Age-related Macular Degeneration Grading Research Articles

Risk Factors of Age-Related Macular Degeneration in a Population-Based Study: Results from SHIP-TREND-1 (Study of Health in Pomerania-TREND-1).

BACKGROUND Age-related macular degeneration (AMD) is the most common cause of visual impairment in the elderly population in industrialized countries. The Study of Health in Pomerania (SHIP) with its cohort SHIP-TREND was designed to investigate risk factors and clinical disorders in the general population of northeast Germany. This work focused on the first follow-up of SHIP-TREND and determined associated modifiable risk factors of AMD. Modifying risk factors is important to slow the progression of early AMD as there is currently no treatment for the late stage of geographic atrophy. Understanding AMD-associated risk factors also plays an important role in the development of therapeutic concepts. MATERIAL AND METHODS Between 2016 and 2019, data were collected from a total of 2507 initially randomly selected subjects from the general population aged 28 to 89 years. Non-mydriatic fundus photography of the right eye was performed in 2489 subjects. Grading of AMD was performed using the Rotterdam classification system. RESULTS We included 1418 gradable fundus photographs in the analysis. The risk of AMD changes increased with age and was positively correlated with HDL cholesterol, fT3, and low educational level. In men, BMI and cigarette smoking were also positively associated with AMD changes. CONCLUSIONS This study emphasizes the consideration of various metabolic pathways for the development of therapeutic concepts.

Implementing and evaluating a fully functional AI-enabled model for chronic eye disease screening in a real clinical environment.

Artificial intelligence (AI) has the potential to increase the affordability and accessibility of eye disease screening, especially with the recent approval of AI-based diabetic retinopathy (DR) screening programs in several countries. This study investigated the performance, feasibility, and user experience of a seamless hardware and software solution for screening chronic eye diseases in a real-world clinical environment in Germany. The solution integrated AI grading for DR, age-related macular degeneration (AMD), and glaucoma, along with specialist auditing and patient referral decision. The study comprised several components: (1) evaluating the entire system solution from recruitment to eye image capture and AI grading for DR, AMD, and glaucoma; (2) comparing specialist's grading results with AI grading results; (3) gathering user feedback on the solution. A total of 231 patients were recruited, and their consent forms were obtained. The sensitivity, specificity, and area under the curve for DR grading were 100.00%, 80.10%, and 90.00%, respectively. For AMD grading, the values were 90.91%, 78.79%, and 85.00%, and for glaucoma grading, the values were 93.26%, 76.76%, and 85.00%. The analysis of all false positive cases across the three diseases and their comparison with the final referral decisions revealed that only 17 patients were falsely referred among the 231 patients. The efficacy analysis of the system demonstrated the effectiveness of the AI grading process in the study's testing environment. Clinical staff involved in using the system provided positive feedback on the disease screening process, particularly praising the seamless workflow from patient registration to image transmission and obtaining the final result. Results from a questionnaire completed by 12 participants indicated that most found the system easy, quick, and highly satisfactory. The study also revealed room for improvement in the AMD model, suggesting the need to enhance its training data. Furthermore, the performance of the glaucoma model grading could be improved by incorporating additional measures such as intraocular pressure. The implementation of the AI-based approach for screening three chronic eye diseases proved effective in real-world settings, earning positive feedback on the usability of the integrated platform from both the screening staff and auditors. The auditing function has proven valuable for obtaining efficient second opinions from experts, pointing to its potential for enhancing remote screening capabilities. Institutional Review Board of the Hamburg Medical Chamber (Ethik-Kommission der Ärztekammer Hamburg): 2021-10574-BO-ff.

Retinal pigment epithelium-Bruch's membrane volume in grading of age-related macular degeneration.

