Objectives: This study aimed to use deep learning to identify glaucoma and normal eyes in groups with high myopia using fundus photographs. Methods: Patients who visited Tri-Services General Hospital from 1 November 2018 to 31 October 2022 were retrospectively reviewed. Patients with high myopia (spherical equivalent refraction of ≤−6.0 D) were included in the current analysis. Meanwhile, patients with pathological myopia were excluded. The participants were then divided into the high myopia group and high myopia glaucoma group. We used two classification models with the convolutional block attention module (CBAM), an attention mechanism module that enhances the performance of convolutional neural networks (CNNs), to investigate glaucoma cases. The learning data of this experiment were evaluated through fivefold cross-validation. The images were categorized into training, validation, and test sets in a ratio of 6:2:2. Grad-CAM visual visualization improved the interpretability of the CNN results. The performance indicators for evaluating the model include the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results: A total of 3088 fundus photographs were used for the deep-learning model, including 1540 and 1548 fundus photographs for the high myopia glaucoma and high myopia groups, respectively. The average refractive power of the high myopia glaucoma group and the high myopia group were −8.83 ± 2.9 D and −8.73 ± 2.6 D, respectively (p = 0.30). Based on a fivefold cross-validation assessment, the ConvNeXt_Base+CBAM architecture had the best performance, with an AUC of 0.894, accuracy of 82.16%, sensitivity of 81.04%, specificity of 83.27%, and F1 score of 81.92%. Conclusions: Glaucoma in individuals with high myopia was identified from their fundus photographs.

(1) Background: It has rarely been studied whether the severity of diabetic retinopathy (DR) could influence renal disease progression in end-stage renal disease (ESRD) and chronic kidney disease (CKD) in patients with type 2 diabetes. The aim of this study was to evaluate renal disease progression in ESRD and CKD according to DR severity in patients with type 2 diabetes. (2) Methods: We included 1329 patients and divided the cohort into two end-points. The first was to trace the incidence of ESRD in all enrolled participants and the other was to follow their progression to CKD. (3) Results: Significantly higher crude hazard ratios (HRs) of ESRD incidence in all enrolled participants were noted, and this ratio increased in a stepwise fashion. However, after adjustment, DR severity was not associated with ESRD events. Therefore, a subgroup of 841 patients without CKD was enrolled to track their progression to CKD. Compared with no diabetic retinopathy, the progression of CKD increased in a stepwise fashion, from mild nonproliferative diabetic retinopathy (NPDR) to moderate NPDR, to severe NPDR and to proliferative diabetic retinopathy (PDR), both in the crude and adjusted models. (4) Conclusions: The severity of retinopathy appeared to be associated with renal lesions and the development of CKD. Our findings suggest that the severity of DR is a risk factor for progression to CKD. Therefore, diabetic retinopathy is useful for prognosticating the clinical course of diabetic kidney disease.

Purpose: To improve disease severity classification from fundus images using a hybrid architecture with symptom awareness for diabetic retinopathy (DR). Methods: We used 26,699 fundus images of 17,834 diabetic patients from three Taiwanese hospitals collected in 2007 to 2018 for DR severity classification. Thirty-seven ophthalmologists verified the images using lesion annotation and severity classification as the ground truth. Two deep learning fusion architectures were proposed: late fusion, which combines lesion and severity classification models in parallel using a postprocessing procedure, and two-stage early fusion, which combines lesion detection and classification models sequentially and mimics the decision-making process of ophthalmologists. Messidor-2 was used with 1748 images to evaluate and benchmark the performance of the architecture. The primary evaluation metrics were classification accuracy, weighted κ statistic, and area under the receiver operating characteristic curve (AUC). Results: For hospital data, a hybrid architecture achieved a good detection rate, with accuracy and weighted κ of 84.29% and 84.01%, respectively, for five-class DR grading. It also classified the images of early stage DR more accurately than conventional algorithms. The Messidor-2 model achieved an AUC of 97.09% in referral DR detection compared to AUC of 85% to 99% for state-of-the-art algorithms that learned from a larger database. Conclusions: Our hybrid architectures strengthened and extracted characteristics from DR images, while improving the performance of DR grading, thereby increasing the robustness and confidence of the architectures for general use. Translational Relevance: The proposed fusion architectures can enable faster and more accurate diagnosis of various DR pathologies than that obtained in current manual clinical practice.

