Diabetic Eye Research Articles

Mitigation of lens opacification by a functional food in a diabetic rodent model

The current study was designed to test a functional food (FF) mixture containing aldose reductase inhibitors and antiglycation bioactive compounds for suppressing the onset and progression of cataracts in a diabetic rat model. Two-month-old Sprague Dawley rats were grouped as control (C), diabetes untreated (D), and diabetic rats treated with FF at two doses (FF1 = 1.35 g and FF2 = 6.25 g/100g of diet). Diabetes was induced by a single injection of streptozotocin. The FF is a mixture of amla, turmeric, black pepper, cinnamon, ginger, and fenugreek added to the rodent diet. The status of cataracts was monitored weekly by a slit lamp examination for 20 weeks, after which animals were sacrificed to collect eye lenses. Feeding FF1 and FF2 to diabetic rats yielded a significant anti-hyperglycaemic effect and marginally prevented body weight loss. FF delayed cataract progression, and FF2 showed better efficacy than FF1. FF prevented the loss of lens crystallins and their insolubilization in diabetic rats. The antioxidant potential of FF was evident with the lowered protein carbonyls, lipid peroxidation, and prevention of altered antioxidant enzyme activities induced by diabetes. These studies demonstrate the efficacy of plant-derived dietary supplements against the onset and progression of cataracts in a well-established rat model of diabetic eye disease.

Diabetic eye disease in computerised tomography of feature extraction and classification in hybrid neural network

ABSTRACT Blindness or loss of vision is primarily caused by diabetic retinopathy, or DR. It is a retinal condition that may cause harm to the eyes’ blood vessels.Due to the complicated eye structure, manually detecting diabetic retinopathy takes time and is prone to human mistake. There have been several automatic methods proposed for the diagnosis of diabetic retinopathy from fundus images.Using a combination of InceptionV3 and ResNet50, an optimised deep Convolutional Neural Network model of feature fusion is presented in this paper to categories diabetic retinopathy photos into five kinds. Concatenating results in the suggested model including both the Inception Modules and the Residual Blocks, this mitigates the gradient problem and reduces the number of parameters. Resnet50 and Inceptionv3, two different deep learning (DL) models, were used. The proposed hybrid InceptionV3 and ResNet50 based optimised deep CNN (IR_ODCNN) model gathers features from the data and joins them before sending them to the DCNN that has been optimised for classification. The hyper parameters for CNN layer training are optimised using the Grey Wolf Optimizer (GWO) method. An image dataset of the fundus is used to assess the suggested model. The experimental findings indicate that, when compared to the current approaches, In terms of classifying diabetic retinopathy, the proposed model performs better.

Efficacy of Smartphone-based Fundus Photo in Vision Threatening Diabetic Retinopathy Screening: Developing Country Perspective

Background Vision-threatening diabetic retinopathy (VTDR) is a microvascular retinal complication caused by diabetes mellitus, which may lead to blindness if left untreated. One of the most effective methods to prevent diabetic-related ocular complications is through diabetic retinopathy (DR) screening. The community rarely carries out diabetic retinopathy-related eye examinations because using non-portable fundus photographs as its gold standard is costly and impracticable. This study aimed to determine the accuracy of smartphone-based fundus photographs as a practical and affordable tool for VTDR screening in developing countries. Methods This cross-sectional study used a consecutive technique at Cicendo National Eye Hospital, Indonesia. Patients with diabetes mellitus aged ≥20 years were evaluated for two-field mydriatic fundus photos using a non-portable fundus photo and a smartphone- based fundus photo utilizing the i-Spot fundus adapter. Results were analyzed to determine diagnostic test parameters. Results Two hundred and nineteen two-field mydriatic fundus photos were obtained from 139 patients. Smartphone-based fundus photography demonstrated a sensitivity of 98.4% (CI 96.6–100%), a specificity of 87.1% (CI 75.3–98.9%), a positive predictive value of 97.9% (CI 95.9–99.9%), a negative predictive value of 90.0% (CI 79.3–100%), and an accuracy of 96.8% (CI 94.5–99.8%). Conclusion The use of smartphone-captured fundus images proves to be a reliable screening method for VTDR. This tool has the potential to effectively screen the population, helping prevent future visual loss attributed to the disease.

