Internal Carotid Artery Stenosis and Ipsilateral Subretinal Drusenoid Deposits.

Purpose: Subretinal drusenoid deposits (SDDs) in age-related macular degeneration (AMD) are strongly associated with vasculopathies such as myocardial infarction and ischemic stroke. This study evaluates ischemic stroke subjects for SDDs to determine whether ocular hypoperfusion from internal carotid artery (ICA) stenosis is associated with ipsilateral SDDs. Methods: A cross-sectional study at Mount Sinai Hospital recruited 39 subjects with ischemic stroke (aged 52-90; 18 women, 21 men); 28 completed all study procedures. Computed tomography (CT) of the head and neck evaluated 54/56 ICAs for stenosis criteria: none (n = 33), mild (n = 12), moderate (n = 3), severe (n = 3), and complete (n = 3). Spectral-domain optical coherence tomography (SD-OCT) scans were read to consensus by two masked graders for soft drusen, SDDs and choroidal thickness (CTh; choroidal thinning = CTh < 250 µm). Univariate testing was done with Fisher's exact test. Multivariate logistic regression models tested age, gender, and ICA stenosis as covariates. Results: Moderate or more ICA stenosis (≥50%-69%) was significantly associated with ipsilateral choroidal thinning (P = 0.021) and ipsilateral SDDs (P = 0.005); the latter were present distal to six of nine stenosed ICAs versus five of 33 normal ICAs. Mild ICA stenosis (≥1%-49%) was not significantly associated with ipsilateral SDDs. Multivariate regression found that older age (P = 0.015) and moderate or more ICA stenosis (P = 0.011) remained significant independent risks for ipsilateral SDDs. Conclusions: At least moderate ICA stenosis (≥50%-69%) is strongly associated with ipsilateral SDDs and choroidal thinning, supporting downstream ophthalmic artery and choroidal hypoperfusion from ICA stenosis as the mechanism for SDD formation. SDDs may thus serve as sensitive biomarkers for ischemic stroke and other vascular diseases.

Subretinal drusenoid deposits, age-related macular degeneration, and cardiovascular disease.

Decades of studies on age-related macular degeneration (AMD), cardiovascular disease and stroke have not found consistent associations between AMD and systemic vascular disease. This study suggests that there is in fact no general relationship, but instead a strong, specific association between only the subretinal drusenoid deposit (SDD) phenotype of AMD on retinal imaging and certain co-existent vascular diseases that are high risk for compromised cardiac output or internal carotid artery stenosis. Future screening initiatives for these high -risk vascular diseases (HRVDs) with fast, inexpensive retinal imaging could make a significant contribution to public health and save lives. Likewise, screening patients with known HRVDs for unrecognized AMD of the SDD form could enable needed treatment and save vision.

SUBRETINAL DRUSENOID DEPOSITS AND SOFT DRUSEN: Are They Markers for Distinct Retinal Diseases?

PURPOSE: Soft drusen and subretinal drusenoid deposits (SDDs) characterize two pathways to advanced age-related macular degeneration (AMD), with distinct genetic risks, serum risks, and associated systemic diseases. METHODS: One hundred and twenty-six subjects with AMD were classified as SDD (with or without soft drusen) or non-SDD (drusen only) by retinal imaging, with serum risks, genetic testing, and histories of cardiovascular disease (CVD) and stroke. RESULTS: There were 62 subjects with SDD and 64 non-SDD subjects, of whom 51 had CVD or stroke. SDD correlated significantly with lower mean serum high-density lipoprotein (61 ± 18 vs. 69 ± 22 mg/dL, P = 0.038, t-test), CVD and stroke (34 of 51 SDD, P = 0.001, chi square), ARMS2 risk allele (P = 0.019, chi square), but not with CFH risk allele (P = 0.66). Non-SDD (drusen only) correlated/trended with APOE2 (P = 0.032) and CETP (P = 0.072) risk alleles (chi square). Multivariate independent risks for SDD were CVD and stroke (P = 0.008) and ARMS2 homozygous risk (P = 0.038). CONCLUSION: Subjects with subretinal drusenoid deposits and non-SDD subjects have distinct systemic associations and serum and genetic risks. Subretinal drusenoid deposits are associated with CVD and stroke, ARMS2 risk, and lower high-density lipoprotein; non-SDDs are associated with higher high-density lipoprotein, CFH risk, and two lipid risk genes. These and other distinct associations suggest that these lesions are markers for distinct diseases.

Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration.

Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. In this study, we compare the performance of retinal fundus images and genetic-information-based machine learning models for the prediction of late AMD. Using data from the Age-related Eye Disease Study, we built machine learning models with various combinations of genetic, socio-demographic/clinical, and retinal image data to predict late AMD using its severity and category in a single visit, in 2, 5, and 10 years. We compared their performance in sensitivity, specificity, accuracy, and unweighted kappa. The 2-year model based on retinal image and socio-demographic (S-D) parameters achieved a sensitivity of 91.34%, specificity of 84.49% while the same for genetic and S-D-parameters-based model was 79.79% and 66.84%. For the 5-year model, the retinal image and S-D-parameters-based model also outperformed the genetic and S-D parameters-based model. The two 10-year models achieved similar sensitivities of 74.24% and 75.79%, respectively, but the retinal image and S-D-parameters-based model was otherwise superior. The retinal-image-based models were not further improved by adding genetic data. Retinal imaging and S-D data can build an excellent machine learning predictor of developing late AMD over 2-5 years; the retinal imaging model appears to be the preferred prognostic tool for efficient patient management.

Combined automated screening for age-related macular degeneration and diabetic retinopathy in primary care settings.

BACKGROUND: Age-related macular degeneration (AMD) and diabetic retinopathy (DR) are among the leading causes of blindness in the United States and other developed countries. Early detection is the key to prevention and effective treatment. We have built an artificial intelligence-based screening system which utilizes a cloud-based platform for combined large scale screening through primary care settings for early diagnosis of these diseases. METHODS: iHealthScreen Inc., an independent medical software company, has developed automated AMD and DR screening systems utilizing a telemedicine platform based on deep machine learning techniques. For both diseases, we prospectively imaged both eyes of 340 unselected non-dilated subjects over 50 years of age. For DR specifically, 152 diabetic patients at New York Eye and Ear faculty retina practices, ophthalmic and primary care clinics in New York city with color fundus cameras. Following the initial review of the images, 308 images with other confounding conditions like high myopia and vascular occlusion, and poor quality were excluded, leaving 676 eligible images for AMD and DR evaluation. Three ophthalmologists evaluated each of the images, and after adjudication, the patients were determined referrable or non-referable for AMD DR. Concerning AMD, 172 were labeled referable (intermediate or late), and 504 were non-referable (no or early). Concurrently, regarding DR, 33 were referable (moderate or worse), and 643 were non-referable (none or mild). All images were uploaded to iHealthScreen's telemedicine platform and analyzed by the automated systems for both diseases. The system performances are tested on per eye basis with sensitivity, specificity, accuracy, and kappa scores with respect to the professional graders. RESULTS: In identifying referable DR, the system achieved a sensitivity of 97.0% and a specificity of 96.3%, and a kappa score of 0.70 on this prospective dataset. For AMD, the sensitivity was 86.6%, the specificity of 92.1%, and a kappa score of 0.76. CONCLUSIONS: The AMD and DR screening tools achieved excellent performance operating together to identify two retinal diseases prospectively in mixed datasets, demonstrating the feasibility of such tools in the early diagnosis of eye diseases. These early screening tools will help create an even more comprehensive system capable of being trained on other retinal pathologies, a goal within reach for public health deployment.

An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging.

RESULTS: The system achieved an accuracy of 89.67% (sensitivity, 83.33%; specificity, 93.89%; and AUC, 0.93). For external validation, the Retinal Fundus Image Database for Glaucoma Analysis dataset, which has 638 gradable quality images, was used. Here, the model achieved an accuracy of 83.54% (sensitivity, 80.11%; specificity, 84.96%; and AUC, 0.85). CONCLUSIONS: Having demonstrated an accurate and fully automated glaucoma-suspect screening system that can be deployed on telemedicine platforms, we plan prospective trials to determine the feasibility of the system in primary-care settings.

Automated diabetic retinopathy screening for primary care settings using deep learning.

