Biomarkers For Age-related Macular Degeneration Research Articles

Retinal Pigment Epithelium Curvature Can Predict Late Age-Related Macular Degeneration.

Outer retinal band integrity strongly predicts late age-related macular degeneration (AMD). However, it is often assessed subjectively as "continuity" using inconsistent definitions. Alternatively, "curvature" of the outer retinal bands is a quantitative metric that strongly correlates with AMD biomarkers and can screen for intermediate AMD. We evaluated the prognostic ability of retinal pigment epithelium (RPE) and ellipsoid zone (EZ) curvature for late AMD against outer retinal band continuity, pigmentary abnormalities, reticular pseudodrusen, and drusen volume. Consecutive patients with intermediate AMD who progressed to late AMD (n = 17) or remained stable (n = 42) were recruited. RPE and EZ curvature were quantified as a ratio of their lengths over Bruch's membrane using the sinuosity method of assessing river curvature, where a ratio of ∼1 indicates no outer retinal pathology. RPE, EZ, and Bruch's membrane were manually segmented and their lengths automatically extracted. The primary outcomes were outer retinal sinuosity and the odds ratio of predicting late AMD. Mean follow-up time for progressors and nonprogressors was 4.4 and 3.6 years. RPE sinuosity was strongly associated with pigmentary abnormalities (P = 0.001) and drusen volume (P = 0.004) but not reticular pseudodrusen (P = 0.28). RPE sinuosity >1.03 was the strongest predictor of late AMD developing within 5 years (15 [2.9-75]) and across the study period (25 [2.3-282]). Drusen volume >0.03 mm3 was the strongest predictor of progression within 2 years (33 [2.5-426]), and RPD could not independently predict progression within any time frame. RPE curvature is a promising, quantitative outer retinal biomarker that can prognosticate late AMD and potentially enhance prognostic models.

Identification of the CDH18 gene associated with age-related macular degeneration using weighted gene co-expression network analysis.

Purpose: Age-related macular degeneration (AMD) is a chronic and progressive macular degenerative disease that culminates in a gradual deterioration of central vision. Despite its prevalence, the key biomarkers for AMD have not yet been fully elucidated. In this study, we aimed to efficiently identify biomarkers crucial for diagnosing AMD. Methods: Three datasets pertaining to retinal pigment epithelium (RPE)/choroid tissues associated with AMD were selected from the GEO database. The GSE50195 dataset was utilized to conduct weighted gene co-expression network analysis (WGCNA) for identifying module genes linked to AMD. KEGG and GO enrichment analyses were subsequently conducted on these module genes. GSE29801 and GSE135092 datasets were subjected to differential expression analysis to pinpoint the DEGs intersecting with the module genes. Subsequently, wet AMD (wAMD) and dry AMD (dAMD) mouse models were developed, from which RPE/choroid tissues were harvested to validate the hub genes via RT-qPCR and Western blot. Results: Using the WGCNA, we selected the "antiquewhite4" module (r = 0.91 and p = 7e-07), which contains a total of 325 genes. Through the intersection of module genes with DEGs, nine hub genes were identified. Pathways involved in complement and coagulation cascades, ECM-receptor interactions, unsaturated fatty acid biosynthesis, and fatty acid elongation play important roles in AMD. Notably, CDH18 demonstrated notable variance across all three datasets. Post validation using RT-qPCR experiments revealed a significant downregulation of CDH18 in both dAMD and wAMD. EGLN3 was expressed at low levels in wAMD. In dAMD, EYA2, LTB, and PODXL were significantly downregulated, whereas APOC1 was notably upregulated. Western blot confirmed that CDH18 was lowly expressed in dAMD and wAMD mouse models. Conclusion: CDH18 was identified as the key gene involved in the pathogenesis of AMD. An imbalance of the complement and coagulation cascades is a potential mechanism of AMD. This study provides a novel idea for diagnosing and treating AMD in the future.

