Dataset Information

Comparing Optic Nerve Head Rim Width, Rim Area, and Peripapillary Retinal Nerve Fiber Layer Thickness to Axon Count in Experimental Glaucoma.

ABSTRACT:

Purpose

We compare spectral-domain optical coherence tomography (SDOCT) measurements of minimum rim width (MRW), minimum rim area (MRA), and peripapillary retinal nerve fiber layer thickness (RNFLT) to complete orbital optic nerve axon counts in nonhuman primates (NHP) with unilateral experimental glaucoma (EG).

Methods

Biweekly SDOCT measurements of MRW, MRA, and RNFLT were acquired under manometric IOP control (10 mm Hg) in 51 NHP during baseline (mean ± SD, 5.0 ± 1.6 sessions) and after laser photocoagulation was applied to the trabecular meshwork of one eye to induce chronic IOP elevation. At the study endpoint (predefined for each NHP), 100% axon counts were obtained from each optic nerve.

Results

For SDOCT parameters at baseline, the correlation between the two eyes of each animal was strongest for RNFLT (R = 0.97) and MRW (R = 0.97), but lower for MRA (R = 0.85). At the final time point, average values in EG eyes relative to control eyes were: -22% for RNFLT, -38% for MRW, -36% for MRA, and -36% for optic nerve axons. The correlation with axon counts was strongest for RNFLT (R = 0.81), compared to MRW (R = 0.72, P = 0.001) or MRA (R = 0.70, P = 0.001). Diagnostic sensitivity was 75% for RNFLT, 90% for MRW, and 88% for MRA; all had 100% specificity.

Conclusions

Peripapillary RNFLT was correlated more closely with total orbital optic nerve axon count than were the ONH parameters MRW or MRA. This is likely because glaucomatous deformation (beyond axon loss alone) has a greater influence on the ONH parameters MRW and MRA than on RNFLT.

SUBMITTER: Fortune B

PROVIDER: S-EPMC4968911 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeTo assess 3-dimensional surface shape patterns of the optic nerve head (ONH) and peripapillary retinal nerve fiber layer (RNFL) in glaucoma with unsupervised artificial intelligence (AI).DesignRetrospective study.ParticipantsPatients with OCT scans obtained between 2016 and 2020 from Massachusetts Eye and Ear.MethodsThe first reliable Cirrus (Carl Zeiss Meditec, Inc) ONH OCT scans from each eye were selected. The ONH and RNFL surface shape was represented by the vertical positions of the inner limiting membrane (ILM) relative to the lowest ILM vertical position in each eye. Nonnegative matrix factorization was applied to determine the ONH and RNFL surface shape patterns, which then were correlated with OCT and visual field (VF) loss parameters and subsequent VF loss rate. We tested whether using ONH and RNFL surface shape patterns improved the prediction accuracy for associated VF loss and subsequent VF loss rates measured by adjusted r 2 and Bayesian information criterion (BIC) difference compared with using established OCT parameters alone.Main outcome measuresOptic nerve head and RNFL surface shape patterns and prediction of the associated VF loss and subsequent VF loss rates.ResultsWe determined 14 ONH and RNFL surface shape patterns using 9854 OCT scans from 5912 participants. Worse mean deviation (MD) was most correlated (r = 0.29 and r = 0.24, Pearson correlation; each P < 0.001) with lower coefficients of patterns 10 and 12 representing inferior and superior para-ONH nerve thinning, respectively. Worse MD was associated most with higher coefficients of patterns 5, 4, and 9 (r = -0.16, r = -0.13, and r = -0.13, respectively), representing higher peripheral ONH and RNFL surfaces. In addition to established ONH summary parameters and 12-clock-hour RNFL thickness, using ONH and RNFL surface patterns improved (BIC decrease: 182, 144, and 101, respectively; BIC decrease ≥ 6; strong model improvement) the prediction of accompanied MD (r 2 from 0.32 to 0.37), superior (r 2 from 0.27 to 0.31), and inferior (r 2 from 0.17 to 0.21) paracentral loss and improved (BIC decrease: 8 and 8, respectively) the prediction of subsequent VF MD loss rates (r 2 from 0 to 0.13) and inferior paracentral loss rates (r 2 from 0 to 0.16).ConclusionsThe ONH and RNFL surface shape patterns quantified by unsupervised AI techniques improved the structure-function relationship and subsequent VF loss rate prediction.