DEVELOPMENT AND VALIDATION OF AN EXPLAINABLE ARTIFICIAL INTELLIGENCE FRAMEWORK FOR MACULAR DISEASE DIAGNOSIS BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES.

PURPOSE: To develop and validate an artificial intelligence framework for identifying multiple retinal lesions at image level and performing an explainable macular disease diagnosis at eye level in optical coherence tomography images. METHODS: A total of 26,815 optical coherence tomography images were collected from 865 eyes, and 9 retinal lesions and 3 macular diseases were labeled by ophthalmologists, including diabetic macular edema and dry/wet age-related macular degeneration. We applied deep learning to classify retinal lesions at image level and random forests to achieve an explainable disease diagnosis at eye level. The performance of the integrated two-stage framework was evaluated and compared with human experts. RESULTS: On testing data set of 2,480 optical coherence tomography images from 80 eyes, the deep learning model achieved an average area under curve of 0.978 (95% confidence interval, 0.971-0.983) for lesion classification. In addition, random forests performed accurate disease diagnosis with a 0% error rate, which achieved the same accuracy as one of the human experts and was better than the other three experts. It also revealed that the detection of specific lesions in the center of macular region had more contribution to macular disease diagnosis. CONCLUSION: The integrated method achieved high accuracy and interpretability in retinal lesion classification and macular disease diagnosis in optical coherence tomography images and could have the potential to facilitate the clinical diagnosis.

Geographic atrophy: where we are now and where we are going.

PURPOSE OF REVIEW: Age-related macular degeneration (AMD) affects a significant percentage of the elderly population and end-stage disease classified by either geographic atrophy (GA) or neovascular AMD (nvAMD) is one of the leading causes of vision loss worldwide. Despite the fact that there are currently treatments for nvAMD, there are no treatments in practice to prevent disease onset or progression of GA. This topic is at the forefront of ophthalmic research demonstrated by the recent advances in disease characterization, genetic and environmental risk factor classification, biomarker discovery and mechanism of pathogenesis categorization. There are also numerous clinical treatment trials underway, targeting proposed pathways and biomarkers associated with GA that are promising. RECENT FINDINGS: With several clinical trials of potential treatments underway and numerous recent publications on disease diagnosis and classification, the understanding of GA pathogenesis has increased substantially. Although the exact mechanism of pathology is still elusive, recent literature has highlighted the utilization of current and new ophthalmic imaging modalities and discovery of objective and functional markers that can lead to earlier diagnosis and treatment. SUMMARY: Herein, we will provide an overview and discussion of the current status of GA including advances in mechanism of pathogenesis, diagnosis, classification and current treatment modalities.

Imaging Features Associated with Progression to Geographic Atrophy in Age-Related Macular Degeneration: Classification of Atrophy Meeting Report 5.

PURPOSE: To provide an image-based description of retinal features associated with risk for development of geographic atrophy (GA) in eyes with age-related macular degeneration (AMD), as visualized with multimodal imaging anchored by structural OCT. DESIGN: Consensus meeting. PARTICIPANTS: International group that included those with expertise in imaging and AMD basic science and histology, and those with Reading Center experience in AMD clinical trials. METHODS: As part of the Classification of Atrophy Meeting program, an international group of experts analyzed and discussed retinal multimodal imaging features in eyes with AMD associated with GA, risk of progression to GA, or both. Attendees undertook premeeting grading exercises that were reviewed during the meeting sessions. Meeting presentations illustrated established and investigational multimodal imaging features and associated histologic features. Each of these different features were then discussed openly by the entire group to arrive at consensus definitions. These definitions were applied to 40 additional images that were graded independently by attendees to refine the consensus definitions and descriptions further. RESULTS: Consensus was reached on images with descriptors for 12 features. These features included components of outer retinal atrophy (e.g., ellipsoid zone disruption), components of complete retinal pigment epithelium (RPE) and outer retinal atrophy (e.g., RPE perturbation with associated hypotransmission or hypertransmission), features frequently seen in eyes with atrophy (e.g., refractile drusen), and features conferring risk for atrophy development (e.g., hyperreflective foci, drusen, and subretinal drusenoid deposits). CONCLUSIONS: An international consensus on terms and descriptions was reached on multimodal imaging features associated GA and with risk for GA progression in eyes with AMD. We believe this information will be useful to clinicians who manage patients with AMD, researchers who study AMD disease interventions and pathogenesis, and those who design clinical trials for therapies targeting earlier AMD stages than GA expansion.

