Preprint / Version 6

Three-Dimensional Convolutional Neural Network Model for Estimating Glaucomatous Visual Fields Based on Optical Coherence Tomography

##article.authors##

  • Makoto Koyama Minamikoyasu Eye Clinic

DOI:

https://doi.org/10.51094/jxiv.170

Keywords:

optical coherence tomography, glaucoma, visual field, Deep learning

Abstract

Purpose: To estimate the glaucomatous visual field (VF) accurately using optical coherence tomography (OCT) images, we trained a segmentation-free, three-dimensional convolutional neural network (3DCNN) model.
Methods: This was a retrospective, single-clinic, and all-case survey. We investigated 3,416 consecutive patients (6,356 eyes) who underwent OCT and Humphrey Field Analyzer (HFA) 24-2 or 10-2 tests, including patients with all types of glaucoma or suspected glaucoma. The supervisory signals to be learned—the VF thresholds and their slopes—were calculated by pointwise linear regression of the measured VF thresholds. The training and test subjects were separated and divided into four groups based on the signal strength index (SSI) and the presence of intraocular disease during training by five-fold cross-validation using the 3DCNN model. Then, 10-fold cross-validation was performed under the training conditions that yielded the best test results.
Results: The best estimation performance was obtained by the trained five-fold cross-validation model, which included all SSIs and intraocular diseases in its training data. The root-mean-square error per paired data for glaucoma patients was [mean ± standard deviation] 3.27 ± 2.20 dB for the HFA24-2 values and 3.02 ± 2.28 dB for the HFA10-2 values, as per the results of the 10-fold cross-validation model.
Conclusions: The proposed model achieved a high VF estimation accuracy. Multicenter studies should be conducted in the future to increase the number of cases.
Translational Relevance: This model can reduce the burden on patients by estimating the VF with high accuracy by using OCT images.

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Submitted: 2022-09-20 04:54:45 UTC

Published: 2022-09-27 00:12:15 UTC — Updated on 2022-12-12 05:29:43 UTC

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Reason(s) for revision

Supplementary information was added to the discussion. Other explanations that were not fully explained were added. Partially corrected the English title and abstract.
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General Medicine, Social Medicine, & Nursing Sciences