Artificial intelligence capabilities have, recently, greatly improved. In the past few years, one of the deep learning algorithms, the recurrent neural network (RNN), has shown an outstanding ability in sequence labeling and prediction tasks for sequential data. We built a reliable visual field prediction algorithm using RNN and evaluated its performance in comparison with the conventional pointwise ordinary linear regression (OLR) method. A total of 1,408 eyes were used as a training dataset and another dataset, comprising 281 eyes, was used as a test dataset. Five consecutive visual field tests were provided to the constructed RNN as input and a 6^th visual field test was compared with the output of the RNN. The performance of the RNN was compared with that of OLR by predicting the 6^th visual field in the test dataset. The overall prediction performance of RNN was significantly better than OLR. The pointwise prediction error of the RNN was significantly smaller than that of the OLR in most areas known to be vulnerable to glaucomatous damage. The RNN was also more robust and reliable regarding worsening in the visual field examination. In clinical practice, the RNN model can therefore assist in decision-making for further treatment of glaucoma.

We developed a deep learning architecture based on Inception V3 to predict visual field using optical coherence tomography (OCT) imaging and evaluated its performance. Two OCT images, macular ganglion cell-inner plexiform layer (mGCIPL) and peripapillary retinal nerve fibre layer (pRNFL) thicknesses, were acquired and combined. A convolutional neural network architecture was constructed to predict visual field using this combined OCT image. The root mean square error (RMSE) between the actual and predicted visual fields was calculated to evaluate the performance. Globally (the entire visual field area), the RMSE for all patients was 4.79 ± 2.56 dB, with 3.27 dB and 5.27 dB for the normal and glaucoma groups, respectively. The RMSE of the macular region (4.40 dB) was higher than that of the peripheral region (4.29 dB) for all subjects. In normal subjects, the RMSE of the macular region (2.45 dB) was significantly lower than that of the peripheral region (3.11 dB), whereas in glaucoma subjects, the RMSE was higher (5.62 dB versus 5.03 dB, respectively). The deep learning method effectively predicted the visual field 24–2 using the combined OCT image. This method may help clinicians determine visual fields, particularly for patients who are unable to undergo a physical visual field exam.

Aims: To investigate the histopathology of the fibrous capsule around Ahmed glaucoma valves (AGVs) implanted with adjunctive amniotic membranes in rabbits. Methods: AGV implantation with or without adjunctive amniotic membrane was performed in a single eye of 20 albino rabbits. The upper surface of the AGV body was covered with amniotic membrane in the study group. After 2 months, histology was used to compare the thickness and characteristics of the fibrous capsule, transdifferentiation of myofibroblasts, and density of blood vessels and leukocytes between the study and control groups. Results: The fibrous capsule along the roof of the bleb was composed of compact collagen fibers with minimal vascularization in the control group. In contrast, in the study group, the fibrous capsule was looser and had a more disorganized collagen architecture. The thickness of the fibrous capsule and the myofibroblast layer was significantly thinner in the study group than in the control group (p < 0.001). The number of CD31-positive blood vessels did not differ between the two groups (p = 0.235). CD45-positive inflammatory cells were more frequently observed in the study group than the control group (p = 0.001). The groups did not differ in the thickness of the fibrous capsule or myofibroblast layer, or the density of blood vessels and leukocytes along the floor of the bleb. Conclusions: Adjunctive amniotic membranes could reduce the risk of encapsulation and aqueous outflow resistance by altering the tissue response to implanted AGVs and subsequent formation of a loose thin capsule.

We evaluated the relationships between corneal biomechanical properties and structural parameters in patients with newly diagnosed, untreated normal-tension glaucoma (NTG). All subjects were evaluated using an Ocular Response Analyzer (ORA) measuring corneal hysteresis (CH) and the corneal resistance factor (CRF). Central corneal thickness (CCT), Goldmann applanation tonometric (GAT) data, axial length, and the spherical equivalent (SE), were also measured. Confocal scanning laser ophthalmoscopy was performed with the aid of a Heidelberg retina tomograph (HRT III). We sought correlations between HRT parameters and different variables including CCT, CH, and the CRF. Multiple linear regression analysis was performed to identify significant associations between corneal biomechanical properties and optic nerve head parameters. We enrolled 95 eyes of 95 NTG patients and 93 eyes of 93 normal subjects. CH and the CRF were significantly lower in more advanced glaucomatous eyes (P = 0.001, P = 0.008, respectively). The rim area, rim volume, linear cup-to-disc ratio (LCDR), and mean retinal nerve fiber layer (RNFL) thickness were significantly worse in more advanced glaucomatous eyes (P < 0.001, P < 0.001, P < 0.001, and P = 0.001). CH was directly associated with rim area, rim volume, and mean RNFL thickness (P = 0.012, P = 0.028, and P = 0.043) and inversely associated with LCDR (P = 0.015), after adjusting for age, axial length, CCT, disc area, GAT data, and SE. However, in normal subjects, there were no significant associations between corneal biomechanical properties and HRT parameters. A lower CH is significantly associated with a smaller rim area and volume, a thinner RNFL, and a larger LCDR, independent of disc size, corneal thickness, intraocular pressure, and age.

Purpose: To compare intraocular pressure (IOP) measurements before and after laser in situ keratomileusis (LASIK) using a femtosecond laser for flap creation, and to identify factors that may influence the preoperative and postoperative IOP, and the change in IOP after LASIK. Methods: Forty eyes from 20 patients who underwent treatment for myopia using a femtosecond laser for flap creation were enrolled in this study. The IOP and corneal biomechanical markers were prospectively measured preoperatively and 1 month after LASIK with Goldmann applanation tonometry (GAT) and the ocular response analyzer (ORA). Manifest refraction spherical equivalent, central corneal thickness, ablation depth, and mean corneal curvature (K reading) were also recorded. Results: After LASIK, there was a significant reduction in the IOP measurement with respect to the corneal-compensated intraocular pressure (IOPcc) (Δ=−0.67±2.07 mm Hg), Goldmann-correlated pressure (IOPg) (Δ=−3.92±2.19 mm Hg), and GAT (Δ=−2.6±2.51). Multiple regression analysis revealed that the corneal hysteresis (CH) and corneal resistance factor (CRF) are statistically significant predictors of IOPcc, IOPg, and GAT (P<0.000). In this analysis, the preoperative manifest refraction spherical equivalent, CH, and CRF were significant predictors of ΔIOPcc (adjusted R²=0.401) and ΔIOPg (adjusted R²=0.386). The preoperative SE and central corneal thickness significantly predicted ΔGAT (adjusted R²=0.464). Conclusions: ORA provides a more complete measurement of IOP after LASIK with a femtosecond laser than GAT because ORA provides greater knowledge of the corneal biomechanics in terms of CH and CRF.