COLORADO AGE-RELATED MACULAR DEGENERATION REGISTRY: Design and Clinical Risk Factors of the Cohort.

PURPOSE: To study new and existing risk factors related to age-related macular degeneration (AMD) phenotypes in a Colorado cohort. METHODS: Age-related macular degeneration was categorized into early, intermediate, or advanced forms. Controls (n = 180) were patients with cataract and no AMD. Demographic and clinical data were gathered by patient interview and verified by chart review. Image data were reviewed by vitreoretinal specialists. Statistical analysis included univariable and multivariate logistic regression analysis (P < 0.05). RESULTS: Among the 456 patients with AMD, 157 (34.4%), 80 (17.6%), and 219 (48.0%) had the early/intermediate, geographic atrophy, and neovascular forms of the disease, respectively. Adjusted for age, African-American race was associated with a reduced risk of early/intermediate (adjusted odds ratio [AOR] = 0.08, confidence interval [CI] = 0.01-0.67) and neovascular AMD (AOR = 0.15, CI = 0.03-0.72). A family history of AMD was a risk factor for early/intermediate (AOR = 4.08, CI = 2.30-7.25), geographic atrophy (AOR = 8.62, CI = 3.77-19.7), and neovascular AMD (AOR = 3.76, CI = 2.16-6.56). A history of asthma was related to the early/intermediate form of AMD (AOR = 2.34, CI = 1.22-4.46). CONCLUSION: Studying AMD in specific populations may reveal novel risk factors such as our finding of a relationship between asthma history and AMD.

DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs.

PURPOSE: In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming participation of expert practitioners. Although several automated deep learning systems have been developed for classifying color fundus photographs (CFP) of individual eyes by AREDS severity score, none to date has used a patient-based scoring system that uses images from both eyes to assign a severity score. DESIGN: DeepSeeNet, a deep learning model, was developed to classify patients automatically by the AREDS Simplified Severity Scale (score 0-5) using bilateral CFP. PARTICIPANTS: DeepSeeNet was trained on 58 402 and tested on 900 images from the longitudinal follow-up of 4549 participants from AREDS. Gold standard labels were obtained using reading center grades. METHODS: DeepSeeNet simulates the human grading process by first detecting individual AMD risk factors (drusen size, pigmentary abnormalities) for each eye and then calculating a patient-based AMD severity score using the AREDS Simplified Severity Scale. MAIN OUTCOME MEASURES: Overall accuracy, specificity, sensitivity, Cohen's kappa, and area under the curve (AUC). The performance of DeepSeeNet was compared with that of retinal specialists. RESULTS: DeepSeeNet performed better on patient-based classification (accuracy = 0.671; kappa = 0.558) than retinal specialists (accuracy = 0.599; kappa = 0.467) with high AUC in the detection of large drusen (0.94), pigmentary abnormalities (0.93), and late AMD (0.97). DeepSeeNet also outperformed retinal specialists in the detection of large drusen (accuracy 0.742 vs. 0.696; kappa 0.601 vs. 0.517) and pigmentary abnormalities (accuracy 0.890 vs. 0.813; kappa 0.723 vs. 0.535) but showed lower performance in the detection of late AMD (accuracy 0.967 vs. 0.973; kappa 0.663 vs. 0.754). CONCLUSIONS: By simulating the human grading process, DeepSeeNet demonstrated high accuracy with increased transparency in the automated assignment of individual patients to AMD risk categories based on the AREDS Simplified Severity Scale. These results highlight the potential of deep learning to assist and enhance clinical decision-making in patients with AMD, such as early AMD detection and risk prediction for developing late AMD. DeepSeeNet is publicly available on https://github.com/ncbi-nlp/DeepSeeNet.

Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier.

PURPOSE: To automatically detect and classify geographic atrophy (GA) in fundus autofluorescence (FAF) images using a deep learning algorithm. METHODS: In this study, FAF images of patients with GA, a healthy comparable group and a comparable group with other retinal diseases (ORDs) were used to train a multi-layer deep convolutional neural network (DCNN) (1) to detect GA and (2) to differentiate in GA between a diffuse-trickling pattern (dt-GA) and other GA FAF patterns (ndt-GA) in FAF images. 1. For the automated detection of GA in FAF images, two classifiers were built (GA vs. healthy/GA vs. ORD). The DCNN was trained and validated with 400 FAF images in each case (GA 200, healthy 200, or ORD 200). For the subsequent testing, the built classifiers were then tested with 60 untrained FAF images in each case (AMD 30, healthy 30, or ORD 30). Hereby, both classifiers automatically determined a GA probability score and a normal FAF probability score or an ORD probability score. 2. To automatically differentiate between dt-GA and ndt-GA, the DCNN was trained and validated with 200 FAF images (dt-GA 72; ndt-GA 138). Afterwards, the built classifier was tested with 20 untrained FAF images (dt-GA 10; ndt-GA 10) and a dt-GA probability score and an ndt-GA probability score was calculated. For both classifiers, the performance of the training and validation procedure after 500 training steps was measured by determining training accuracy, validation accuracy, and cross entropy. RESULTS: For the GA classifiers (GA vs. healthy/GA vs. ORD), the achieved training accuracy was 99/98%, the validation accuracy 96/91%, and the cross entropy 0.062/0.100. For the dt-GA classifier, the training accuracy was 99%, the validation accuracy 77%, and the cross entropy 0.166. The mean GA probability score was 0.981 ± 0.048 (GA vs. healthy)/0.972 ± 0.439 (GA vs. ORD) in the GA image group and 0.01 ± 0.016 (healthy)/0.061 ± 0.072 (ORD) in the comparison groups (p < 0.001). The mean dt-GA probability score was 0.807 ± 0.116 in the dt-GA image group and 0.180 ± 0.100 in the ndt-GA image group (p < 0.001). CONCLUSION: For the first time, this study describes the use of a deep learning-based algorithm to automatically detect and classify GA in FAF. Hereby, the created classifiers showed excellent results. With further developments, this model may be a tool to predict the individual progression risk of GA and give relevant information for future therapeutic approaches.

Visual Function Metrics in Early and Intermediate Dry Age-related Macular Degeneration for Use as Clinical Trial Endpoints.

PURPOSE: To evaluate and quantify visual function metrics to be used as endpoints of age-related macular degeneration (AMD) stages and visual acuity (VA) loss in patients with early and intermediate AMD. DESIGN: Cross-sectional analysis of baseline data from a prospective study. METHODS: One hundred and one patients were enrolled at Duke Eye Center: 80 patients with early AMD (Age-Related Eye Disease Study [AREDS] stage 2 [n = 33] and intermediate stage 3 [n = 47]) and 21 age-matched, normal controls. A dilated retinal examination, macular pigment optical density measurements, and several functional assessments (best-corrected visual acuity, macular integrity assessment mesopic microperimety, dark adaptometry, low-luminance visual acuity [LLVA] [standard using a log 2.0 neutral density filter and computerized method], and cone contrast test [CCT]) were performed. Low-luminance deficit (LLD) was defined as the difference in numbers of letters read at standard vs low luminance. Group comparisons were performed to evaluate differences between the control and the early and intermediate AMD groups using 2-sided significance tests. RESULTS: Functional measures that significantly distinguished between normal and intermediate AMD were standard and computerized (0.5 cd/m2) LLVA, percent reduced threshold and average threshold on microperimetry, CCTs, and rod intercept on dark adaptation (P < .05). The intermediate group demonstrated deficits in microperimetry reduced threshhold, computerized LLD2, and dark adaptation (P < .05) relative to early AMD. CONCLUSIONS: Our study suggests that LLVA, microperimetry, CCT, and dark adaptation may serve as functional measures differentiating early-to-intermediate stages of dry AMD.