Deep Learning–Based Clustering of OCT Images for Biomarker Discovery in Age-Related Macular Degeneration (PINNACLE Study Report 4)

PurposeWe introduce a deep-learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (AMD). DesignRetrospective analysis of a large dataset of retinal optical coherence tomography (OCT) images. ParticipantsA total of 3,456 adults aged between 51 and 102 years of whom OCT images were collected under the PINNACLE project. MethodsOur system proposes candidates for novel AMD imaging biomarkers in OCT. It works by first training a neural network using self-supervised contrastive learning to discover, without any clinical annotations, features relating to both known and unknown AMD biomarkers present in 46,496 retinal OCT images. To interpret the learned biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We conduct two parallel 1.5-hour semi-structured interviews with two independent teams of retinal specialists to assign descriptions in clinical language to each cluster. Descriptions of clusters achieving consensus can potentially inform new biomarker candidates. Main Outcome MeasuresWe checked if each cluster showed clear features comprehensible to retinal specialists, if they related to AMD and how many described established biomarkers used in grading systems as opposed to recently proposed or potentially new biomarkers. We also compared their prognostic value for late stage wet and dry AMD against an established clinical grading system and a demographic baseline model. ResultsOverall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognised as known biomarkers used in established grading systems and 16 depicted biomarker combinations or subtypes that are either not yet used in grading systems, were only recently proposed, or were unknown. Clusters separated incomplete from complete retinal atrophy, intraretinal from subretinal fluid and thick from thin choroids, and in simulation outperformed clinically-used grading systems in prognostic value. ConclusionsUsing self-supervised deep learning we were able to automatically propose AMD biomarkers going beyond the set used in clinically established grading systems. Without any clinical annotations, contrastive learning discovered subtle differences between fine-grained biomarkers. Ultimately, we envision that equipping clinicians with discovery-oriented deep-learning tools can accelerate discovery of novel prognostic biomarkers.

Human versus Artificial Intelligence: Validation of a Deep Learning Model for Retinal Layer and Fluid Segmentation in Optical Coherence Tomography Images from Patients with Age-Related Macular Degeneration.

Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold-standard method for manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software Version 6.16.8.0. A total of sixty OCT images of healthy subjects and patients with intermediate and exudative age-related macular degeneration (AMD) were included. A quantitative analysis of the retinal thickness and fluid area was performed, and the discrepancy between these methods was investigated. The results showed a moderate-to-strong correlation between the metrics extracted by both software types, in all the groups, and an overall near-perfect area overlap was observed, except for in the inner segment ellipsoid (ISE) layer. The DL system detected a significant difference in the outer retinal thickness across disease stages and accurately identified fluid in exudative cases. In more diseased eyes, there was significantly more disagreement between these methods. This DL system appears to be a reliable method for accessing important OCT biomarkers in AMD. However, further accuracy testing should be conducted to confirm its validity in real-world settings to ultimately aid ophthalmologists in OCT imaging management and guide timely treatment approaches.

Drusen in AMD from the Perspective of Cholesterol Metabolism and Hypoxic Response.

Drusen are one of the most characteristic pathologies of precursor lesion of age-related macular degeneration (AMD). Drusen comprise a yellowish white substance that accumulates typically under the retinal pigment epithelium (RPE), and their constituents are lipids, complement, amyloid, crystallin, and others. In the past, many researchers have focused on drusen and tried to elucidate the pathophysiology of AMD because they believed that disease progression from early AMD to advanced AMD might be based on drusen or drusen might cause AMD. In fact, it is well established that drusen are the hallmark of precursor lesion of AMD and a major risk factor for AMD progression mainly based on their size and number. However, the existence of advanced AMD without drusen has long been recognized. For example, polypoidal choroidal vasculopathy (PCV), which comprises the majority of AMD cases in Asians, often lacks drusen. Thus, there is the possibility that drusen might be no more than a biomarker of AMD and not a cause of AMD. Now is the time to reconsider the relationship between AMD and drusen. In this review, we focus on early AMD pathogenesis based on basic research from the perspective of cholesterol metabolism and hypoxic response in the retina, and we discuss the role of drusen.

