Dataset Information

Evaluation of a Method for Estimating Retinal Ganglion Cell Counts Using Visual Fields and Optical Coherence Tomography.

ABSTRACT:

Purpose

To evaluate the accuracy and generalizability of a published model that derives estimates of retinal ganglion cell (RGC) counts and relates structural and functional changes due to glaucoma.

Methods

Both the Harwerth et al. nonlinear model (H-NLM) and the Hood and Kardon linear model (HK-LM) were applied to an independent dataset of frequency-domain optical coherence tomography and visual fields, consisting of 48 eyes of 48 healthy controls, 100 eyes of 77 glaucoma patients and suspects, and 18 eyes of 14 nonarteritic anterior ischemic optic neuropathy (ION) patients with severe vision loss. Using the coefficient of determination R2, the models were compared while keeping constant the topographic maps, specifically a map by Garway-Heath et al. and a separate map by Harwerth et al., which relate sensitivity test stimulus locations to corresponding regions around the optic disc. Additionally, simulations were used to evaluate the assumptions of the H-NLM.

Results

Although the predictions of the HK-LM with the anatomically-derived Garway-Heath et al. map were reasonably good (R2 = 0.31-0.64), the predictions of the H-NLM were poor (R2 < 0) regardless of the map used. Furthermore, simulations of the H-NLM yielded results that differed substantially from RGC estimates based on histology from human subjects. Finally, the value-added of factors increasing the relative complexity of the H-NLM, such as assumptions regarding age- and stage-dependent corrections to structural measures, was unclear.

Conclusions

Several of the assumptions underlying the H-NLM should be revisited. Studies and models relying on the RGC estimates of the H-NLM should be interpreted with caution.

SUBMITTER: Raza AS

PROVIDER: S-EPMC4406034 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Publications

Evaluation of a Method for Estimating Retinal Ganglion Cell Counts Using Visual Fields and Optical Coherence Tomography.

Raza Ali S AS Hood Donald C DC

Investigative ophthalmology & visual science 20150401 4

<h4>Purpose</h4>To evaluate the accuracy and generalizability of a published model that derives estimates of retinal ganglion cell (RGC) counts and relates structural and functional changes due to glaucoma.<h4>Methods</h4>Both the Harwerth et al. nonlinear model (H-NLM) and the Hood and Kardon linear model (HK-LM) were applied to an independent dataset of frequency-domain optical coherence tomography and visual fields, consisting of 48 eyes of 48 healthy controls, 100 eyes of 77 glaucoma patient ...[more]

PMID: 25604684

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Project description:ImportanceAs currently used, microperimetry is a burdensome clinical testing modality for testing retinal sensitivity requiring long testing times and trained technicians.ObjectiveTo create a deep-learning network that could directly estimate function from structure de novo to provide an en face high-resolution map of estimated retinal sensitivity.Design, setting, and participantsA cross-sectional imaging study using data collected between January 1, 2016, and November 30, 2017, from the Natural History Observation and Registry of macular telangiectasia type 2 (MacTel) evaluated 38 participants with confirmed MacTel from 2 centers.Main outcomes and measuresMean absolute error of estimated compared with observed retinal sensitivity. Observed retinal sensitivity was obtained with fundus-controlled perimetry (microperimetry). Estimates of retinal sensitivity were made with deep-learning models that learned on superpositions of high-resolution optical coherence tomography (OCT) scans and microperimetry results. Those predictions were used to create high-density en face sensitivity maps of the macula. Training, validation, and test sets were segregated at the patient level.ResultsA total of 2499 microperimetry sensitivities were mapped onto 1708 OCT B-scans from 63 eyes of 38 patients (mean [SD] age, 74.3 [9.7] years; 15 men [39.5%]). The numbers of examples for our algorithm were 67 899 (103 053 after data augmentation) for training, 1695 for validation, and 1212 for testing. Mean absolute error results were 4.51 dB (95% CI, 4.36-4.65 dB) when using linear regression and 3.66 dB (95% CI, 3.53-3.78 dB) when using the LeNet model. Using a 49.9 million-variable deep-learning model, a mean absolute error of 3.36 dB (95% CI, 3.25-3.48 dB) of retinal sensitivity for validation and test was achieved. Correlation showed a high degree of agreement (Pearson correlation r = 0.78). By paired Wilcoxon rank sum test, our model significantly outperformed these 2 baseline models (P < .001).Conclusions and relevanceHigh-resolution en face maps of estimated retinal sensitivities were created in eyes with MacTel. The maps were of unequalled resolution compared with microperimetry and were able to correctly delineate functionally healthy and impaired retina. This model may be useful to monitor structural and functional disease progression and has potential as an objective surrogate outcome measure in investigational trials.

Dataset Information

Evaluation of a Method for Estimating Retinal Ganglion Cell Counts Using Visual Fields and Optical Coherence Tomography.

Purpose

Methods

Results

Conclusions

Publications

Evaluation of a Method for Estimating Retinal Ganglion Cell Counts Using Visual Fields and Optical Coherence Tomography.

Similar Datasets

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