PURPOSE.To characterize longitudinal changes in macular microvasculature as quantified from optical coherence tomography angiography (OCTA) metrics in primary open-angle glaucoma (POAG) eyes with and without high myopia. METHODS.In total, 63 and 61 POAG eyes with and without high myopia, respectively, underwent swept-source OCTA imaging in at least four follow-up visits at an ophthalmic center, with a scanning protocol of 3-× 3-mm centered at the fovea. The foveal avascular zone (FAZ) area, FAZ circularity, and vessel density (VD) in both the superficial (SCP) and deep capillary plexuses (DCP) were measured. The rate of change in macular OCTA metrics over time was estimated using linear mixed-effects models in both groups of POAG eyes.RESULTS. The mean follow-up time and number of visits were 27.72 ± 8.57 months and 8.5 (8 to 13) times, and 30.95 ± 10.19 months and 10 (8-13) times in POAG eyes with and without high myopia, respectively. VD in the DCP reduced significantly more quickly in POAG eyes with high myopia than in those without high myopia (−5.14%/year vs. −3.71%/year, P = 0.008). Moreover, lower baseline VD in the DCP was significantly associated with faster VD reduction in POAG with high myopia eyes (P < 0.001). Conversely, the VD reduction rate in the SCP, FAZ area, and FAZ circularity in both the SCP and DCP were similar in both groups (all Ps > 0.05). CONCLUSIONS.VD in DCP reduced significantly more quickly in POAG eyes with high myopia over time. Density in the DCP reduced more quickly when baseline VD was low.

Purpose: To evaluate the intraocular pressure (IOP)-lowering effect of the combination of phacoemulsification with intraocular lens implantation (PEI), goniosynechialysis (GSL), and goniotomy (GT) in eyes of advanced primary angle-closure glaucoma (PACG) with cataract. Design: Multicenter observational study. Methods: We enrolled 83 eyes of 83 patients with advanced PACG who received combined PEI+GSL+GT at 8 ophthalmic institutes. Each patient was assessed before treatment and at 1, 7 days, 1, 3, 6, and 12 months postsurgery. The criteria for complete success were IOP within 6 to 18 mm Hg and at least 20% of reduction in IOP from baseline without ocular hypotensive medications or reoperation. The definition of qualified success was similar to that of complete success, except for the need for ocular hypotensive medications. The potential prognostic factors for surgical success were investigated using a multivariate logistic model. Results: All participants completed 1 year of follow-up. Complete and qualified success were achieved in 74 (89.1%) and 79 (95.2%) of 83 eyes, respectively. The mean preoperative and postsurgical IOPs were 27.4±7.3 and 14.2±2.6 mm Hg, respectively. Participants used an average of 2.0 and 0.3 types of ocular hypotensive medications before and after surgery, respectively. The chief complications included hyphema (n=9), IOP spike (n=9), and corneal edema (n=8). None of the eyes required reoperation or developed vision-threatening complications. Multivariate analysis showed that older age was associated with a higher probability of complete success (odds ratio=1.13; 95% CI: 1.02–1.25; P=0.020). Conclusions: The 1-year results of combination of PEI+GSL+GT in treating advanced PACG cases with cataract appear to be safe and effective. Further large-scale multination and multicenter studies are warranted.

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J. Wu et al. / Preprint (2022) development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multimodality dataset for glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, top-10 teams were selected to the final stage. We analysis their results and summarize their methods in the paper. Since all these teams submitted their source code in the challenge, a detailed ablation study is also conducted to verify the effectiveness of the particular modules proposed. We find many of the proposed techniques are practical for the clinical diagnosis of glaucoma. As the first in-depth study of fundus & OCT multi-modality glaucoma grading, we believe the GAMMA Challenge will be an essential starting point for future research.

Background Deep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts. Methods We established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively. Results The AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81–0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83–0.95) and 0.88 (0.79–0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88–0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81–0.92) and 0.88 (0.83–0.94) in external test sets 1 and 2, respectively. Conclusion Our study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression. FUNDING National Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC.

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