Periodontitis and Outer Retinal Thickness: a Cross-Sectional Analysis of the United Kingdom Biobank Cohort

PurposePeriodontitis, a ubiquitous severe gum disease affecting the teeth and surrounding alveolar bone can heighten systemic inflammation. We investigated the association between very severe periodontitis and early biomarkers of age-related macular degeneration, in individuals with no eye disease. DesignCross-sectional analysis of the prospective community-based cohort United Kingdom (UK) Biobank. ParticipantsSixty-seven thousand three hundred eleven UK residents aged 40-70 years recruited between 2006-2010 underwent retinal imaging. MethodsMacular-centered optical coherence tomography images acquired at the baseline visit were segmented for retinal sublayer thicknesses. Very severe periodontitis was ascertained through a touchscreen questionnaire. Linear mixed effects regression modeled the association between very severe periodontitis and retinal sublayer thicknesses adjusting for age, sex, ethnicity, socioeconomic status, alcohol consumption, smoking status, diabetes mellitus, hypertension, refractive error, and previous cataract surgery. Main Outcome MeasuresPhotoreceptor layer (PRL) and retinal pigment epithelium-Bruch’s membrane (RPE-BM) thicknesses. ResultsAmong 36,897 participants included in the analysis, 1,571 (4.3%) reported very severe periodontitis. Affected individuals were older, lived in areas of greater socioeconomic deprivation and were more likely to be hypertensive, diabetic and current smokers (all p<0.001). On average, those with very severe periodontitis were myopic (-0.29 ± 2.40 diopters) while those unaffected were hyperopic (0.05 ± 2.27 diopters, p<0.001). Following adjusted analysis, very severe periodontitis was associated with thinner PRL (-0.55 μm, 95% CI: -0.97, -0.12, p=0.022) but there was no difference in RPE-BM thickness (0.00 μm, 95% CI: -0.12, 0.13, p=0.97). The association between PRL thickness and very severe periodontitis was modified by age (p<0.001). Stratifying individuals by age, thinner PRL was seen among those aged 60-69 years with disease (-1.19 μm, 95% CI: -1.85, -0.53, p<0.001) but not among those under 60 years. ConclusionsAmong those with no known eye disease, very severe periodontitis is statistically associated with a thinner PRL, consistent with incipient age-related macular degeneration. Optimizing oral hygiene may hold additional relevance for people at risk of degenerative retinal disease.

Systematic review and meta-analysis of mass spectrometry proteomics applied to ocular fluids to assess potential biomarkers of age-related macular degeneration

BackgroundAge-related macular degeneration (AMD) is a significant cause of severe vision loss. The main purpose of this study was to identify mass spectrometry proteomics-based potential biomarkers of AMD that contribute to understanding the mechanisms of disease and aiding in early diagnosis.MethodsThis study retrieved studies that aim to detect differences relate to proteomics in AMD patients and healthy control groups by mass spectrometry (MS) proteomics approaches. The search process was accord with PRISMA guidelines (PROSPERO database: CRD42023388093). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes Pathway Analysis (KEGG) were performed on differentially expressed proteins (DEPs) in the included articles using the DAVID database. DEPs were included in a meta-analysis when their effect size could be computed in at least two research studies. The effect size of measured proteins was transformed to the log2-fold change. Protein‒protein interaction (PPI) analysis was conducted on proteins that were statistically significant in the meta-analysis using the String online database.ResultsEleven studies fulfilled the inclusion criteria, and 161 DEPs were identified. The GO analysis showed that AMD is significantly related to proteolysis, extracellular exosome and protein binding. In KEGG, the most significant pathway was the complement and coagulation cascades. Meta-analysis results suggested that eight proteins were statistically significant, and according to PPI results, the most significant four proteins were serotransferrin (TF), apolipoprotein A1 (APOA1), complement C3 (C3) and lipocalin-1 (LCN1).ConclusionsFour possible biomarkers, TF, APOA1, C3 and LCN1, were found to be significant in the pathogenesis of AMD and need to be further validated. Further studies should be performed to evaluate diagnostic and therapeutic value of these proteins.

A Radiomic-Based Machine Learning System to Diagnose Age-Related Macular Degeneration from Ultra-Widefield Fundus Retinography.