Erythropoietin (EPO) is known as a hormone for erythropoiesis in response to anemia and hypoxia. However, the effect of EPO is not only limited to hematopoietic tissue. Several studies have highlighted the neuroprotective function of EPO in extra-hematopoietic tissues, especially the retina. EPO could interact with its heterodimer receptor (EPOR/βcR) to exert its anti-apoptosis, anti-inflammation and anti-oxidation effects in preventing retinal ganglion cells death through different intracellular signaling pathways. In this review, we summarized the available pre-clinical studies of EPO in treating glaucomatous optic neuropathy, optic neuritis, non-arteritic anterior ischemic optic neuropathy and traumatic optic neuropathy. In addition, we explore the future strategies of EPO for optic nerve protection and repair, including advances in EPO derivates, and EPO deliveries. These strategies will lead to a new chapter in the treatment of optic neuropathy.

Scar formation can cause the failure of glaucoma filtration surgery. We investigated the effect of AR12286, a selective Rho-associated kinase inhibitor, on myofibroblast transdifferentiation and intraocular pressure assessment in rabbit glaucoma filtration surgery models. Cell migration and collagen contraction were used to demonstrate the functionality of AR12286-modulated human conjunctival fibroblasts (HConFs). Polymerase chain reaction quantitative analysis was used to determine the effect of AR12286 on the production of collagen Type 1A1 and fibronectin 1. Cell migration and collagen contraction in HConFs were activated by TGF-β1. However, compared with the control group, rabbit models treated with AR12286 exhibited higher reduction in intraocular pressure after filtration surgery, and decreased collagen levels at the wound site in vivo. Therefore, increased α-SMA expression in HConFs induced by TGF-β1 could be inhibited by AR12286, and the production of Type 1A1 collagen and fibronectin 1 in TGF-β1-treated HConFs was inhibited by AR12286. Overall, the stimulation of HConFs by TGF-β1 was alleviated by AR12286, and this effect was mediated by the downregulation of TGF-β receptor-related SMAD signaling pathways. In vivo results indicated that AR12286 thus improves the outcome of filtration surgery as a result of its antifibrotic action in the bleb tissue because AR12286 inhibited the TGF-β receptor-related signaling pathway, suppressing several downstream reactions in myofibroblast transdifferentiation.

We conducted a retrospective group study to evaluate the potential systemic risk factors for major postoperative complications of cataract surgery. Individuals diagnosed with (n = 2046) and without (n = 8184) serious complications after cataract surgery were matched 1:4 for age, sex, and index date obtained using Taiwan’s National Health Insurance Research Database. The outcome was defined as at least one new inpatient or outpatient diagnosis of systemic disease one year before the index date. The effect of demographic data on postoperative complications was also analyzed in the multivariable model. Data were analyzed using univariate and multivariate conditional logistic regression models to calculate odds ratios (ORs) and 95% confidence intervals of the risk of developing serious complications. After the entire study interval, the major postoperative complications of cataract surgery were associated with the following systemic diseases: hypertension (adjusted OR (aOR) = 2.329, p < 0.001), diabetes mellitus (aOR = 2.818, p < 0.001), hyperlipidemia (aOR = 1.702, p < 0.001), congestive heart failure (aOR = 2.891, p < 0.001), rheumatic disease (aOR = 1.965, p < 0.001), and kidney disease needing hemodialysis (aOR = 2.942, p < 0.001). Additionally, demographic data including old age, higher urbanization level, higher level of care, and more frequent inpatient department visits were associated with a higher rate of postoperative complications. In conclusion, metabolic syndrome, chronic heart failure, end-stage renal disease, rheumatic disease, older age, and frequent inpatient department visits are correlated with the development of severe postoperative complications of cataract surgery. Therefore, cataract surgery patients should be informed about a higher possibility of postoperative complications.