The role of cadre in the community on diabetic retinopathy management and its challenges in low-middle income countries: a scoping review

IntroductionDiabetes is a serious public health problem, with low- and middle-income countries (LMICs) bearing over 80% of the burden. Diabetic retinopathy (DR) is one of the most prevalent diabetic microvascular problems, and early diagnosis through eye screening programs for people with diabetes is critical to prevent vision impairment and blindness. Community-based treatments, including non-physician cadres have been recommended to enhance DR care.MethodsThe review protocol was determined and scoping review was conducted.The population, concept, and context were “cadre”, “role of cadre in the management of DR”, and LMICs”. Data were collected from databases and searches, including grey literature.ResultsCadre can motivate people to attend a diabetic eye screening event when the rate of eye examinations is about six times higher than before the start of the intervention. Health education is a possible area for task sharing, and the cadre reported could also perform the task of vision testing. The cadre could be a good supporter and a good reminder for society. However, several challenges have been faced in this study and inadequate infrastructure is the foremost challenge found in this study. Other challenges encountered in the studies include poverty, lack of community awareness, trust issues, and low education levels contributing to poor health.ConclusionThe current study highlighted significant gaps in the literature, which focus on the role of cadre as a community-based intervention in managing DR in LMICs. Further research is needed to develop evidence to support cost-effective screening services and cadre-related policy development in LMICs.

Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial

Diabetic retinopathy can be prevented with screening and early detection. We hypothesized that autonomous artificial intelligence (AI) diabetic eye exams at the point-of-care would increase diabetic eye exam completion rates in a racially and ethnically diverse youth population. AI for Children’s diabetiC Eye ExamS (NCT05131451) is a parallel randomized controlled trial that randomized youth (ages 8-21 years) with type 1 and type 2 diabetes to intervention (autonomous artificial intelligence diabetic eye exam at the point of care), or control (scripted eye care provider referral and education) in an academic pediatric diabetes center. The primary outcome was diabetic eye exam completion rate within 6 months. The secondary outcome was the proportion of participants who completed follow-through with an eye care provider if deemed appropriate. Diabetic eye exam completion rate was significantly higher (100%, 95%CI: 95.5%, 100%) in the intervention group (n = 81) than the control group (n = 83) (22%, 95%CI: 14.2%, 32.4%)(p < 0.001). In the intervention arm, 25/81 participants had an abnormal result, of whom 64% (16/25) completed follow-through with an eye care provider, compared to 22% in the control arm (p < 0.001). Autonomous AI increases diabetic eye exam completion rates in youth with diabetes.

Unique composition of ocular surface microbiome in the old patients with dry eye and diabetes mellitus in a community from Shanghai, China

BackgroundThis study investigates the variations in microbiome abundance and diversity on the ocular surfaces of diabetic patients suffering from dry eye within a community setting. The goal is to offer theoretical insights for the community-level prevention and treatment of dry eye in diabetic cohorts.MethodsDry eye screening was performed in the Shanghai Cohort Study of Diabetic Eye Disease (SCODE) from July 15, 2021, to August 15, 2021, in the Xingjing community; this study included both a population with diabetes and a normal population. The population with diabetes included a dry eye group (DM-DE, n = 40) and a non-dry eye group (DM-NoDE, n = 39). The normal population included a dry eye group (NoDM-DE, n = 40) and a control group (control, n = 39). High-throughput sequencing of the 16 S rRNA V3-V4 region was performed on conjunctival swab from both eyes of each subject, and the composition of microbiome on the ocular surface of each group was analyzed.ResultsSignificant statistical differences were observed in both α and β diversity of the ocular surface microbiome among the diabetic dry eye, diabetic non-dry eye, non-diabetic dry eye, and normal control groups (P < 0.05).ConclusionsThe study revealed distinct microecological compositions on the ocular surfaces between the diabetic dry eye group and other studied groups. Firmicutes and Anoxybacillus were unique bacterial phyla and genera in the dry eye with DM group, while Actinobacteria and Corynebacterium were unique bacterial phyla and genera in the normal control group.

Discriminating early-stage diabetic retinopathy with subjective and objective perimetry.