The present study was conducted to investigate the potential of radiomics to develop an explainable AI-based system to be applied to ultra-widefield fundus retinographies (UWF-FRTs) with the objective of predicting the presence of the early signs of Age-related Macular Degeneration (AMD) and stratifying subjects with low- versus high-risk of AMD. The ultimate aim was to provide clinicians with an automatic classifier and a signature of objective quantitative image biomarkers of AMD. The use of Machine Learning (ML) and radiomics was based on intensity and texture analysis in the macular region, detected by a Deep Learning (DL)-based macular detector. Two-hundred and twenty six UWF-FRTs were retrospectively collected from two centres and manually annotated to train and test the algorithms. Notably, the combination of the ML-based radiomics model and the DL-based macular detector reported 93% sensitivity and 74% specificity when applied to the data of the centre used for external testing, capturing explainable features associated with drusen or pigmentary abnormalities. In comparison to the human operator's annotations, the system yielded a 0.79 Cohen κ, demonstrating substantial concordance. To our knowledge, these results are the first provided by a radiomic approach for AMD supporting the suitability of an explainable feature extraction method combined with ML for UWF-FRT.

Identification of the Immune Landscapes and Follicular Helper T Cell-Related Genes for the Diagnosis of Age-Related Macular Degeneration.

Age-related macular degeneration (AMD) is a progressive ocular ailment causing age-associated vision deterioration, characterized by dysregulated immune cell activity. Notably, follicular helper T (Tfh) cells have emerged as pivotal contributors to AMD pathogenesis. Nonetheless, investigations into Tfh-associated gene biomarkers for this disorder remain limited. Utilizing gene expression data pertinent to AMD procured from the Gene Expression Omnibus (GEO) repository, we employed the "DESeq2" R software package to standardize and preprocess expression levels. Concurrently, CIBERSORT analysis was utilized to compute the infiltration proportions of 22 distinct immune cell types. Subsequent to weighted gene correlation network analysis (WGCNA), coupled with differential expression scrutiny, we pinpointed genes intricately linked with Tfh cells. These potential genes underwent further screening using the MCODE function within Cytoscape software. Ultimately, a judicious selection of pivotal genes from these identified clusters was executed through the LASSO algorithm. Subsequently, a diagnostic nomogram was devised based on these selected genes. Evident Tfh cell disparities between AMD and control cohorts were observed. Our amalgamated analysis, amalgamating differential expression data with co-expression patterns, unveiled six genes closely associated with Tfh cells in AMD. Subsequent employment of the LASSO algo-rithm facilitated identification of the most pertinent genes conducive to predictive modeling. From these, GABRB3, MFF, and PROX1 were elected as prospective diagnostic biomarkers for AMD. This investigation discerned three novel biomarker genes, linked to inflammatory mechanisms and pivotal in diagnosing AMD. Further exploration of these genes holds potential to foster novel therapeutic modalities and augment comprehension of AMD's disease trajectory.

Screening for biomarkers in age-related macular degeneration

ObjectiveAge-related macular degeneration (AMD) is a significant cause of blindness, initially characterized by the accumulation of sub-Retinal pigment epithelium (RPE) deposits, leading to progressive retinal degeneration and, eventually, irreversible vision loss. This study aimed to elucidate the differential expression of transcriptomic information in AMD and normal human RPE choroidal donor eyes and to investigate whether it could be used as a biomarker for AMD. MethodsRPE choroidal tissue samples (46 Normal samples, 38 AMD samples) were obtained from the GEO (GSE29801) database and screened for differentially expressed genes in normal and AMD patients using GEO2R and R to compare the degree of enrichment of differentially expressed genes in the GO, KEGG pathway. Firstly, we used machine learning models (LASSO, SVM algorithm) to screen disease signature genes and compare the differences between these signature genes in GSVA and immune cell infiltration. Secondly, we also performed a cluster analysis to classify AMD patients. We selected the best classification by weighted gene co-expression network analysis (WGCNA) to screen the key modules and modular genes with the strongest association with AMD. Based on the module genes, four machine models, RF, SVM, XGB, and GLM, were constructed to screen the predictive genes and further construct the AMD clinical prediction model. The accuracy of the column line graphs was evaluated using decision and calibration curves. ResultsFirstly, we identified 15 disease signature genes by lasso and SVM algorithms, which were associated with abnormal glucose metabolism and immune cell infiltration. Secondly, we identified 52 modular signature genes by WGCNA analysis. We found that SVM was the optimal machine learning model for AMD and constructed a clinical prediction model for AMD consisting of 5 predictive genes. ConclusionWe constructed a disease signature genome model and an AMD clinical prediction model by LASSO, WGCNA, and four machine models. The disease signature genes are of great reference significance for AMD etiology research. At the same time, the AMD clinical prediction model provides a reference for early clinical detection of AMD and even becomes a future census tool. In conclusion, our discovery of disease signature genes and AMD clinical prediction models may become promising new targets for the targeted treatment of AMD.