To assess the agreement of optical coherence tomography (OCT) algorithm-based retinal pigment epithelium -Bruch's membrane complex volume (RBV) with fundus photograph-based age-related macular degeneration (AMD) grading. Digital color fundus photographs (CFPs) and spectral domain OCT images were acquired from 96 elderly subjects. CFPs were graded according to Age-Related Eye Disease Study (AREDS) classification. OCT image segmentation and RBV data calculation were done with Orion™ software. Univariate and multivariate analyses were performed to find out whether AMD lesion features associated with higher RBVs. RBV correlated with AMD grading (rs=0.338, P=0.001), the correlation was slightly stronger in early AMD (n=52; rs=0.432, P=0.001). RBV was higher in subjects with early AMD compared with those with no AMD lesions evident in fundus photographs (1.05±0.20 vs 0.96±0.13 mm3, P=0.023). In multivariate analysis higher RBVs were associated significantly with higher total drusen (β=0.388, P=0.027) and pigmentation areas (β=0.319, P=0.020) in fundus photographs, whereas depigmentation area (β=-0.295, P=0.015) associated with lower RBV. RBV correlate with AMD grading status, with a stronger association in patients with moderate, non-late AMD grades. This effect is driven mostly by lesions with drusen or pigmentation. Lesions with depigmentation tend to have lower values. RBV is more comprehensive measurement of the key area of AMD pathogenesis, compared to sole drusen volume analysis. RBV measurements are independent on grader variations and offer a possibility to quantify early and middle grade AMD lesions in a research setting, but may not substitute fundus photograph-based grading in the whole range of AMD spectrum.

Vitreomacular interface abnormalities in the Ghanaian African

Background/ObjectiveDescribe vitreomacular interface abnormalities (VMIA) using spectral-domain optical coherence tomography (SD-OCT), and correlations with age-related macular degeneration (AMD) grade in Ghanaian Africans.Subjects/MethodsProspective, cross-sectional study of adults aged ≥50 years recruited in Ghana AMD Study. Participant demographics, medical histories, ophthalmic examination, digital colour fundus photography (CFP) were obtained. High-resolution five-line raster OCT, Macular Cube 512 × 128 scans, and additional line scans in areas of clinical abnormality, were acquired. SD-OCT VMI features classified by International Vitreomacular Traction Study Group system and relationships to AMD grade were evaluated. Outcomes: VMIA prevalence, posterior vitreous detachment (PVD), vitreomacular adhesions (VMA), vitreomacular traction (VMT), epiretinal membranes (ERM), correlations with AMD grade.ResultsThe full Ghana AMD cohort included 718 participants; 624 participants (1248 eyes) aged ≥50 years (range = 50–101, mean = 68.8), 68.9% female were included in this analysis. CFP with OCT scans were available for 776 eyes (397 participants); 707 (91.1%) had gradable CFP and OCT scans for both AMD and VMI grading forming the dataset for this report. PVD was absent in 504 (71.3%); partial and complete PVD occurred in 16.7% and 12.0% respectively. PVD did not increase with age (p = 0.720). VMIA without traction and macular holes were observed in 12.2% of eyes; 87.8% had no abnormalities. VMIA was not significantly correlated with AMD grade (p = 0.819).ConclusionsThis provides the first assessment of VMIA in Ghanaian Africans. VMIA are common in Africans; PVD may be less common than in Caucasians. There was no significant association of AMD grade with VMIA.

Scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images

Age-related Macular Degeneration (AMD), a retinal disease that affects the macula, can be caused by aging abnormalities in number of different cells and tissues in the retina, retinal pigment epithelium, and choroid, leading to vision loss. An advanced form of AMD, called exudative or wet AMD, is characterized by the ingrowth of abnormal blood vessels beneath or into the macula itself. The diagnosis is confirmed by either fundus auto-fluorescence imaging or optical coherence tomography (OCT) supplemented by fluorescein angiography or OCT angiography without dye. Fluorescein angiography, the gold standard diagnostic procedure for AMD, involves invasive injections of fluorescent dye to highlight retinal vasculature. Meanwhile, patients can be exposed to life-threatening allergic reactions and other risks. This study proposes a scale-adaptive auto-encoder-based model integrated with a deep learning model that can detect AMD early by automatically analyzing the texture patterns in color fundus imaging and correlating them to the vasculature activity in the retina. Moreover, the proposed model can automatically distinguish between AMD grades assisting in early diagnosis and thus allowing for earlier treatment of the patient’s condition, slowing the disease and minimizing its severity. Our model features two main blocks, the first is an auto-encoder-based network for scale adaption, and the second is a convolutional neural network (CNN) classification network. Based on a conducted set of experiments, the proposed model achieves higher diagnostic accuracy compared to other models with accuracy, sensitivity, and specificity that reach 96.2%, 96.2%, and 99%, respectively.