To prevent progression of early-stage diabetic retinopathy, we need functional tests that can distinguish multiple levels of neural damage before classical vasculopathy. To that end, we compared multifocal pupillographic objective perimetry (mfPOP), and two types of subjective automated perimetry (SAP), in persons with type 2 diabetes (PwT2D) with either no retinopathy (noDR) or mild to-moderate non-proliferative retinopathy (mmDR). Both eyes were assessed by two mfPOP test methods that present stimuli within either the central ±15° (OFA15) or ±30° (OFA30), each producing per-region sensitivities and response delays. The SAP tests were 24-2 Short Wavelength Automated Perimetry and 24-2 Matrix perimetry. Five of eight mfPOP global indices were significantly different between noDR and mmDR eyes, but none of the equivalent measures differed for SAP. Per-region mfPOP identified significant hypersensitivity and longer delays in the peripheral visual field, verifying earlier findings. Diagnostic power for discrimination of noDR vs. mmDR, and normal controls vs. PwT2D, was much higher for mfPOP than SAP. The mfPOP per-region delays provided the best discrimination. The presence of localized rather than global changes in delay ruled out iris neuropathy as a major factor. mfPOP response delays may provide new surrogate endpoints for studies of interventions for early-stage diabetic eye damage.

Knowledge, attitude, and practice of diabetes in patients with and without sight-threatening diabetic retinopathy from two secondary eye care centres in India.

Good knowledge, Attitude, and Practice (KAP) of diabetes influence its control and complications. We examined the KAP of diabetes in patients with sight-threatening diabetic retinopathy (STDR) and non-sight-threatening diabetic retinopathy (NSTDR) attending two different referral hospitals in India. 400 consecutive patients (mean age = 58.5years ± 10.3) with diabetic retinopathy attending retina referral clinics in Chennai (private) and Darjeeling (public) were recruited. A validated questionnaire on diabetic KAP was administered in English or the local language. Data were analysed using an established scalar-scoring method in which a score of 1 was assigned to the correct answer/healthy lifestyle and 0 to an incorrect answer/unhealthy lifestyle/practice. Clinical data included fasting blood sugar, blood pressure, retinopathy, and visual acuity. Retinopathy was graded as STDR/NSTDR from retinal images using Early Treatment of Diabetic Retinopathy Study criteria. Usable data from 383 participants (95.8%) were analysed. Of these, 83 (21.7%) had STDR, and 300 (78.3%) had NSTDR. The NSTDR group reported a significantly lower total KAP score (mean rank = 183.4) compared to the STDR group (mean rank = 233.1), z = -3.0, p < 0.001. A significantly greater percentage in the NSTDR group reported to being unaware that diabetes could affect eyes, did not know about possible treatment for DR, and checked their blood sugar less frequently than once a month. Patients who had not developed STDR had poorer KAP about diabetes and diabetes-related eye diseases. This is an important issue to address as the risk of their progressing to STDR is high unless appropriate steps to improve their knowledge/awareness and lifestyle practice are introduced early.

An attempt to evaluate the anterior surface homeostasis in patients with diabetic retinopathy

Aims/Purpose: The aim of our study was to verify the presence of dry eye disease (DED) in diabetic patients. We also tried to determine the relationship between diabetic neuropathy and the presence of ocular surface disease.Methods: 52 type 2 diabetic patients and 52 non‐diabetic, sex and age‐matched controls were included in the study. All participants completed the OSDI questionnaire and undergone the eye surface examination with IDRA analyser including interferometry, tear meniscus height, non‐invasive tear break‐up time (NIBUT), evaluation of blepharitis, blinking pattern, pupillometry and meibography. Corneal sensation in the centre of the cornea and its 4 quadrants was analysed with the Cochet–Bonnet aesthesiometer (CBE). Corneal confocal microscopy images of sub‐basal plexus were evaluated using ACCMetrics. The concentrations of the neurotrophic factors: beta‐NGF, BDNF, NT‐4 as well as inflammatory mediators (TNF‐α, VEGF, IL‐2, ‐1β, ‐4, ‐5, ‐6, ‐8, ‐10, ‐12, GM‐CSF, IFN‐γ) were measured in the tear film using Luminex 200 analytic platform.Results: The OSDI was significantly higher in diabetic patients compared to the controls (mean: 29.08 vs. 11.67 points; p &lt; 0.001). The groups did not differ in the results of the Schirmer test, interferometry, NIBUT and meibography values. Moreover, we did not find the elevation in the concentrations of the ocular surface inflammation indicators in tears of diabetics as compared to controls. There was a significant difference in tear meniscus between groups. Diabetics achieved significantly worse results than the control group in terms of corneal sensation, innervation parameters (average nerve length = 6.70 vs 11.54 mm/mm2; p &lt; 0.001, average density = 9.26 vs. 19.19/mm2; p &lt; 0.001), scotopic pupillometry and blinking interval. Tear film BDNF concentrations were lower in diabetic eyes as compared to controls (mean 1.85 vs. 2.29 pg/mg; p = &lt;0.001).Conclusions: The lack of proinflammatory response in diabetic eyes and no differences between groups in the terms of objective clinical features may suggest that there is no cause‐and‐effect relationship between diabetes and DED. Corneal innervation disorders, clinically noticeable as decreased corneal sensation, result in a number of complications, that have a direct impact on the subjective feelings of the patient.