Accurate drusen segmentation in optical coherence tomography via order-constrained regression of retinal layer heights

Drusen are an important biomarker for age-related macular degeneration (AMD). Their accurate segmentation based on optical coherence tomography (OCT) is therefore relevant to the detection, staging, and treatment of disease. Since manual OCT segmentation is resource-consuming and has low reproducibility, automatic techniques are required. In this work, we introduce a novel deep learning based architecture that directly predicts the position of layers in OCT and guarantees their correct order, achieving state-of-the-art results for retinal layer segmentation. In particular, the average absolute distance between our model’s prediction and the ground truth layer segmentation in an AMD dataset is 0.63, 0.85, and 0.44 pixel for Bruch's membrane (BM), retinal pigment epithelium (RPE) and ellipsoid zone (EZ), respectively. Based on layer positions, we further quantify drusen load with excellent accuracy, achieving 0.994 and 0.988 Pearson correlation between drusen volumes estimated by our method and two human readers, and increasing the Dice score to 0.71 ± 0.16 (from 0.60 ± 0.23) and 0.62 ± 0.23 (from 0.53 ± 0.25), respectively, compared to a previous state-of-the-art method. Given its reproducible, accurate, and scalable results, our method can be used for the large-scale analysis of OCT data.

MicroRNAs as potential biomarkers and therapeutic targets in age-related macular degeneration.

Age-related macular degeneration (AMD) involves degenerative and neovascular alteration in the macular region of the retina resulting in central vision loss. AMD can be classified into dry (dAMD) and wet AMD (wAMD). There is no established treatment for dAMD, and therapies available for wAMD have limited success. Diagnosis in early AMD stages is difficult due to the absence of clinical symptoms. Currently, imaging tests are used in the diagnosis of AMD, but cannot predict the clinical course. The clinical limitations to establishing a diagnosis of AMD have led to exploration for innovative and more sensitive tests to support the diagnosis and prognosis of the disease. MicroRNAs (miRNAs) are small single-stranded non-coding RNA molecules that negatively regulate genes by post-transcriptional gene silencing. Because these molecules are dysregulated in various processes implicated in the pathogenesis of AMD, they could contribute to the early detection of the disease and monitoring of its progression. Studies of miRNA profiling have indicated several miRNAs as potential diagnostic biomarkers of AMD, but no approved biomarker is available at present for early AMD detection. Thus, understanding the function of miRNAs in AMD and their use as potential biomarkers may lead to future advances in diagnosis and treatment. Here we present a brief review of some of the miRNAs involved in regulating pathological processes associated with AMD and discuss several candidate miRNAs proposed as biomarkers or therapeutic targets for AMD.

Automated large-scale prediction of exudative AMD progression using machine-read OCT biomarkers.