CLASSIFICATION AND QUANTITATIVE ANALYSIS OF GEOGRAPHIC ATROPHY JUNCTIONAL ZONE USING SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY.

PURPOSE: The junctional zone at the border of areas of geographic atrophy (GA) in eyes with nonneovascular age-related macular degeneration is an important target region for future therapeutic strategies. The goal of this study was to perform a detailed classification and quantitative characterization of the junctional zone using spectral domain optical coherence tomography. METHODS: Spectral domain optical coherence tomography volume cube scans (Spectralis OCT, 1024 × 37, Automatic Real Time > 9) were obtained from 15 eyes of 11 patients with GA because of nonneovascular age-related macular degeneration. Volume optical coherence tomography data were imported into previously described validated grading software (3D-OCTOR), and manual segmentation of the retinal pigment epithelium (RPE) and photoreceptor layers was performed on all B-scans (total of 555). Retinal pigment epithelium and photoreceptor defect maps were produced for each case. The borders of the photoreceptor defect area and RPE defect area were delineated individually on separate annotation layers. The two outlines were then superimposed to compare the areas of overlap and nonoverlap. The perimeter of the RPE defect area was calculated by the software in pixels. The superimposed outline of the photoreceptor defect area and the RPE defect area was scrutinized to classify the overlap configuration of the junctional zone into one of three categories: Type 0, exact correspondence between the edge of the RPE defect and photoreceptor defect; Type 1, loss of photoreceptors outside and beyond the edge of the RPE defect; Type 2, preservation of photoreceptors beyond the edge of the RPE defect. The relative proportion of the various border configurations was expressed as a percentage of the perimeter of the RPE defect. Each configuration was then classified into four subgroups according to irregularity of the RPE band and the presence of debris. RESULTS: Fifteen eyes of 11 patients (mean age: 79.3 ± 4.3 years; range: 79-94 years) were included in this study. Seventeen GA lesions were analyzed. Two hundred and thirty-two B-scans were found to pass through the GA lesions, yielding 612 individual GA borders which were separately analyzed and classified. The mean area of the RPE defect was 4.0 ± 4.4 mm, which was significantly smaller than that of the photoreceptor defect which measured 4.4 ± 4.1 mm (paired t test, P = 0.037). On average, 18.0 ± 9.6% (range, 2.3-36.6%) of the junctional zone was of the Type 0 configuration, 57.3 ± 19.0% (range, 21.3-96.8%) was Type 1, and 24.7 ± 18.0% (range, 0.9-64.4%) was Type 2. Type 1 was more prevalent than Type 0 and 2 (analysis of variance, P = 0.000). Debris was present at the margin of the defect in 24.3% (149 of 612) of all assessed junctional zones; 20.0% (14 of 70) of Type 0 junctions, 28.7% (120 of 418) of Type 1, and 12.1% (15 of 124) of Type 2. Debris was more common in Type 1 than Type 2 junctions (P < 0.001). Retinal pigment epithelial irregularity was present at the margin of the defect in 34.8% (213 of 612) of all assessed junctional zones; 52.9% (37 of 70) of Type 0 junctions, 38.0% (159 of 418) of Type 1, and 13.7% (17 of 124) of Type 2. Retinal pigment epithelial irregularity was present more often at Type 0 and Type 1 than at Type 2 junctions (P < 0.001 for both). CONCLUSION: The size of the optical coherence tomography-visible RPE and photoreceptor defect in GA lesions differ significantly. There were significant areas where the photoreceptor outer segments were preserved despite the absence of visible RPE cells, and also areas of photoreceptor outer segment loss despite apparent RPE preservation. These findings have implications for development of therapeutic strategies, particularly cell-replacement approaches.

Consensus Definition for Atrophy Associated with Age-Related Macular Degeneration on OCT: Classification of Atrophy Report 3.