Efficacy of Anti-VEGF Therapy in Diabetic Macular Edema-Bashundhara Eye Hospital &amp; Research Institute Experience

Efficacy of Anti-VEGF Therapy in Diabetic Macular Edema-Bashundhara Eye Hospital &amp; Research Institute Experience

Incidence of Central Involving Macular Oedema After Cataract Surgery in Diabetic Retinopathy Patients – A Prospective Study

Aim: To study the incidence of central macular oedema at 4, 8 and 12 weeks following cataract surgery in diabetic retinopathy eyes. Materials and Methods: A prospective observational study was conducted over a period of 3 months to investigate the presence of centrally involved macular oedema following cataract surgery in diabetic retinopathy patients. Preoperative OCT scans were performed and patients without centrally involved macular oedema were enrolled. Subfield thickness (central, outer and inner) was measured preoperatively and at 4, 8 and 12 weeks postoperatively. The correlation between diabetic macular oedema (DME) and HbA1c, as well as random blood sugar (RBS) levels at the time of surgery, was analysed. Best.corrected visual acuity (BCVA) was assessed preoperatively and postoperatively, and fundus examinations were conducted each visit to evaluate diabetic retinopathy progression. Results: This study included 80 eyes from 44 patients. Postoperatively, central subfield thickness (CSF) increased significantly at first (14.7 μm), second (26.7 μm) and third (30.3 μm) compared to baseline. Inner subfield thickness (ISF) also increased at first (13.3 μm), second (20.6 μm) and third (24.3 μm) months, while the outer subfield thickness (OSF) showed progression from 6.7 μm to 16.6 μm from the first to third month. A statistically significant shift in DME was observed three months post.cataract surgery. There was a significant association between HbA1c levels and the development of DME, but no correlation with RBS levels. BCVA improved remarkably from preoperative mean log MAR of 0.75 to a mean log MAR of 0.1 at 3 months postoperatively. About 30% had mild non-proliferative diabetic retinopathy (NPDR) preoperatively, and in that group, 60% progressed to moderate NPDR. The status of moderate and severe NPDR remained the same postoperatively at 3 months. Of all patients who progressed to moderate NPDR, 3 cases developed non-central macular oedema, and three cases developed both central and non.central macular oedema postoperatively at 3 months. All eyes with severe NPDR resulted in DME, except for one (9.1%) eye. Conclusion: Preoperatively, 57.5% had no macular oedema, while 42.5% had non.central macular oedema. Postoperatively, 4.4% had central oedema, and 10.9% had both central and non.central oedema among those who initially had no macular oedema. Additionally, 38.2% developed oedema in both central and non.central regions among patients initially presenting with non-central macular oedema. There was also a significant statistical difference with longer duration of diabetes mellitus, severity of diabetic retinopathy and elevated HbA1c values with the development of diabetic macular oedema postoperatively. However, larger sample sizes and longer-term follow-up are essential for assessing retinopathy progression and the incidence of DME.

Empowering healthcare innovation : IoT-enabled smart systems and deep learning for enhanced diabetic retinopathy in the telehealth landscape

Background: Among prevalent medical complications, Diabetic Eye Disease (DED) stands as a significant contributor to vision loss. To forecast its progression and accurately assess the various stages, diverse methodologies have emerged. Machine Learning (ML) and Deep Learning (DL) algorithms have become essential tools in this endeavor, primarily through their adept analysis of Diabetic Retinopathy (DR) images. However, there is still a need for a more efficient and accurate method to predict DR performance. Method: We have developed an innovative method for classifying and predicting diabetic retinopathy. The novel idea in this research is to combine several techniques, including ensemble learning and a 2D convolutional neural network; we utilized transfer learning and a correlation method in our approach. Initially, the Stochastic Gradient Boosting process was employed for predicting diabetic retinopathy. We then used a boosting-based Ensemble Learning method for predicting images of diabetic retinopathy. Next, we applied a 2D Convolutional Neural Network. We successfully employed Transfer Learning to classify different stages of diabetic retinopathy images accurately. This research explores the role of artificial intelligence in identifying and categorizing diabetic retinopathy at an early stage, using techniques such as machine learning and deep learning. It also use techniques like transfer learning, domain adaptation, multitask learning, and explainable AI to accurately classify different stages of diabetic retinopathy images. Our proposed technique achieves impressive results through experiments, with a 97.9% accuracy in forecasting DR images and a 98.1% accuracy in image grading. Additionally, sensitivity and specificity metrics measure 99.3% and 97.6%, respectively. Comparative analysis with existing methods underscores the high predictive accuracy achieved by our proposed approach.