Simultaneous screening and classification of diabetic retinopathy and age-related macular degeneration based on fundus photos-a prospective analysis of the RetCAD system.

To assess the accuracy of an artificial intelligence (AI) based software (RetCAD, Thirona, The Netherlands) to identify and grade age-related macular degeneration (AMD) and diabetic retinopathy (DR) simultaneously based on fundus photos. This prospective study included 1245 eyes of 630 patients attending an ophthalmology day-care clinic. Fundus photos were acquired and parallel graded by the RetCAD AI software and by an expert reference examiner for image quality, and staging of AMD and DR. Adjudication was provided by a second expert examiner in case of disagreement between the AI software and the reference examiner. Statistical analysis was performed on eye-level and on patient-level, by summarizing the individual image level-gradings into and eye-level or patient-level score, respectively. The performance of the RetCAD system was measured using receiver operating characteristics (ROC) analysis and sensitivity and specificity for both AMD and DR were reported. The RetCAD achieved an area under the ROC (Az) of 0.926 with a sensitivity of 84.6% at a specificity of 84.0% for image quality. On image level, the RetCAD software achieved Az values of 0.964 and 0.961 with sensitivity/specificity pairs of 98.2%/79.1% and 83.9%/93.3% for AMD and DR, respectively. On patient level, the RetCAD software achieved Az values of 0.960 and 0.948 with sensitivity/specificity pairs of 97.3%/73.3% and 80.0%/90.1% for AMD and DR, respectively. After adjudication by the second expert examiner sensitivity/specificity increases on patient-level to 98.6%/78.3% and 100.0%/92.3% for AMD and DR, respectively. The RetCAD offers very good sensitivity and specificity compared to manual grading by experts and is in line with that obtained by similar automated grading systems. The RetCAD AI software enables simultaneous grading of both AMD and DR based on the same fundus photos. Its sensitivity may be adjusted according to the desired acceptable sensitivity and specificity. Its simplicity cloud base integration allows cost-effective screening where routine expert evaluation may be limited.

Role of Optical Coherence Tomography Imaging in Predicting Progression of Age-Related Macular Disease: A Survey.

In developed countries, age-related macular degeneration (AMD), a retinal disease, is the main cause of vision loss in the elderly. Optical Coherence Tomography (OCT) is currently the gold standard for assessing individuals for initial AMD diagnosis. In this paper, we look at how OCT imaging can be used to diagnose AMD. Our main aim is to examine and compare automated computer-aided diagnostic (CAD) systems for diagnosing and grading of AMD. We provide a brief summary, outlining the main aspects of performance assessment and providing a basis for current research in AMD diagnosis. As a result, the only viable alternative is to prevent AMD and stop both this devastating eye condition and unwanted visual impairment. On the other hand, the grading of AMD is very important in order to detect early AMD and prevent patients from reaching advanced AMD disease. In light of this, we explore the remaining issues with automated systems for AMD detection based on OCT imaging, as well as potential directions for diagnosis and monitoring systems based on OCT imaging and telemedicine applications.

Genomic-Metabolomic Associations Support the Role of LIPC and Glycerophospholipids in Age-Related Macular Degeneration.