PURPOSE: To develop consensus terminology and criteria for defining atrophy based on OCT findings in the setting of age-related macular degeneration (AMD). DESIGN: Consensus meeting. PARTICIPANTS: Panel of retina specialists, image reading center experts, retinal histologists, and optics engineers. METHODS: As part of the Classification of Atrophy Meetings (CAM) program, an international group of experts surveyed the existing literature, performed a masked analysis of longitudinal multimodal imaging for a series of eyes with AMD, and reviewed the results of this analysis to define areas of agreement and disagreement. Through consensus discussions at 3 meetings over 12 months, a classification system based on OCT was proposed for atrophy secondary to AMD. Specific criteria were defined to establish the presence of atrophy. MAIN OUTCOME MEASURES: A consensus classification system for atrophy and OCT-based criteria to identify atrophy. RESULTS: OCT was proposed as the reference standard or base imaging method to diagnose and stage atrophy. Other methods, including fundus autofluorescence, near-infrared reflectance, and color imaging, provided complementary and confirmatory information. Recognizing that photoreceptor atrophy can occur without retinal pigment epithelium (RPE) atrophy and that atrophy can undergo an evolution of different stages, 4 terms and histologic candidates were proposed: complete RPE and outer retinal atrophy (cRORA), incomplete RPE and outer retinal atrophy, complete outer retinal atrophy, and incomplete outer retinal atrophy. Specific OCT criteria to diagnose cRORA were proposed: (1) a region of hypertransmission of at least 250 µm in diameter, (2) a zone of attenuation or disruption of the RPE of at least 250 µm in diameter, (3) evidence of overlying photoreceptor degeneration, and (4) absence of scrolled RPE or other signs of an RPE tear. CONCLUSIONS: A classification system and criteria for OCT-defined atrophy in the setting of AMD has been proposed based on an international consensus. This classification is a more complete representation of changes that occur in AMD than can be detected using color fundus photography alone. Longitudinal information is required to validate the implied risk of vision loss associated with these terms. This system will enable such future studies to be undertaken using consistent definitions.

Imaging Protocols in Clinical Studies in Advanced Age-Related Macular Degeneration: Recommendations from Classification of Atrophy Consensus Meetings.

PURPOSE: To summarize the results of 2 consensus meetings (Classification of Atrophy Meeting [CAM]) on conventional and advanced imaging modalities used to detect and quantify atrophy due to late-stage non-neovascular and neovascular age-related macular degeneration (AMD) and to provide recommendations on the use of these modalities in natural history studies and interventional clinical trials. DESIGN: Systematic debate on the relevance of distinct imaging modalities held in 2 consensus meetings. PARTICIPANTS: A panel of retina specialists. METHODS: During the CAM, a consortium of international experts evaluated the advantages and disadvantages of various imaging modalities on the basis of the collective analysis of a large series of clinical cases. A systematic discussion on the role of each modality in future studies in non-neovascular and neovascular AMD was held. MAIN OUTCOME MEASURES: Advantages and disadvantages of current retinal imaging technologies and recommendations for their use in advanced AMD trials. RESULTS: Imaging protocols to detect, quantify, and monitor progression of atrophy should include color fundus photography (CFP), confocal fundus autofluorescence (FAF), confocal near-infrared reflectance (NIR), and high-resolution optical coherence tomography volume scans. These images should be acquired at regular intervals throughout the study. In studies of non-neovascular AMD (without evident signs of active or regressed neovascularization [NV] at baseline), CFP may be sufficient at baseline and end-of-study visit. Fluorescein angiography (FA) may become necessary to evaluate for NV at any visit during the study. Indocyanine-green angiography (ICG-A) may be considered at baseline under certain conditions. For studies in patients with neovascular AMD, increased need for visualization of the vasculature must be taken into account. Accordingly, these studies should include FA (recommended at baseline and selected follow-up visits) and ICG-A under certain conditions. CONCLUSIONS: A multimodal imaging approach is recommended in clinical studies for the optimal detection and measurement of atrophy and its associated features. Specific validation studies will be necessary to determine the best combination of imaging modalities, and these recommendations will need to be updated as new imaging technologies become available in the future.