Be mindful of the blood glucose measurement: The relevance of blood glucose in the eye emergency department

Aims/Purpose: Diabetic eye disease is a leading cause of blindness worldwide and diabetes‐related eye conditions are frequent presentations in Eye Emergency Departments (EEDs). Early detection and intervention of inadequate blood glucose control in the EED could be sight‐saving.The aims of this study are to determine the: prevalence of pre‐existing diabetes mellitus (DM) in the EED. prevalence of suboptimal (HbA1c 53–70 mmol/mol) or poor (HbA1c &gt;70 mmol/mol) glycaemic control in the EED. proportion of diabetes patients with this diagnosis documented in the EED. proportion of EED patients at risk of undiagnosed diabetes. number of EED patients that had a capillary blood glucose measurement (BgM). Methods: A retrospective review of 683 patients attending the EED over 10 consecutive weekdays at Manchester Royal Eye Hospital, Manchester, United Kingdom. The following parameters were assessed: EED documentation: patient age, gender, presentation, diagnosis, management plan and whether a BgM was done. General Practitioner records: diabetes diagnosis cross‐referenced with EED documentation. If diabetes patients: diabetes type, duration, and glycaemic control. Potentially undiagnosed diabetes. Results: The prevalence of diabetes patients was 9.5%–64.6% of these patients had diabetes documented in EED and 1 had a capillary BgM. 70.7% of diabetes patients had pre‐existing suboptimal [33.8%] or poor [36.9%] glycaemic control. 8.5% of patients were considered high‐risk for undiagnosed diabetes of which 1 had a BgM.Conclusions: There is a high prevalence of diabetes patients in the EED, and most have inadequate glycaemic control. This makes the EED an important setting to detect patients at high risk of diabetes‐related ocular disease and complications. BgM testing for all suspected patients is a quick test to allow earlier detection and intervention of inadequately controlled diabetes and reduce the risk of diabetes‐related vision loss.

Autonomous AI testing for diabetic eye disease closes care gap and improves health equity on a systems level

We aimed to examine the change in adherence to annual diabetic eye disease (DED) testing in an integrated healthcare system (Johns Hopkins Community Physicians 30+ community based primary care clinics) before and after deployment of autonomous artificial intelligence (AI). Adherence to annual DED testing is defined as completed evaluation by either a human ophthalmology provider, or autonomous AI within a given calendar year. During the COVID pandemic, autonomous AI (IDx‐DR, Digital Diagnostics, Coralville, IA) was deployed at multiple clinics, so that by 2021, some clinics had autonomous AI (AI sites), while others did not (non‐AI sites). Overall adherence in 2019 (pre‐AI) and 2021 (with AI) were compared, and stratified by demographics, using chi‐square or Fisher's exact test. Changes from 2019 to 2021 in odds of adherence within each subgroup were assessed for significant differences by site type (AI vs. non‐AI), using logistic regression with a site type‐by‐time interaction term. From 2019 to 2021, the overall adherence rate increased from 42.6% to 55.5% at AI sites (1949 patients), and increased from 38.0% to 41.0% at non‐AI sites (5379 patients). The increase in overall adherence rate at AI sites was significantly greater than that at non‐AI sites.