Purpose:Large-scale genome-wide association studies (GWAS) have reported important single nucleotide polymorphisms (SNPs) with significant associations with age-related macular degeneration (AMD). However, their role in disease development remains elusive. This study aimed to assess SNP–metabolite associations (i.e., metabolite quantitative trait loci [met-QTL]) and to provide insights into the biological mechanisms of AMD risk SNPs.Design:Cross-sectional multicenter study (Boston, Massachusetts, and Coimbra, Portugal).Participants:Patients with AMD (n = 388) and control participants (n = 98) without any vitreoretinal disease (> 50 years).Methods:Age-related macular degeneration grading was performed using color fundus photographs according to the Age-Related Eye Disease Study classification scheme. Fasting blood samples were collected and evaluated with mass spectrometry for metabolomic profiling and Illumina OmniExpress for SNPs profiling. Analyses of met-QTL of endogenous metabolites were conducted using linear regression models adjusted for age, gender, smoking, 10 metabolite principal components (PCs), and 10 SNP PCs. Additionally, we analyzed the cumulative effect of AMD risk SNPs on plasma metabolites by generating genetic risk scores and assessing their associations with metabolites using linear regression models, accounting for the same covariates. Modeling was performed first for each cohort, and then combined by meta-analysis. Multiple comparisons were accounted for using the false discovery rate (FDR).Main Outcome Measures:Plasma metabolite levels associated with AMD risk SNPs.Results:After quality control, data for 544 plasma metabolites were included. Meta-analysis of data from all individuals (AMD patients and control participants) identified 28 significant met-QTL (β = 0.016−0.083; FDR q-value < 1.14 × 10−2), which corresponded to 5 metabolites and 2 genes: ASPM and LIPC. Polymorphisms in the LIPC gene were associated with phosphatidylethanolamine metabolites, which are glycerophospholipids, and polymorphisms in the ASPM gene with branched-chain amino acids. Similar results were observed when considering only patients with AMD. Genetic risk score–metabolite associations further supported a global impact of AMD risk SNPs on the plasma metabolome.Conclusions:This study demonstrated that genomic–metabolomic associations can provide insights into the biological relevance of AMD risk SNPs. In particular, our results support that the LIPC gene and the glycerophospholipid metabolic pathway may play an important role in AMD, thus offering new potential therapeutic targets for this disease.

Characterization of age-related macular degeneration in Indian donor eyes.

Purpose:The purpose of this study was to test the reliability of fundus stereomicroscopy in postmortem eyes to assign severity of age-related macular degeneration (AMD) using the Minnesota grading and confirmation by histology using Alabama and Sarks grading scales and to assess the incidence of AMD pathology in donor eyes from a South Indian population.Methods:Eyes (199) from 153 donors (55–95 years) after obtaining fundus images were processed for histology. Fundus images were graded according to the Minnesota grading system based on drusen size, area of depigmentation, and atrophy. At least one eye from each donor displaying the AMD phenotypes were subjected to histological examination. The fundus grading was correlated with histology and the stages of AMD assigned for early AMD by the Alabama AMD grading system and for both early and advanced AMD by the Sarks classification.Results:Stereoscopic examination of the fundus found that 10 of the 153 donors had features of early AMD and 3 advanced AMD. Following histological examination, one of the early AMD eyes was reclassified as advanced AMD. Early AMD features that were observed on histology included soft drusen (>63 μm), basal laminar deposits, photoreceptor outer segment degeneration, disorganization of retinal pigment epithelium (RPE), Bruch's membrane thickening. Advanced AMD features observed in histology are extensive atrophy of RPE, choroidal neovascularization and disciform scar formation.Conclusion:Identification of either early or advanced AMD using stereomicroscopic assessment (SMA) showed high sensitivity and specificity. However, misclassification between AMD stages can occur when only SMA is used.

Association of Drusen Phenotype in Age-Related Macular Degeneration from Human Eye-Bank Eyes to Disease Stage and Cause of Death

To stage maculopathy, assess and quantify drusen, determine drusen subtype frequency, and compare subtypes with age-related macular degeneration (AMD) stage and cause of death using an eye-bank model of AMD. Cross-sectional study. Two thousand ninety-two human eyes from 1067 eye-bank donors, selected from a population at risk for AMD. We analyzed donor eye tissue images (2005-2020) using both the 4- and 9-step Minnesota Grading System (MGS), an AMD grading system for eye-bank eyes corresponding to the Age-Related Eye Disease Study classification. The 9-step MGS quantifies total drusen area, hyperpigmentation, and depigmentation. We analyzed reticular pseudodrusen (RPD), basal laminar drusen (BLD), and calcified drusen (CaD) frequency within this population and explored associations with AMD stage, donor age, gender, and cause of death. Statistical analyses were performed using Wilcoxon rank-sum and chi-square tests. Testing encompassed staging eye-bank eyes using MGS analysis. Drusen subtype frequency associations with AMD stage and cause of death. We detected RPD in 228 (13%), BLD in 131 (7%), and CaD in 84 (5%) of the examined eyes (n=1777). All subtypes were associated with advanced AMD (RPD: odds ratio [OR], 3.4 [95% confidence interval (CI), 2.5-4.5; P < 0.0001]; BLD: OR, 2.2 [95% CI, 1.5-3.2; P < 0.0001]; and CaD: OR, 39.1 [95% CI, 16.8-91.0; P< 0.0001]). Only the RPD subtype was associated statistically with cardiovascular death when compared with those without cardiovascular death (48% vs. 32%; OR, 2.0 [95% CI, 1.4-2.9]; P= 0.0002). In a large group of eye-bank eyes selected from a population at risk for AMD and graded using the 4-step and 9-step MGS, RPD, BLD, and especially CaD were associated strongly with advanced AMD. The RPD subtype was associated with a cardiovascular cause of death and may represent an ophthalmologic biomarker for cardiovascular disease.