GEOGRAPHIC ATROPHY: Semantic Considerations and Literature Review.

PURPOSE: There is a lack of agreement regarding the types of lesions and clinical conditions that should be included in the term "geographic atrophy." Varied and conflicting views prevail throughout the literature and are currently used by retinal experts and other health care professionals. METHODS: We reviewed the nominal definition of the term "geographic atrophy" and conducted a search of the ophthalmologic literature focusing on preceding terminologies and the first citations of the term "geographic atrophy" secondary to age-related macular degeneration. RESULTS: According to the nominal definition, the term "geography" stands for a detailed description of the surface features of a specific region, indicating its relative position. However, it does not necessarily imply that the borders of the region must be sharply demarcated or related to any anatomical structures. The term "geographical areas of atrophy" was initially cited in the 1960s in the ophthalmologic literature in the context of uveitic eye disease and shortly thereafter also for the description of variants of "senile macular degeneration." However, no direct explanation could be found in the literature as to why the terms "geographical" and "geographic" were chosen. Presumably the terms were used as the atrophic regions resembled the map of a continent or well-defined country borders on thematic geographical maps. With the evolution of the terminology, the commonly used adjunct "of the retinal pigment epithelium" was frequently omitted and solely the term "geographic atrophy" prevailed for the nonexudative late-stage of age-related macular degeneration itself. Along with the quantification of atrophic areas, based on different imaging modalities and the use of both manual and semiautomated approaches, various and inconsistent definitions for the minimal lesion diameter or size of atrophic lesions have also emerged. CONCLUSION: Reconsideration of the application of the term "geographic atrophy" in the context of age-related macular degeneration seems to be prudent given ongoing advances in multimodal retinal imaging technology with identification of various phenotypic characteristics, and the observation of atrophy development in eyes under antiangiogenic therapy.

Prevalence and Genetic Characteristics of Geographic Atrophy among Elderly Japanese with Age-Related Macular Degeneration.

OBJECTIVE: To investigate the prevalence and genetic characteristics of geographic atrophy (GA) among elderly Japanese with advanced age-related macular degeneration (AMD) in a clinic-based study. METHODS: Two-hundred and ninety consecutive patients with advanced AMD were classified into typical neovascular AMD, polypoidal choroidal vasculopathy (PCV), retinal angiomatous proliferation (RAP) or geographic atrophy (GA). Genetic variants of ARMS2 A69S (rs10490924) and CFH I62V (rs800292) were genotyped using TaqMan Genotyping Assays. The clinical and genetic characteristics were compared between patients with and without GA. RESULTS: The number of patients diagnosed as having typical neovascular AMD, PCV, RAP and GA were 98 (33.8%), 151 (52.1%), 22 (7.5%) and 19 (6.6%), respectively. Of 19 patients with GA, 13 patients (68.4%) had unilateral GA with exudative AMD in the contralateral eye. Patients with GA were significantly older, with a higher prevalence of reticular pseudodrusen, bilateral involvement of advanced AMD and T-allele frequency of ARMS2 A69S compared with those with typical AMD and PCV; although there were no differences in the genetic and clinical characteristics among patients with GA and RAP. CONCLUSIONS: The prevalence of GA was 6.6% among elderly Japanese with AMD. Patients with GA and RAP exhibited genetic and clinical similarities.

RETINAL VASCULAR PLEXUSES' CHANGES IN DRY AGE-RELATED MACULAR DEGENERATION, EVALUATED BY MEANS OF OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY.