Prevalence of diabetic eye diseases among target population in urban area of India

Prevalence of diabetic eye diseases among target population in urban area of India

Towards the preclinical diagnosis of diabetic retinopathy and diabetic macular oedema using tear samples

Aims/Purpose: A wide spectrum of molecular‐genetic events, including inflammation (INF), angiogenesis (ANG), apoptosis (AP) and the concurrence of miRNAs and target genes, are involved in diabetic retinopathy (DR) and diabetic macular oedema (DMO). To unravel the complex pathogenesis of DR and DMO, we focus on the microvascular endothelial dysfunction (MVED) pathophysiologic pathways.Methods: Volunteers of both sexes, aged 35–80 years were enrolled and distributed into type 2 diabetics (T2DM), with DR (+DRG; n = 35), without DR (−DRG; n = 35), with DMO (DMOG; n = 28), and controls (CG; n = 40). Sociodemographic and comorbid data, ophthalmologic parameters, and molecular‐genetic variables related to INF, ANG, and AP, assayed in tear samples, were recorded. Statistics were performed by the R Core Team (2022) program.Results: Most of our participants were middle‐aged (mean age was 58 ± 11 years). Differential expression profile in tears was found statistically significant for SOCS6 gene (involved in INF) target of hsa‐miR‐155‐5p, VEGF‐A gene (implicated in ANG) target of hsa‐miR‐15b‐5p, and the BCL2L2 gene (AP effector) target of hsa‐miR‐10a‐5p, in both the +DRG and DMOG, as compared to the −DRG and CG.Conclusions: We confirm that INF, ANG, and AP, regulated by the hsa‐miR‐155‐5p, hsa‐miR‐15b‐5p and hsa‐miR‐10a‐5p, and its respective target genes SOCS6, VEGF‐A, and BCL2L2, expressed in tears, are key players in the development of structural and functional chorioretinal changes associated with MVED in DR/DMO patients. We may propose the above insights as potential diagnostic‐prognostic biomarkers for better managing the diabetic eyes and vision.

Evaluation of the Current Care Pathway for Diabetic Eye Screening in Malta

Evaluation of the Current Care Pathway for Diabetic Eye Screening in Malta

Neurodegeneration in diabetic retinopathy: An insight on its main signalling pathways

Aims/Purpose: In the latest years neurodegeneration has been considered as one of the main pathological processes involved in proliferative diabetic retinopathy's (PDR) onset and progression. We aimed to increase the understanding on the role of the main signalling molecules that participate in this phenomenon, including apoptotic, excitotoxic and neurotrophic factor pathways.Methods: Participants of both sexes were selected and programmed for vitrectomy by its diabetes mellitus 2 (DM2)‐derived vitreoretinal complications (PDR group, PDRG; n = 20), or by suffering macular hole, epiretinal membrane or rhegmatogenous retinal detachment (as the surrogate control group, SCG; n = 26). Vitreous body (VIT) and plasma (PLS) samples were collected and processed to determine the glutamate, caspase‐3 (CAS‐3) and brain‐derived neurotrophic factor (BDNF) levels. Next‐Generation Sequencing (NGS) techniques were applied to characterize the miRNA expression pattern in both groups.Results: Significantly higher VIT glutamate (p &lt; 0.05) and CAS‐3 (p &lt; 0.05), and significantly lower BDNF (p &lt; 0.05) levels were detected in the PDRG. Significantly higher PLS glutamate (p &lt; 0.05) and CAS‐3 (p &lt; 0.05) levels were found in diabetics. However, PLS levels of BDNF showed no differences between groups (p = 0.80). NGS analysis showed a different miRNA expression pattern between both groups.Conclusions: Diabetes‐induced changes in retinal neurons/glia may be the key to discover preclinical biomarkers for better managing diabetic eyes. Glutamate, caspase‐3 and BDNF, playing important roles in the neurodegenerative processes occurring in PDR, are pivotal candidates to identify diabetics at high risk of sight‐threatening complications. Additionally, certain miRNAs could be playing an important role in these signalling pathways, being involved in PDR's neurodegenerative phenomenon.

DARC technology: Where do we stand?

The emergence of neuroprotection in glaucoma has emphasized the importance of new endpoints to assess disease and identify those at greatest risk of blindness. Several potential outcome measures have been advocated, including immunological, genetic, structural and functional biomarkers. The identification of in vivo apoptosis could serve as a potential biomarker of disease activity. The technology that has been developed to detect apoptosing retinal cells (DARC) is now being used to assess treatment efficacy in glaucoma, wet AMD, geographic atrophy and diabetic eye disease. It is currently used as an exploratory endpoint with future plans for its development as a clinically approved endpoint.

DeepDiabetic: An Identification System of Diabetic Eye Diseases Using Deep Neural Networks

DeepDiabetic: An Identification System of Diabetic Eye Diseases Using Deep Neural Networks