Fig 1.
Identification of disc tilt ratio, torsion degree, disc-foveal angle, and peripapillary atrophy area by ImageJ software, and subfoveal choroidal and scleral thicknesses by the caliper function of the OCT software.
Red-free fundus photographs (top), The horizontal cross-sectional images of the posterior pole obtained by Swept-source optical coherence tomography (bottom). (A) Tilt ratio was defined as the ratio between the longest diameter (LD) and shortest diameter (SD) of the optic disc. Peripapillary atrophy area was outlined manually, and the pixel area was calculated automatically using the software. (B) Torsion degree was measured the angle between the LD and vertical meridian (VM) which was considered a vertical line from a horizontal line (HL) connecting the fovea to the center of the optic disc. The disc–foveal angle was measured, defined as the angle between a horizontal line (HL) through the disc center and the line connecting the fovea and disc center. (C, D) Subfoveal choroidal and scleral thicknesses were measured at 3 locations: the subfoveal point and 500 mm temporally and nasally therefrom. Lamellated, high-reflective features are characteristic of the sclera. The full length of the sclera was visible with Swept-source OCT.
Table 1.
Demographic and ocular characteristics of participants with glaucoma.
Table 2.
Logistic regression analysis demonstrates significant variables for visual field defects in open-angle glaucoma patients with myopia.
Fig 2.
Diagram showing the structure of artificial neural networks used in the current study.
In the multilayer perceptron, there were 10 nodes in the first input layer, 6 nodes in the second layer, and 3 nodes in the third layer. Each of the 4 layers (input, two hidden, and output layers) were connected through the stacked denoising autoencoder. Artificial neural network output diagram with insets for each layer. Output figure generated by IBM SPSS vesion.23.0 (Chicago, IL, USA). *parameters include 10 variables.
Fig 3.
Results of artificial neural networks reveal importance and normalized importance values of ten variables for visual field defects in open-angle glaucoma patients with myopia.
Output figure generated by IBM SPSS vesion.23.0 (Chicago, IL, USA). RNFL = retinal nerve fiber layer; PPA = peripapillary atrophy.
Fig 4.
Scatterplots illustrating the linear correlation between standard automated perimetry (SAP) mean deviation (MD) value (dB) and predicted value of visual function.
(A) Correlation between MD value of SAP and the predicted value of visual function using peripapillary RNFL thickness (R2 = 0.642, P < .001). (B) Correlation between MD value of SAP and the predicted value of visual function using 4 variables including PPA area, peripapillary RNFL thickness, disc-foveal angel, and disc torsion degree(R2 = 0.939, P < .001). *Predicted value of visual function was calculated using peripapillary RNFL thickness. †Predicted value of visual function was calculated using PPA area, peripapillary RNFL thickness, disc-foveal angel, and disc torsion degree.
Table 3.
Results of artificial neural networks reveal normalized importance values of variables for visual field defects in open-angle glaucoma patients with myopia.
Fig 5.
Dendrogram shows how clusters were made by the agglomerative technique, which began with each subject being a cluster by itself and merged together continuously based on similarity between clusters.
The two-cluster solution in this analysis had a semipartial R2 value of 0.072, and two clusters seemed to best represent the data in this procedure. Output figure generated by Output figure generated by SAS software version 9.2 (SAS Institute, Cary, NC).
Table 4.
Statistical criteria to determine the optimal number of clusters.
Table 5.
Characteristics of the 2 clusters obtained by a hierarchical cluster analysis.