australia.
Sieu K KhuuSchool of Optometry and Vision Science, University of New South Wales, Sydney, NSW Australia.
Michael KalloniatisCentre for Eye Health, University of New South Wales (UNSW), Sydney, NSW Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Retinal Ganglion CellsTomography, Optical CoherenceHumansNerve FibersRetinaVisual Fields

Identifiers

PMID35575777
PMCPMC9123515

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.