Age-related Macular Degeneration (AMD) is a major cause of irreversible vision loss in individuals over 55 years old in the United States. One of the late-stage manifestations of AMD, and a major cause of vision loss, is the development of exudative macular neovascularization (MNV). Optical Coherence Tomography (OCT) is the gold standard to identify fluid at different levels within the retina. The presence of fluid is considered the hallmark to define the presence of disease activity. Anti-vascular growth factor (anti-VEGF) injections can be used to treat exudative MNV. However, given the limitations of anti-VEGF treatment, as burdensome need for frequent visits and repeated injections to sustain efficacy, limited durability of the treatment, poor or no response, there is a great interest in detecting early biomarkers associated with a higher risk for AMD progression to exudative forms in order to optimize the design of early intervention clinical trials. The annotation of structural biomarkers on optical coherence tomography (OCT) B-scans is a laborious, complex and time-consuming process, and discrepancies between human graders can introduce variability into this assessment. To address this issue, a deep-learning model (SLIVER-net) was proposed, which could identify AMD biomarkers on structural OCT volumes with high precision and without human supervision. However, the validation was performed on a small dataset, and the true predictive power of these detected biomarkers in the context of a large cohort has not been evaluated. In this retrospective cohort study, we perform the largest-scale validation of these biomarkers to date. We also assess how these features combined with other EHR data (demographics, comorbidities, etc) affect and/or improve the prediction performance relative to known factors. Our hypothesis is that these biomarkers can be identified by a machine learning algorithm without human supervision, in a way that they preserve their predictive nature. The way we test this hypothesis is by building several machine learning models utilizing these machine-read biomarkers and assessing their added predictive power. We found that not only can we show that the machine-read OCT B-scan biomarkers are predictive of AMD progression, we also observe that our proposed combined OCT and EHR data-based algorithm outperforms the state-of-the-art solution in clinically relevant metrics and provides actionable information which has the potential to improve patient care. In addition, it provides a framework for automated large-scale processing of OCT volumes, making it possible to analyze vast archives without human supervision.

Photostress Recovery Time as a Potential Predictive Biomarker for Age-Related Macular Degeneration.

The purpose of this study was to assess recovery time following photostress and its association with age-related macular degeneration (AMD) cross-sectionally and longitudinally in an elderly population-based cohort. We analyzed photostress recovery time (PRT) and AMD in >1800 AugUR study participants aged 70+ years. On color fundus images from baseline and 3-year follow-up, presence of AMD was graded manually (Three Continent AMD Consortium Severity Scale). Visual acuity (VA) was assessed via Early Treatment Diabetic Retinopathy Study (ETDRS) charts. After a 30-second bleaching of the macular region via direct ophthalmoscope, PRT was measured as the seconds to regain VA. First, we analyzed 1208 AugUR participants cross-sectionally (288 with early AMD, and 78 with late AMD). Prolonged PRT was associated with early and late AMD versus no AMD (median PRT = 119.5, 198.0 versus 80.0 seconds, respectively; logistic regression odds ratio [OR] = 1.109-1.165 per 10 seconds, P values < 0.0001). Sensitivity analyses using alternative models or restricting to participants after cataract surgery revealed similar ORs. Second, the association was confirmed in an independent cross-sectional AugUR sample (n = 486). Third, in longitudinal analysis of 233 AugUR participants without AMD, prolonged PRT was associated with incident AMD ascertained 3 years later (follow-up time = 3.2 ± 0.2 years, OR = 1.112-1.162 per 10 seconds, P < 0.05). Overall, we demonstrate a significant association of prolonged PRT with AMD cross-sectionally and longitudinally in elderly individuals. Prolonged PRT might capture retinal function impairment after cell damage before early AMD is visible via color fundus imaging. Our results suggest PRT as quantitative predictive biomarker for incident AMD, making it potentially worthwhile also for clinical care.

Implementing Predictive Models in Artificial Intelligence through OCT Biomarkers for Age-Related Macular Degeneration

Artificial intelligence (AI) represents a growing and promising branch of computer science that is expanding the horizon of prediction, screening, and disease monitoring. The use of multimodal imaging in retinal diseases is particularly advantageous to valorize the integration of machine learning and deep learning for early diagnosis, prediction, and management of retinal disorders. In age-related macular degeneration (AMD) beyond its diagnosis and characterization, the prediction of AMD high-risk phenotypes evolving into late forms remains a critical point. The main multimodal imaging modalities adopted included color fundus photography, fundus autofluorescence, and optical coherence tomography (OCT), which represents undoubtful advantages over other methods. OCT features identified as predictors of late AMD include the morphometric evaluation of retinal layers, drusen volume and topographic distribution, reticular pseudodrusen, and hyperreflective foci quantification. The present narrative review proposes to analyze the current evidence on AI models and biomarkers identified to predict disease progression with particular attention to OCT-based features and to highlight potential perspectives for future research.

