PurposeTo identify factors predicting corneal astigmatic change following suture removal in post-penetrating keratoplasty patients.Patients and methodsThis cross-sectional study included the data of 33 events of corneal suture removal from 27 post-penetrating keratoplasty patients. Sutures were removed from the steep axis of transplanted cornea with 16 interrupted corneal sutures. Corneal astigmatism was measured before and after suture removal using ORBSCAN II. Patients’ demographic data and corneal biomechanics parameters obtaining from the Corvis ST were recorded. The changes in corneal astigmatism were calculated using vector analysis. The correlation between changes in corneal astigmatism and the potential factors was evaluated using Spearman’s correlation coefficient and linear regression model.ResultsThe mean corneal astigmatism before and after suture removal was 7.1±3.7 diopters (D) and 5.5±3.2D, respectively. The mean astigmatic change was 7.0±6.3D (range, 0.3–30.8D) by vector analysis at 9.7±5.5 weeks after suture removal. Change in corneal astigmatism was significantly correlated with pre-suture removal astigmatism (Rs=0.47, P=0.01). There was no correlation between the donor-recipient trephine diameter difference, the duration from corneal transplantation to suture removal, the number of removed sutures with the change in corneal astigmatism, and corneal biomechanics parameters (P>0.05). Linear regression is given by the following equation: astigmatic change (D)=1.05x pre-suture removal astigmatism (D) - 0.43.ConclusionThe astigmatic change after corneal suture removal in post-keratoplasty patients was significantly correlated with pre-suture removal astigmatism. These findings will permit a validated approach for reducing corneal astigmatism in post-keratoplasty patients.
Glaucoma is a neurodegenerative disease that affects the optic nerve head and causes visual field defect. Current investigations focus on neural component which may overlook other important factors such as the vascular cause. The optical coherence tomography angiography (OCTA) imaging has been developed and provided quantitative parameters that showed good diagnostic accuracy to detect glaucoma. However, those parameters are based on image processing of observed clinical findings, therefore, some image information can be lost. Convolutional neural network has been successfully applied for automatic feature extraction and object classification. In this study, the glaucoma diagnosis network, namely GlauNet, has been proposed. GlauNet consists of two sections: the feature-extraction section and the classification section. The feature-extraction section has three convolutional layers. Each convolutional layer is followed by rectified linear unit and maximum pooling layer. The classification section contains five fully connected layers. GlauNet was trained with 258 glaucomatous and 439 non-glaucomatous eyes. The visualization of the feature-extraction section showed the highlight in the area of optic nerve head and retinal nerve fiber layer in the superotemporal and inferotemporal regions. It was then tested on 27 glaucomatous and 48 non-glaucomatous eyes. Its sensitivity and specificity were 88.9% with 89.6%, respectively. The area under receiver operating characteristic curve of GlauNet was 0.89. GlauNet was robust against the artifacts. Its sensitivity and specificity were still higher than 80% (82.4% and 80.3%, respectively) when tested on 88 poor-quality images.
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