Background: Intraocular pressure (IOP) may vary according to the change of ocular conditions. In this study, we want to assess the effect and mechanism of pupil dilation on IOP in normal subjects. Methods: We prospectively evaluated 32 eyes of 32 patients (age; 61.7 ± 8.2 years) with normal open angles under diurnal IOP. IOP was measured every two hours from 9 AM to 11 PM for one day to establish baseline values and was measured again for one day to assess the differences after dilation. To induce dilation, we administered 2.5% phenylephrine and 1% tropicamide every 5 minutes from 8:30 AM to 8:45 AM and for every two hours from 11 AM to 9 PM to keep the pupil dilated. Diurnal IOP, biometry, Visante OCT, and laser flare photometry were measured before and after dilation. Results: We observed a significant increase in IOP after dilation, 1.85 ± 2.01 mmHg (p = 0.002). IOP elevation remained significant until about four hours after dilation. Thereafter, IOP decreased slowly and eventually reached pre-dilation level (p > 0.05). Flare values decreased, and the anterior chamber angle became wider after mydriasis. Conclusions: Dilation of the pupil significantly and incidentally elevated IOP in normal subjects. Further related studies are warranted to characterize the mechanism of the increased IOP after dilation.

This study aimed to develop and validate a deep learning system for diagnosing glaucoma using optical coherence tomography (OCT). A training set of 1822 eyes (332 control, 1490 glaucoma) with 7288 OCT images, an internal validation set of 425 eyes (104 control, 321 glaucoma) with 1700 images, and an external validation set of 355 eyes (108 control, 247 glaucoma) with 1420 images were included. Deviation and thickness maps of retinal nerve fiber layer (RNFL) and ganglion cell–inner plexiform layer (GCIPL) analyses were used to develop the deep learning system for glaucoma diagnosis based on the visual geometry group deep convolutional neural network (VGG-19) model. The diagnostic abilities of deep learning models using different OCT maps were evaluated, and the best model was compared with the diagnostic results produced by two glaucoma specialists. The glaucoma-diagnostic ability was highest when the deep learning system used the RNFL thickness map alone (area under the receiver operating characteristic curve (AUROC) 0.987), followed by the RNFL deviation map (AUROC 0.974), the GCIPL thickness map (AUROC 0.966), and the GCIPL deviation map (AUROC 0.903). Among combination sets, use of the RNFL and GCIPL deviation map showed the highest diagnostic ability, showing similar results when tested via an external validation dataset. The inclusion of the axial length did not significantly affect the diagnostic performance of the deep learning system. The location of glaucomatous damage showed generally high level of agreement between the heatmap and the diagnosis of glaucoma specialists, with 90.0% agreement when using the RNFL thickness map and 88.0% when using the GCIPL thickness map. In conclusion, our deep learning system showed high glaucoma-diagnostic abilities using OCT thickness and deviation maps. It also showed detection patterns similar to those of glaucoma specialists, showing promising results for future clinical application as an interpretable computer-aided diagnosis.

Kim HT, et al. Korean J Ophthalmol. 2016 Dec;30(6):426-433. https://doi.org/10.3341/kjo.2016.30.6.426

Purpose: To investigate the prevalence of open-angle glaucoma (OAG) in myopia by age. Materials and methods: A cross-sectional study using a stratified, multistage, probability cluster survey. Participants in the Korean National Health and Nutrition Examination Survey between 2010 and 2011 were included. A standardized protocol was used to interview every participant and perform comprehensive ophthalmic examinations. Glaucoma was diagnosed according to the International Society of Geographical and Epidemiological Ophthalmology (ISGEO) criteria. Results: After adjusting for age and sex, there was a positive correlation between OAG prevalence and increasing myopic refractive error except in participants with hyperopia. Younger participants with higher myopic refractive error had higher OAG prevalence than older participants with lower myopic refractive error. Participants with high myopia (OR 3.90, 95% confidence interval (CI) 2.30–6.59) had significantly greater age- and sex-adjusted odd ratios (ORs) than did those with emmetropia who were younger than 60 years. Conclusions: These data suggest that OAG develops earlier in participants with high myopia than in others. There was a high prevalence of OAG in participants with high myopia, even in those 19–29 years of age. Therefore, OAG screening should be performed earlier in participants with high myopia than is suggested by traditional guidelines.

Aims: To evaluate the age-related variations of ocular parameters in Korean subjects. Methods: We recruited 314 normal subjects who visited the department of ­Ophthalmology between January 2007 and October 2007. Refraction, axial length, corneal curvature, white-to-white distance, anterior chamber depth, corneal endothelial cell density, and retinal nerve fiber layer (RNFL) thickness were measured using auto-refractive keratometer, intraocular lens master, noncontact specular microscope, and optical coherence tomography. Result: In correlation analysis, from 19 to 82 years, hyperopic shift showed a strong positive statistical correlation with age (r = 0.553, P < 0.001). Corneal curvatures increased (r = 0.221, P < 0.001), while axial length (r = -0.506, P < 0.001), anterior chamber depth (r = -0.491, P < 0.001) and white-to-white distance (r = -0.205, P < 0.001) decreased with age. Also, ­corneal endothelial cell density was lower in older patients than in younger patients (r = -0.409, P < 0.001). Compared to younger patients, RNFL thickness was lower in the older patients as well, in all quadrants (superior, r = -0.283, P < 0.001; inferior, r = -0.230, P < 0.001; nasal, r = 0.025, P = 0.676; and temporal, r = -0.393, P < 0.001). According to multiple regression analysis, out of the six parameters measured, only hyperopic shift, anterior chamber depth and corneal endothelial cell density (P < 0.05) had statistically significant ­correlation with age. Conclusion: Some of the ocular parameters changed with aging. Hyperopic shift, shallowing anterior chamber depth, and reduction of corneal endothelial cell density were only definitely related to age.

Purpose.To investigate the role of systemic arterial stiffness in glaucoma patients with diabetes mellitus (DM).Design.Retrospective, cross-sectional study.Participants.DM subjects who underwent brachial-ankle pulse wave velocity (baPWV) were recruited.Methods.Glaucoma patients (n=75) and age-matched control subjects (n=92) were enrolled. Systemic examination including BaPWV and detailed eye examination were performed. The glaucoma group was divided into subgroups of normal tension glaucoma (NTG,n=55) and primary open-angle glaucoma (POAG) based on an IOP of 21 mmHg. BaPWV was used to stratify the population into 4 groups based on the rate. Stepwise multiple logistic regression analysis by baPWV quartiles was used to compare the glaucoma group with the control group.Main Outcome Measures.BaPWV in glaucoma with DM patients.Results.Faster baPWV was positively associated with glaucoma (odds ratio: 3.74; 95% CI: 1.03–13.56, stepwise multiple logistic regression analysis) in patients with DM. Increasing baPWV was also positively associated with glaucoma (pfor trend = 0.036). The NTG subgroup showed similar results to those of the glaucoma group.Conclusions.In this study, increased arterial stiffness was shown to be associated with glaucoma and may contribute to the pathogenesis of glaucoma in DM patients.