A 3-Dimensional Convolutional Neural Network Model to Estimate the Glaucomatous Visual Field from Optical Coherence Tomography
DOI:
https://doi.org/10.51094/jxiv.170Keywords:
optical coherence tomography, glaucoma, visual field, machine learningAbstract
Purpose: To improve the accuracy of glaucomatous visual field (VF) estimations from optical coherence tomography (OCT) images, we trained a segmentation-free 3-dimensional convolutional neural network (3DCNN) model instead of the conventional 2-dimensional model.
Design: Retrospective, single-clinic all-cases survey.
Method: We investigated 3,416 patients (6,356 eyes) who underwent OCT and Humphrey Field Analyzer (HFA) 24-2 or 10-2 tests, including all types of glaucoma or suspected glaucoma. All patients containing intraocular disease with or without glaucoma and all images with a weak signal strength index, except those with VF defects due to extraocular diseases, were included. The teacher values of the VF thresholds to be learned and of the VF threshold slopes to be learned were calculated from the pointwise linear regression of the VF thresholds. The mean VF values of all measurement points were 26.5 ± 6.91 dB [mean ± standard deviation] for HFA24-2 and 27.8 ± 8.30 for HFA10-2.
Results: The root mean square error per paired data for glaucoma cases were 3.27 ± 2.20 dB for HFA24-2 and 3.02 ± 2.28 dB for HFA10-2. The Pearson's correlation between the VF and the estimated VF in glaucoma cases were r=0.848 for HFA24-2 and r=0.882 for HFA10-2. The Pearson's correlation between the mean deviation (MD) and the estimated MD in glaucoma cases were r=0.903 for HFA24-2 and r=0.923 for HFA10-2. The Pearson's correlation between the VF slopes and the estimated VF slopes in glaucoma cases were r=0.431 for HFA24-2 and r=0.438 for HFA10-2. The Pearson's correlation between the MD slopes and the estimated MD slopes in glaucoma cases were r=0.429 for HFA24-2 and r=0.383 for HFA10-2.
Conclusion: The 3DCNN model achieved high VF estimation accuracy using OCT images.
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Submitted: 2022-09-20 04:54:45 UTC
Published: 2022-09-27 00:12:15 UTC
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Makoto Koyama
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