Dataset Information

Effectiveness of a Qualitative Approach Toward Evaluating OCT Imaging for Detecting Glaucomatous Damage.

ABSTRACT:

Purpose

The purpose of this study was to determine whether a qualitative approach toward evaluating optical coherence tomography (OCT) imaging improves the ability to detect glaucomatous damage compared to a conventional metric of global circumpapillary retinal nerve fiber layer (cpRNFL) thickness.

Methods

A total of 394 healthy eyes and 272 glaucoma eyes were evaluated. Glaucoma eyes were categorized as perimetric (156 eyes) based on a history of three or more consecutive abnormal 24-2 visual field tests or suspected glaucoma if they did not (116 eyes). Customized one-page reports derived using OCT volume scans of the optic disc and macula from these eyes were qualitatively graded for the probability of optic neuropathy affecting the eye.

Results

The sensitivity of detecting perimetric glaucoma eyes with the global circumpapillary RNFL thickness metric and qualitative evaluation of the OCT imaging results were 86.5% and 95.5% at a specificity of 95%, being significantly higher for the latter (P < 0.001). There were seven eyes with perimetric glaucoma missed by the qualitative evaluation. Based upon examination of all available visual fields, at least four of these seven eyes had visual fields that either improved or had abnormalities that were inconsistent over time or with patterns of glaucomatous damage.

Conclusions

Qualitative evaluation of OCT imaging results allows glaucoma eyes with repeatable visual field abnormalities to be detected with a high level of accuracy, performing better than a conventional summary metric of global cpRNFL thickness.

Translational relevance

Clinical detection of glaucomatous damage with OCT imaging can be optimized through a qualitative evaluation of its results.

SUBMITTER: Wu Z

PROVIDER: S-EPMC6052952 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposePrevious approaches using deep learning (DL) algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using quantitative spectral-domain (SD) OCT data to train a DL algorithm to quantify glaucomatous structural damage on optic disc photographs.DesignCross-sectional study.ParticipantsA total of 32 820 pairs of optic disc photographs and SD OCT retinal nerve fiber layer (RNFL) scans from 2312 eyes of 1198 participants.MethodsThe sample was divided randomly into validation plus training (80%) and test (20%) sets, with randomization performed at the patient level. A DL convolutional neural network was trained to assess optic disc photographs and predict SD OCT average RNFL thickness.Main outcome measuresThe DL algorithm performance was evaluated in the test sample by evaluating correlation and agreement between the predictions and actual SD OCT measurements. We also assessed the ability to discriminate eyes with glaucomatous visual field loss from healthy eyes with area under the receiver operating characteristic (ROC) curves.ResultsThe mean prediction of average RNFL thickness from all 6292 optic disc photographs in the test set was 83.3±14.5 μm, whereas the mean average RNFL thickness from all corresponding SD OCT scans was 82.5±16.8 μm (P = 0.164). There was a very strong correlation between predicted and observed RNFL thickness values (Pearson r = 0.832; R2 = 69.3%; P < 0.001), with mean absolute error of the predictions of 7.39 μm. The area under the ROC curves for discriminating glaucomatous from healthy eyes with the DL predictions and actual SD OCT average RNFL thickness measurements were 0.944 (95% confidence interval [CI], 0.912-0.966) and 0.940 (95% CI, 0.902-0.966), respectively (P = 0.724).ConclusionsWe introduced a novel DL approach to assess fundus photographs and provide quantitative information about the amount of neural damage that can be used to diagnose and stage glaucoma. In addition, training neural networks to predict SD OCT data objectively represents a new approach that overcomes limitations of human labeling and could be useful in other areas of ophthalmology.

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