Among participants with complete screening images from both UWF-CFP and SD-OCT, 20% (n = 6/30) of those with self-reported diabetes and 8.5% (n = 5/59) of participants with no history of diabetes were unaware they had mild/moderate nonproliferative DR. Among all participants, 20% (20/100) had a retinal finding, on either UWF-CFP or SD-OCT, or both, which prompted a referral for further evaluation. A retinal screening program deployed via a secure, scalable, and interoperable cloud-based platform was feasible and conveniently integrated into the workplace. Cloud-based platforms could be used to promote a secure, scalable, and interoperable system for retinal screening in nontraditional environments.Cloud-based platforms could be used to promote a secure, scalable, and interoperable system for retinal screening in nontraditional environments. To assess en face ellipsoid zone (EZ) maps of remaining retinal structure as outcome measures for the future clinical research in patients with choroideremia. Twenty eyes from 12 patients with a confirmed genetic diagnosis of choroideremia were included retrospectively from a single site. From spectral domain-optical coherence tomography volume scans, slabs including the EZ were manually segmented to create the en face EZ maps. The preserved EZ area was measured by two graders. Lengths of the EZ were recorded at 0°, 45°, 90°, and 135°. The intraclass correlation coefficients and Bland-Altman plots were used to show intergrader agreement. The Pearson correlation coefficient evaluated the correlation between length and area. A Bland-Altman plot compared en face EZ and the preserved fundus autofluorescence area. Measurements of EZ area by two graders showed excellent agreement with an intraclass correlation coefficient of 0.992 (95% confidence interval, 0.980-0.997). A Pearson correlation analysis showed that the existing marker for preserved photoreceptor (horizontal EZ length) was correlated with the area (r = 0.722). The average EZ length in four meridians showed a much better correlation with the EZ area (r = 0.929). The fundus autofluorescence area was found to be a mean of 0.45 ± 0.99 mm2 greater than the EZ area. EZ area measurement provides excellent intergrader reliability, although the process is time consuming. We propose a less time-consuming alternative to estimate the EZ by using the average EZ band length in meridians. Our data also suggest that the loss of photoreceptor inner segments is an early change in choroideremia and may happen before the loss of the retinal pigment epithelium. En face EZ mapping is a potential tool for future clinical trials to quantify preserved photoreceptor structure in choroideremia.En face EZ mapping is a potential tool for future clinical trials to quantify preserved photoreceptor structure in choroideremia. The purpose of this study was to evaluate the predictive value of optical coherence tomography (OCT) and OCT angiography (OCTA) parameters at baseline on lesion's activity at the 1-year follow-up in type 1 macular neovascularizations (MNVs) treated with 1-year fixed regimen of intravitreal aflibercept injections (q8 IAIs). All patients were imaged by structural OCT to evaluate central macular thickness (CMT), subretinal fluid (SRF), subretinal hyper-reflective material (SHRM), intraretinal fluid (IRF) and intraretinal hyper-reflective dots (HRDs), and by Swept-Source OCTA to measure baseline MNV area, perfusion density (PD), vessel length density (VLD), and vessel diameter index. Metrazole At the end of q8 IAI, patients were classified in two groups active-MNV (A-MNV) and inactive-MNV (I-MNV), considering the OCT signs of activity. Three binary logistic regression models were developed (1) OCT-based, (2) OCTA-based, and (3) OCT/OCTA-based model. Thirty-one treatment-naïve type 1 MNVs were enrolled (13 A-MNV and 18 I-MNV). No differences were observed in baseline OCT and OCTA characteristics between A-MNV and I-MNV. Among the models developed, model 3 that combined OCT/OCTA parameters showed a performance of 87.5% and excellent sensitivity for A-MNV lesions (100%). By analyzing the model, the A-MNV group appears more likely to show at baseline SRF, greater CMT, wider MNV area, and lower PD and VLD compared to I-MNV. Our study demonstrated that the combination of baseline OCT and OCTA parameters allowed to achieve a good models' performance in the prediction of MNV activity permitting to correctly classifying the active lesions at the end of follow-up period, with excellent sensitivity. OCT/OCTA could integrate statistical models potentially useful for artificial intelligence.OCT/OCTA could integrate statistical models potentially useful for artificial intelligence. The purpose of this study was to develop a software package for the automatic classification of anterior chamber angle using anterior segment optical coherence tomography (AS-OCT). AS-OCT images were collected from subjects with open, narrow, and closure anterior chamber angles, which were graded based on ultrasound biomicroscopy (UBM) results. The Inception version 3 network and the transfer learning technique were applied in the design of an algorithm for anterior chamber angle classification. The classification performance was evaluated by fivefold cross-validation and on an independent test dataset. The proposed algorithm reached a sensitivity of 0.999 and specificity of 1.000 in the judgment of closed and nonclosed angles. The overall classification of the proposed method in open angle, narrow angle, and angle-closure classifications reached a sensitivity of 0.989 and specificity of 0.995. Additionally, the sensitivity and specificity reached 1.000 and 1.000 for angle-closure, 0.983 and 0.993 for narrow angle, and 0.985 and 0.991 for open angle. The experimental results showed that the proposed method can achieve a high accuracy of anterior chamber angle classification using AS-OCT images, and could be of value in future practice. The proposed deep learning-based method that automate the classification of anterior chamber angle can facilitate clinical assessment of glaucoma.The proposed deep learning-based method that automate the classification of anterior chamber angle can facilitate clinical assessment of glaucoma.