Optical coherence tomography and optical coherence tomography angiography biomarkers for age-related macular degeneration and retinovascular diseases

Optical coherence tomography and optical coherence tomography angiography biomarkers for age-related macular degeneration and retinovascular diseases

Reverse engineering for reconstructing baseline features of dry age-related macular degeneration in optical coherence tomography

Age-related macular degeneration (AMD) is the most widespread cause of blindness and the identification of baseline AMD features or biomarkers is critical for early intervention. Optical coherence tomography (OCT) imaging produces a 3D volume consisting of cross sections of retinal tissue while fundus fluorescence (FAF) imaging produces a 2D mapping of retina. FAF has been a good standard for assessing dry AMD late-stage geographic atrophy (GA) while OCT has been used for assessing early AMD biomarkers beyond as well. However, previous approaches in large extent defined AMD features subjectively based on clinicians’ observation. Deep learning—an objective artificial intelligence approach, may enable to discover ’true’ salient AMD features. We develop a novel reverse engineering approach which bases on the backbone of a fully convolutional neural network to objectively identify and visualize AMD early biomarkers in OCT from baseline exams before significant atrophy occurs. Utilizing manually annotated GA regions on FAF from a follow-up visit as ground truth, we segment GA regions and reconstruct early AMD features in baseline OCT volumes. In this preliminary exploration, compared with ground truth, we achieve baseline GA segmentation accuracy of 0.95 and overlapping ratio of 0.65. The reconstructions consistently highlight that large druse and druse clusters with or without mixed hyper-reflective focus lesion on baseline OCT cause the conversion of GA after 12 months. However, hyper-reflective focus lesions and subretinal drusenoid deposit lesions alone are not seen such conversion after 12 months. Further research with larger dataset would be needed to verify these findings.

Characteristics and Spatial Distribution of Structural Features in Age-Related Macular Degeneration: A MACUSTAR Study Report

To report the prevalence and topographic distribution of structural characteristics in study participants with age-related macular degeneration (AMD) and controls in the cross-sectional study part of the MACUSTAR study (ClinicalTrials.gov Identifier: NCT03349801). European, multicenter cohort study. Overall, 301 eyes of 301 subjects with early (n= 34), intermediate (n= 168), and late AMD (n= 43), as well as eyes without any AMD features (n= 56). In study eyes with intermediate AMD (iAMD), the presence of structural AMD biomarkers, including pigmentary abnormalities (PAs), pigment epithelium detachment (PED), refractile deposits, reticular pseudodrusen (RPD), hyperreflective foci (HRF), incomplete/complete retinal pigment epithelium (RPE), and outer retinal atrophy (i/cRORA), and quiescent choroidal neovascularization (qCNV) was systematically determined in the prospectively acquired multimodal retinal imaging cross-sectional data set of MACUSTAR. Retinal layer thicknesses and the RPE drusen complex (RPEDC) volume were determined for the total study cohort in spectral-domain (SD) OCT imaging using a deep-learning-based algorithm. Prevalence and topographic distribution of structural iAMD features. A total of 301 study eyes of 301 subjects with a mean (± standard deviation) age of 71.2 ± 7.20 years (63.1% women) were included. Besides large drusen, the most prevalent structural feature in iAMD study eyes were PA (57.1%), followed by HRF (51.8%) and RPD (22.0%). Pigment epithelium detachment lesions were observed in 4.8%, vitelliform lesions in 4.2%, refractile deposits in 3.0%, and qCNV in 2.4%. Direct precursor lesions for manifest retinal atrophy were detected in 10.7% (iRORA) and 4.2% (cRORA) in iAMD eyes. Overall, the highest RPEDC volume with a median of 98.92× 10-4 mm³ was found in iAMD study eyes. Spatial analysis demonstrated a predominant distribution of RPD in the superior and temporal subfields at a foveal eccentricity of 1.5 to 2 mm, whereas HRF and large drusen had a distinct topographic distribution involving the foveal center. Detailed knowledge of the prevalence and distribution of structural iAMD biomarkers is vital to identify reliable outcome measure for disease progression. Longitudinal analyses are needed to evaluate their prognostic value for conversion to advanced disease stages. Proprietary or commercial disclosure may be found after the references.