Age-related macular degeneration (AMD): More than meets the eye. The role of multimodal imaging in today's management of AMD-A review.

Multimodal imaging (MMI) allows a more granular grading of age-related macular degeneration (AMD) disease severity, with many novel risk factors having been recently identified. With this imaging information, we are better able to counsel our patients with more accurate and individualized progression scenarios. MMI also allows identification of anatomical features that increase our understanding of disease processes involved in progression to late AMD. Treatment protocols for neovascular AMD (nAMD) depend largely on the optical coherence tomography (OCT) appearance to determine disease activity, which allows us to individualize treatment. In geographic atrophy (GA), new intervention trials require the ability to define the extent of GA, so that GA growth rate can be determined. This is achieved through fundus autofluorescence (FAF) imaging, which allows greater accuracy of border identification, as well as revealing FAF patterns predictive of growth rates. As we strive to bring interventions earlier in the disease course, OCT imaging provides an ability to identify the first signs of atrophy, which may serve as novel surrogate biomarkers for GA, thereby facilitating trials. In the future, the use of artificial intelligence (AI) to automatically identify relevant features on MMI could further enhance our ability to determine disease severity, predict progression and assist in identifying disease activity parameters to support clinical decision making when treating nAMD. Newer developments may allow frequent, remote capturing of images, reducing clinic visits, detecting progression and monitoring neovascular activity in-between clinic visits. Being aware of these new imaging insights in AMD, greatly enhance our clinical management of AMD.

Six-Year Incidence and Risk Factors for Age-Related Macular Degeneration in a Rural Chinese Population: The Handan Eye Study.

To describe the 6-year incidence of early and late age-related macular degeneration (AMD) and its associated factors in a representative large rural Chinese population. A population-based longitudinal study was conducted in rural China from 2006 to 2007. In total, 6830 persons aged 30 years or older participated in the study. The 6-year follow-up study was performed between 2012 and 2013. The modified Wisconsin Age-Related Maculopathy Grading System (WARMGS) protocol in the Blue Mountains Eye Study was used as the AMD grading standard. Excluding 509 deceased subjects, 5394 (follow-up rate 85.3%) completed the follow-up. Among them, 5048 participants had gradable photographs of at least one eye at both examinations. The incidence of early and late AMD over 6 years was 4.2% (95% CI, 3.8%-4.7%) and 0.2% (95% CI, 0.2%-0.3%), respectively. In the multivariable analysis, per-year increase in age (P < 0.001; OR = 1.06; 95% CI, 1.04-1.07), male sex (P = 0.006; OR = 0.64; 95% CI, 0.47-0.88), and per-millimeter increase in axial length (P = 0.010; OR = 0.78; 95% CI, 0.63-0.94) at baseline were significantly associated with incident early AMD. Early AMD was not associated with systolic blood pressure, diastolic blood pressure, hypertension, diabetes, history of stroke, history of heart disease, body mass index, total cholesterol, high density lipoprotein cholesterol, low density lipoprotein cholesterol, triglycerides, smoking status, refractive error, or corneal curvature radius. There were too few cases of late AMD for a valid statistical analysis of the risk factors. The incidence of early and late AMD over 6 years in a rural Chinese population was 4.2% (95% CI, 3.8%-4.7%) and 0.2% (95% CI, 0.2%-0.3%), respectively. Age, sex, and axial length are relevant risk factors for early AMD in rural China.

