By 2040, ~100 million people will have glaucoma. To date, there are a lack of high-efficiency glaucoma diagnostic tools based on visual fields (VFs). Herein, we develop and evaluate the performance of ‘iGlaucoma’, a smartphone application-based deep learning system (DLS) in detecting glaucomatous VF changes. A total of 1,614,808 data points of 10,784 VFs (5542 patients) from seven centers in China were included in this study, divided over two phases. In Phase I, 1,581,060 data points from 10,135 VFs of 5105 patients were included to train (8424 VFs), validate (598 VFs) and test (3 independent test sets—200, 406, 507 samples) the diagnostic performance of the DLS. In Phase II, using the same DLS, iGlaucoma cloud-based application further tested on 33,748 data points from 649 VFs of 437 patients from three glaucoma clinics. With reference to three experienced expert glaucomatologists, the diagnostic performance (area under curve [AUC], sensitivity and specificity) of the DLS and six ophthalmologists were evaluated in detecting glaucoma. In Phase I, the DLS outperformed all six ophthalmologists in the three test sets (AUC of 0.834–0.877, with a sensitivity of 0.831–0.922 and a specificity of 0.676–0.709). In Phase II, iGlaucoma had 0.99 accuracy in recognizing different patterns in pattern deviation probability plots region, with corresponding AUC, sensitivity and specificity of 0.966 (0.953–0.979), 0.954 (0.930–0.977), and 0.873 (0.838–0.908), respectively. The ‘iGlaucoma’ is a clinically effective glaucoma diagnostic tool to detect glaucoma from humphrey VFs, although the target population will need to be carefully identified with glaucoma expertise input.

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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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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Trabeculectomy with adjunctive use of Mitomycin C (MMC) has been a benchmark for glaucoma filtration surgery for decades. However, there are many variations in the ways that the sponges soaked with MMC are applied during the trabeculectomy surgery. We herein describe our way of placing the MMC-soaked sponges to improve the safety and efficacy of the trabeculectomy. The sponges are placed vertically and posteriorly with the long side of the sponge perpendicular to the limbus, not parallel. This will reduce the size of the conjunctival wound at the limbus to preserve more virgin conjunctiva that can be used for repeated trabeculectomy when needed. This will also facilitate a more posteriorly directed flow of aqueous drainage that, in turn, may increase the success rate of the trabeculectomy. We have obtained encouraging results in our practice, and further large-scale randomized studies seem warranted.

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