Hyperreflective Foci in Age-Related Macular Degeneration are Associated with Disease Severity and Functional Impairment

To analyze presence of hyperreflective foci (HRF) across different age-related macular degeneration (AMD) severities and examine its correlation with other structural and functional AMD features. Longitudinal, single-center, case-control study. One hundred and fifty-eight participants aged > 50 years old with varying AMD severities (including no AMD). Color fundus imaging was used to assess AMD severity and hyperpigmentation (PGM) presence. Subretinal drusenoid deposits (SDD) and HRF were detected on OCT volumes. The correlations of HRF with additional AMD features were evaluated using linear and logistic mixed-effects models. One study eye per participant underwent dark adaptation (DA) testing to measure rod intercept time (RIT) for structure function associations. Eyes were followed longitudinally and changes in AMD severity and RIT were measured relative to HRF presence. The primary outcome was presence of HRF, which was compared with presence of other AMD features and DA impairment. One hundred and fifty-eight participants (median baseline age of 73.1 [interquartile range (IQR)= 66-79] years) contributing 1277 eye visits were included. Hyperreflective foci (HRF) were detected more frequently in higher AMD severities. Hyperreflective-foci presence was significantly associated with PGM presence (odds ratio 832.9, P < 0.001) and SDD presence (odds ratio 9.42, P= 0.017). Eyes with HRF demonstrated significantly longer DA (median 27.1 [IQR= 16-40] minutes) than those without HRF (13.5 [10-22] minutes) but less than eyes with SDD only (40 [28-40] minutes). Highest RIT values were found in eyes with both HRF and SDD (40.0 [40-40] minutes). Age and HRF explained a similar proportion of RIT variability as age and SDD. Eyes that developed HRF demonstrated baseline RITs closer to eyes with HRF at baseline, compared with eyes that never developed HRF (29.1 [16-40], 38.5 [22-40] versus 13.1 [10-22] minutes; Kruskal-Wallis P < 0.001). The progressively increased presence of HRF in higher AMD severities, and its correlation with previously associated AMD biomarkers, suggests HRF is an important OCT feature adding to the understanding of disease progression. Hyperreflective foci presence was associated with delays in DA, indicating HRF is a marker for visual cycle impairment. Proprietary or commercial disclosure may be found after the references.

Relative ellipsoid zone reflectivity and its association with disease severity in age-related macular degeneration: a MACUSTAR study report

Quantification of the relative ellipsoid zone reflectivity (rEZR) might be a structural surrogate parameter for an early disease progression in the context of age-related macular degeneration (AMD). Within the European multicenter, cross-sectional MACUSTAR study, we have devised an automatic approach to determine the mean rEZR [arbitrary units, AU] at two independent visits in SD-OCT volume scans in study participants. Linear mixed-effects models were applied to analyze the association of AMD stage and AMD associated high-risk features including presence of pigmentary abnormalities, reticular pseudodrusen (RPD), volume of the retinal-pigment-epithelial–drusenoid-complex (RPEDC) with the rEZR. Intra-class correlation coefficients (ICC) were determined for rEZR reliability analysis. Within the overall study cohort (301 participants), we could observe decreased rEZR values (coefficient estimate ± standard error) of − 8.05 ± 2.44 AU (p = 0.0011) in the intermediate and of − 22.35 ± 3.28 AU (p < 0.0001) in the late AMD group. RPD presence was significantly associated with the rEZR in iAMD eyes (− 6.49 ± 3.14 AU; p = 0.0403), while there was a good ICC of 0.846 (95% confidence interval: 0.809; 0.876) in the overall study cohort. This study showed an association of rEZR with increasing disease severity and the presence of iAMD high-risk features. Further studies are necessary to evaluate the rEZR’s value as a novel biomarker for AMD and disease progression.