Prevalence of age-related macular degeneration in Chinese populations worldwide: A systematic review and meta-analysis.

Age-related macular degeneration (AMD) is a leading cause of irreversible blindness, but the magnitude of AMD among Chinese populations worldwide is still unknown. To investigate the prevalence of AMD in Chinese populations worldwide. Meta-analysis. Nine studies with 29 344 subjects in total. All population-based studies on AMD prevalence in Chinese populations worldwide were identified and only studies using standardized AMD grading systems (Wisconsin Age-Related Maculopathy Grading System, Age-Related Eye Disease Study System of Classifying AMD, the International Classification and Grading System for AMD or the Clinical Classification of Age-Related Macular Degeneration) were included. We used meta-analysis to estimate the pooled prevalence and its 95% confidence interval (95% CI) of AMD, and to explore the racial differences and regional differences. Age-specific prevalence, gender-specific prevalence and pooled prevalence of early and late AMD among Chinese population worldwide. Altogether, 9 studies with 29 344 individuals were included and analysed. The crude pooled prevalence of early and late AMD among Chinese populations worldwide aged 50 years and above is 4.9% (95% CI: 3.1%-7.7%) and 0.7% (95% CI: 0.5%-1.1%), respectively. Corresponding crude prevalence among Caucasian populations are 10.1% (95% CI: 5.7%-17.2%) and 1.6% (95% CI: 1.0%-2.4%). There are statistically significant differences within age and gender subgroups. Among persons aged 50+ years, both early AMD and late AMD in Chinese populations worldwide were less common compared with that reported from Caucasian populations. Considering the significant racial or ethnic differences in AMD prevalence between Chinese and Caucasian people, further studies are needed to explore the possible mechanism behind this discrepancy.

Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk Among Patients With Age-Related Macular Degeneration

Although deep learning (DL) can identify the intermediate or advanced stages of age-related macular degeneration (AMD) as a binary yes or no, stratified gradings using the more granular Age-Related Eye Disease Study (AREDS) 9-step detailed severity scale for AMD provide more precise estimation of 5-year progression to advanced stages. The AREDS 9-step detailed scale's complexity and implementation solely with highly trained fundus photograph graders potentially hampered its clinical use, warranting development and use of an alternate AREDS simple scale, which although valuable, has less predictive ability. To describe DL techniques for the AREDS 9-step detailed severity scale for AMD to estimate 5-year risk probability with reasonable accuracy. This study used data collected from November 13, 1992, to November 30, 2005, from 4613 study participants of the AREDS data set to develop deep convolutional neural networks that were trained to provide detailed automated AMD grading on several AMD severity classification scales, using a multiclass classification setting. Two AMD severity classification problems using criteria based on 4-step (AMD-1, AMD-2, AMD-3, and AMD-4 from classifications developed for AREDS eligibility criteria) and 9-step (from AREDS detailed severity scale) AMD severity scales were investigated. The performance of these algorithms was compared with a contemporary human grader and against a criterion standard (fundus photograph reading center graders) used at the time of AREDS enrollment and follow-up. Three methods for estimating 5-year risk were developed, including one based on DL regression. Data were analyzed from December 1, 2017, through April 15, 2018. Weighted κ scores and mean unsigned errors for estimating 5-year risk probability of progression to advanced AMD. This study used 67 401 color fundus images from the 4613 study participants. The weighted κ scores were 0.77 for the 4-step and 0.74 for the 9-step AMD severity scales. The overall mean estimation error for the 5-year risk ranged from 3.5% to 5.3%. These findings suggest that DL AMD grading has, for the 4-step classification evaluation, performance comparable with that of humans and achieves promising results for providing AMD detailed severity grading (9-step classification), which normally requires highly trained graders, and for estimating 5-year risk of progression to advanced AMD. Use of DL has the potential to assist physicians in longitudinal care for individualized, detailed risk assessment as well as clinical studies of disease progression during treatment or as public screening or monitoring worldwide.

Association of obesity and age-related macular degeneration in Indian population.