PURPOSE: To investigate alteration in superficial and deep retinal vascular densities and choroidal thickness, in patients affected by early and intermediate age-related macular degeneration (AMD). METHODS: All patients had undergone optical coherence tomography angiography (OCTA). All eyes were grouped into two stages: "early AMD" and "intermediate AMD." Outcome measures were superficial vessel density, deep vessel density, and choroidal thickness. A control group of healthy subjects was selected for the statistical comparisons. RESULTS: A total of 37 eyes of 37 dry AMD patients were enrolled for the study. Fourteen of 37 eyes were classified as having early AMD, the remaining 23 of 37 eyes were classified as being affected by intermediate AMD. Superficial and deep vessel densities were 39.21% ± 10.67% and 43.84% ± 11.57%, respectively, in the control group and 28.30% ± 10.73% and 36.41% ± 12.30%, respectively, in AMD patients (P = 0.001 and P = 0.017, respectively). Choroidal thickness was significantly reduced in AMD patients. CONCLUSION: In the last years, several studies have reported vascular factors playing an important role in AMD pathogenesis. We demonstrated that both superficial and deep retinal plexuses are altered among patients affected by AMD. Interestingly, this alteration starts immediately at the intermediate AMD stage and also the choroidal thickness reduction.

Photoreceptor dysfunction in early and intermediate age-related macular degeneration assessed with mfERG and spectral domain OCT.

PURPOSE: To evaluate the changes of the photoreceptor layer (PRL) thickness with spectral domain optical coherence tomography (SD-OCT) and the retinal function by mfERG, as well as the correlation of morphology and function parameters in subjects with early and intermediate age-related macular degeneration (AMD). METHODS: Subjects with clinical diagnosis of early or intermediate AMD and age-matched healthy subjects were recruited prospectively in this study. Color fundus photography, SD-OCT, and mfERG were conducted. Retinal photoreceptor thickness was measured, and first-order kernel responses were recorded. The differences between AMD group and control group were compared, and the correlations between macular photoreceptor thickness and the mfERG were analyzed. RESULTS: PRL thickness (µm) in four areas including foveola and 0.5, 1.5, and 3 mm away from foveola was 192.48 ± 17.37, 163.73 ± 12.95, 130.93 ± 9.20, and 108.78 ± 7.81, respectively, in normal eyes, whereas in AMD group, they were 158.61 ± 45.25, 138.91 ± 20.92, 118.91 ± 12.85, and 95.00 ± 9.64, respectively (P < 0.001). The mean amplitude response densities of AMD patients decreased significantly compared to the control group in ring 1-6 (P < 0.001). The mean mfERG N1 and P1 latency of AMD patients prolonged compared to the control group, except the ring 1 (P = 0.588 and P = 0.084). The macular PRL thickness was significantly associated with the mfERGN1 and P1 amplitude density in ring 1-4 (r = 0.338-0.533, P < 0.01). CONCLUSIONS: PRL thickness decreases are in accordance with the deterioration of retinal electrophysiological activity. The retinal PRL thickness is important parameter to assess of early and intermediate AMD severity.

Macular ganglion cell complex and retinal nerve fiber layer comparison in different stages of age-related macular degeneration.

PURPOSE: To employ optical coherence tomography (OCT) to analyze the morphologic changes in the inner retina in different categories of age-related macular degeneration (AMD). DESIGN: Observational cross-sectional study. METHODS: Single-center study. Inclusion criteria were age over 50, diagnosis of Age-Related Eye Disease Study (AREDS) category 2 and 3, naïve neovascular AMD, and atrophic AMD. Healthy patients of similar age acted as a control group. Primary outcome measures were the changes in ganglion cell complex (GCC) and retinal nerve fiber layer (RNFL). Secondary outcomes included modifications of rim area and cup-to-disc ratio. RESULTS: One hundred and thirty eyes of 130 patients were recruited: 26 eyes for AREDS category 2, 26 for AREDS category 3, 26 for neovascular AMD, 26 with atrophic AMD, and 26 controls. Mean peripapillary RNFL thickness was significantly lower in neovascular AMD, compared to controls (P = .004); peripapillary RNFL did not significantly vary among AREDS category 2 and 3 and atrophic AMD groups, compared to controls. Mean GCC thickness was higher in the control group, becoming progressively thinner up to neovascular and atrophic AMD groups (P < .0001). Rim area was significantly thinner in the neovascular AMD group compared with controls (P = .047); cup-to-disc ratio was higher in the neovascular AMD group compared with the control group (P = .047). CONCLUSIONS: This study demonstrates that eyes with neovascular AMD display reduced RNFL and GCC thickness. RNFL is partially spared in atrophic advanced AMD. The identification of alteration in RNFL and GCC thickness may reveal useful for future therapeutic implications.