Purpose:The aim of this study was to establish the prevalence and association of age-related macular degeneration (AMD) and obesity which was not studied extensively in Indian population over 60 years of age.Methods:This was a cross-sectional, population-based study. A total of 4791 patients with gradable fundus photography were included. All patients underwent detailed ophthalmic examination and AMD was graded with retinal photographs. Grading of AMD was done according to the International ARM Epidemiological Study Group and staged based on grading in worse eye. The association of AMD severity and obesity (based on body mass index, waist–hip ratio, waist circumference, isolated abdominal obesity, isolated generalized obesity, and combined obesity) was assessed. The main outcome variable was an association between the presence and severity of AMD with different grades of obesity.Results:No direct significant association was noted between the presence and severity of AMD and any obesity indices. Subgroup analyses based on lifestyle patterns and common systemic pathologies in AMD population were done. Late AMD was significantly associated with tobacco consumption in population with combined obesity (P = 0.033 and odds ratio = 2.998).Conclusion:No direct association was noted between the presence or severity of AMD and obesity in South Indian population. However, indirect associations between the severity of AMD and combined obesity were found.

Genetic risk factors for late age-related macular degeneration in India

Background/AimsThere are limited data from India on genetic variants influencing late age-related macular degeneration (AMD). We have previously reported associations from a population-based study in India (the India age-related eye...

The Relationship Between Plasma Concentrations of Lutein and Zeaxanthin with Self-Reported and Actual Prevalence of AMD in an Irish Population-Based Sample

ABSTRACTPurpose: To investigate plasma lutein (L) and zeaxanthin (Z) concentrations with grading-confirmed and self-reported prevalence of age-related macular degeneration (AMD).Material and methods: Data collected from a nationally representative prospective cohort study of community-dwelling adults aged 50 years and over in the Republic of Ireland. Participants underwent a computer-assisted personal interview and a center-based health assessment. Plasma concentrations of L and total Z (Z and meso-zeaxanthin [MZ]) were measured by high performance liquid chromatography, and retinal photographs were graded using a version of the AMD International Classification and Grading System. Consumption of supplements containing L and/or Z and/or MZ was recorded as supplement use. Four groups were identified: Group 1 (n = 24): AMD-afflicted and correctly aware; Group 2 (n = 264): AMD-afflicted but unaware; Group 3 (n = 41): AMD-free and incorrectly believed that they were afflicted with the condition; Group 4 (n = 4094): AMD-free and correctly self-reported absence of AMD.Results: Of 4,423 participants with plasma concentrations of L and Z and gradable retinal photographs, 288 (6.5%) were afflicted with AMD, and 65 (1.5%) self-reported AMD. Controlling for family history and age, the relationship between grading-confirmed AMD and plasma L was positive and significant (p < 0.001). Mean plasma concentrations of L in Group 2 (mean = 0.2162 ± 0.132 µmol) and Group 4 (mean = 0.2040 ± 0.121 µmol/L) were significantly lower than Group 1 (mean = 0.4691 ± 0.0.372 µmol/L) and Group 3 (mean = 0.3176 ± 0.0.235 µmol/L). Supplement use was reported by 41.7% and 17.1% of participants in Groups 1 and 3, respectively, but only 2.7% and 1.9% of participants in Groups 2 and 4, respectively.Conclusion: A belief that one suffers from AMD, whether justified or not, is associated with supplement use and with higher plasma concentrations of L.

Novel grid combined with peripheral distortion correction for ultra-widefield image grading of age-related macular degeneration.

PurposeEyes with age-related macular degeneration (AMD) often harbor pathological changes in the retinal periphery and perimacular region. These extramacular changes have not been well classified, but may be phenotypically and functionally relevant. The purpose of this study was to demonstrate a novel grid to systematically study peripheral retinal abnormalities in AMD using geometric distortion-corrected ultra-widefield (UWF) imaging.MethodsThis is a cross-sectional observational case series. Consecutive patients with AMD without any other coexisting vitreoretinal disease and control patients over age 50 without AMD or any other vitreoretinal disease were imaged using Optos 200 Tx. Captured 200° UWF images were corrected for peripheral geometric distortion using Optos transformation software. A newly developed grid to study perimacular and peripheral abnormalities in AMD was then projected onto the images.ResultsPeripheral and perimacular changes such as drusen, retinal pigment epithelium changes and atrophy were found in patients with AMD. The presented grid in conjunction with geometric distortion-corrected UWF images allowed for systematic study of these peripheral changes in AMD.ConclusionWe present a novel grid to study peripheral and posterior pole changes in AMD. The grid is unique in that it adds a perimacular zone, which may be important in characterizing certain phenotypes in AMD. Our UWF images were corrected for geometric peripheral distortion to accurately reflect the anatomical dimensions of the retina. This grid offers a reliable and reproducible foundation for the exploration of peripheral retinal pathology associated with AMD.

Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks

Age-related macular degeneration (AMD) affects millions of people throughout the world. The intermediate stage may go undetected, as it typically is asymptomatic. However, the preferred practice patterns for AMD recommend identifying individuals with this stage of the disease to educate how to monitor for the early detection of the choroidal neovascular stage before substantial vision loss has occurred and to consider dietary supplements that might reduce the risk of the disease progressing from the intermediate to the advanced stage. Identification, though, can be time-intensive and requires expertly trained individuals. To develop methods for automatically detecting AMD from fundus images using a novel application of deep learning methods to the automated assessment of these images and to leverage artificial intelligence advances. Deep convolutional neural networks that are explicitly trained for performing automated AMD grading were compared with an alternate deep learning method that used transfer learning and universal features and with a trained clinical grader. Age-related macular degeneration automated detection was applied to a 2-class classification problem in which the task was to distinguish the disease-free/early stages from the referable intermediate/advanced stages. Using several experiments that entailed different data partitioning, the performance of the machine algorithms and human graders in evaluating over 130 000 images that were deidentified with respect to age, sex, and race/ethnicity from 4613 patients against a gold standard included in the National Institutes of Health Age-related Eye Disease Study data set was evaluated. Accuracy, receiver operating characteristics and area under the curve, and kappa score. The deep convolutional neural network method yielded accuracy (SD) that ranged between 88.4% (0.5%) and 91.6% (0.1%), the area under the receiver operating characteristic curve was between 0.94 and 0.96, and kappa coefficient (SD) between 0.764 (0.010) and 0.829 (0.003), which indicated a substantial agreement with the gold standard Age-related Eye Disease Study data set. Applying a deep learning-based automated assessment of AMD from fundus images can produce results that are similar to human performance levels. This study demonstrates that automated algorithms could play a role that is independent of expert human graders in the current management of AMD and could address the costs of screening or monitoring, access to health care, and the assessment of novel treatments that address the development or progression of AMD.

Six-Year Incidence of Age-Related Macular Degeneration in Asian Malays: The Singapore Malay Eye Study

To determine the 6-year incidence of early and late age-related macular degeneration (AMD) in a Singaporean Malay population and to validate the Age-Related Eye Disease Study (AREDS) simplified severity scale in Asians. Prospective, population cohort study. The Singapore Malay Eye Study baseline participants (age, ≥40 years; 2006-2008) were followed up in 2011 through 2013, and 1901 of 3280 of eligible participants (72.1%) took part. Fundus photographs were graded using the Wisconsin AMD grading system. Incidence of early and late AMD. Gradable fundus photographs were available for 1809 participants who attended both baseline and 6-year follow-up examinations. The age-standardized incidences of early and late AMD were 5.89% (95% confidence interval [CI], 4.81-7.16) and 0.76% (95% CI, 0.42-1.29), respectively. The 5-year age-standardized incidence of early AMD (calculated based on the 6-year incidence) was lower in our population (5.58%; 95% CI, 4.43-7.01) compared with the Beaver Dam Eye Study population (8.19%). The incidence of late AMD in our population was similar to that of the Beaver Dam Eye Study population (0.98% [95% CI, 0.49-1.86] vs. 0.91%), the Blue Mountains Eye Study population (1.10% [95% CI, 0.52-9.56] vs. 1.10%), and the Hisayama Study population (1.09% [95% CI, 0.54-4.25] vs. 0.84%). The incidence of late AMD increased markedly with increasing baseline AREDS score (step 0, 0.23%; step 4, 9.09%). This study documented the incidence of early and late AMD in a Malay population. The AREDS simplified severity scale is useful in predicting the risk of late AMD development in Asians.