Comparison of Geographic Atrophy Growth Rates Using Different Imaging Modalities in the COMPLETE Study.

BACKGROUND AND OBJECTIVE: To compare the measurements and growth rates of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) obtained using different imaging modalities. PATIENTS AND METHODS: Thirty patients with AMD and GA measuring from 1.25 mm² to 18 mm² based on spectral-domain optical coherence tomography (SD-OCT) fundus imaging were enrolled. Imaging was performed at baseline and at follow-up months 3, 6, 9, and 12, including autofluorescence (AF) imaging with a fundus camera-based flash system (TRC-50DX; Topcon Medical Systems, Oakland, NJ; AF excitation λ: 535-585 nm; detection λ: 605-715 nm), AF and fluorescein angiography (FA) imaging with a confocal scanning laser ophthalmoscopy (SLO) system (Spectralis; Heidelberg Engineering, Heidelberg, Germany; AF excitation λ: 488 nm; detection λ: > 500 nm), and SD-OCT en face imaging (Cirrus; Carl Zeiss Meditec, Dublin, CA). RESULTS: Average baseline square root measurements and enlargement rates of square root areas appeared similar across all modalities; 0.2 mm was the largest difference between any pair of measurement means. The intraclass correlation coefficients (ICC) were essentially equal to 1 for all comparisons of area measurements but were lower for growth rates than area measurements. Comparison of 26-week average enlargement rates showed no significant difference between the SLO AF image and enhanced SD-OCT en face image (mean difference: 0.01 mm; SD: 0.10; P = .70). CONCLUSION: Agreement among all imaging modalities in measuring the areas of GA at baseline diminished when the growth rates of GA were compared over 26 weeks, likely because each imaging technique identifies different anatomic features along the border of GA, which may appear similar but change at different rates.

MORPHOLOGY SCORE AS A MARKER OF RETINAL FUNCTION IN DRUSENOID PIGMENT EPITHELIAL DETACHMENT.

PURPOSE: To evaluate a morphology score for drusenoid pigment epithelial detachment (dPED) regarding predictability of a decline in retinal function beyond best-corrected visual acuity. METHODS: Thirteen eyes of 10 patients with dPED due to age-related macular degeneration (AMD) were included (age 72.8 ± 4.2 years). All underwent volume spectral domain optical coherence tomography, fluorescence angiography, and confocal scanning laser ophthalmoscopy infrared imaging as well as multifocal electroretinography and microperimetry. The dPED morphology score suggested consists of five parameters: hyperreflective spots in infrared, lesion diameter, lesion height, presence of vitelliform-like material in the subretinal space or subretinal fluid, and integrity of the ellipsoid zone in spectral domain optical coherence tomography. Subsequently, a score value between 0 and 1 according to the extent of morphologic changes was correlated to foveal multifocal electroretinography and microperimetry measurements. RESULTS: The mean best-corrected visual acuity was 20/40. The mean height and mean diameter of dPED were 312.2 ± 111 µm and 2,535 ± 805 µm. Two dPED showed no hyperreflective spots in confocal scanning laser ophthalmoscopy infrared images, three displayed a moderate stage of hyperreflective spots, and eight had severe hyperreflective spots. Two eyes showed subretinal fluid, and five patients showed vitelliform-like material in the subretinal space. Eight eyes revealed a severe disruption of the ellipsoid zone. Although no correlation was found between dPED morphology score and best-corrected visual acuity, eyes with a dPED morphology score >0.5 revealed distinctly decreased values in functional measurements compared with those with a score ≤0.5. CONCLUSION: The dPED morphology score aggregates all currently known morphologic changes in dPED and represents a valuable tool for clinical lesion evaluation. Furthermore, it allows for assessing an estimate of functional decline beyond best-corrected visual acuity.