Diabetic Retinopathy (DR) is one of the leading causes of blindness in the United States and other high-income countries. Early detection is key to prevention, which could be achieved effectively with a fully automated screening tool performing well on clinically relevant measures in primary care settings. We have built an artificial intelligence-based tool on a cloud-based platform for large-scale screening of DR as referable or non-referable. In this paper, we aim to validate this tool built using deep learning based techniques. The cloud-based screening model was developed and tested using deep learning techniques with 88702 images from the Kaggle dataset and externally validated using 1748 high-resolution images of the retina (or fundus images) from the Messidor-2 dataset. For validation in the primary care settings, 264 images were taken prospectively from two diabetes clinics in Queens, New York. The images were uploaded to the cloud-based software for testing the automated system as compared to expert ophthalmologists' evaluations of referable DR. Measures used were area under the curve (AUC), sensitivity, and specificity of the screening model with respect to professional graders. The screening system achieved a high sensitivity of 99.21% and a specificity of 97.59% on the Kaggle test dataset with an AUC of 0.9992. The system was also externally validated in Messidor-2, where it achieved a sensitivity of 97.63% and a specificity of 99.49% (AUC, 0.9985). On primary care data, the sensitivity was 92.3% overall (12/13 referable images are correctly identified), and overall specificity was 94.8% (233/251 non-referable images). The proposed DR screening tool achieves state-of-the-art performance among the publicly available datasets: Kaggle and Messidor-2 to the best of our knowledge. The performance on various clinically relevant measures demonstrates that the tool is suitable for screening and early diagnosis of DR in primary care settings.

Subretinal Deposits in Pre-eclampsia and Malignant Hypertension: Implications for Age-Related Macular Degeneration.

PURPOSE: To describe the incidence of subretinal deposits that are similar in structure and stage on OCT imaging to subretinal drusenoid deposits (SDDs) in age-related macular degeneration (AMD) in patients with hypertensive choroidopathy secondary to severe pre-eclampsia and malignant hypertension (MHT) and the implications of this ischemic choroidopathy for the pathophysiologic characteristics of SDDs in AMD. DESIGN: Retrospective cross-sectional study. PARTICIPANTS: Thirty-three pre-eclampsia patients and 25 MHT patients with serous retinal detachment (SRD) in at least 1 eye were included. METHODS: Serial multimodal images, including enhanced depth imaging spectral-domain OCT of eyes with hypertensive choroidopathy secondary to pre-eclampsia and MHT, were reviewed at 2 time points, the acute phase (within 4 weeks of initial hypertensive insult) and the recovery phase (beyond 4 weeks). MAIN OUTCOME MEASURES: Incidence of SDD-like lesions in patients with hypertensive choroidopathy secondary to pre-eclampsia and MHT. RESULTS: Subretinal drusenoid deposit-like lesions were observed exclusively in eyes with SRD. Serous retinal detachment occurred in 87.87% of eyes of pre-eclampsia patients and in 94% of eyes of MHT patients. Subretinal drusenoid deposit-like lesions occurred in 28.57% of all eyes with SRD, in 32.76% of eyes with SRD from the pre-eclampsia group, and in 23.40% of eyes with SRD from the MHT group. Vascular imaging suggested underlying choroidal ischemia in all patients (12 eyes) in which it was performed. CONCLUSIONS: Choroidal ischemia may be the underlying mechanism of SDD-like lesions in patients with pre-eclampsia and MHT choroidopathy. These findings potentially are of utmost importance in understanding the mechanism of the reticular macular disease subtype of AMD. Reticular macular disease is characterized by the known association of choroidal insufficiency and SDD, with choroidal insufficiency postulated, but not proven, to be causative. Pre-eclampsia and MHT choroidopathy seems to be a model for lesions similar to SDD in AMD developing based on choroidal insufficiency and, as such, may offer further insights into the pathoetiologic features of SDD in AMD.

Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD.

Purpose: To build and validate artificial intelligence (AI)-based models for AMD screening and for predicting late dry and wet AMD progression within 1 and 2 years. Methods: The dataset of the Age-related Eye Disease Study (AREDS) was used to train and validate our prediction model. External validation was performed on the Nutritional AMD Treatment-2 (NAT-2) study. First Step: An ensemble of deep learning screening methods was trained and validated on 116,875 color fundus photos from 4139 participants in the AREDS study to classify them as no, early, intermediate, or advanced AMD and further stratified them along the AREDS 12 level severity scale. Second step: the resulting AMD scores were combined with sociodemographic clinical data and other automatically extracted imaging data by a logistic model tree machine learning technique to predict risk for progression to late AMD within 1 or 2 years, with training and validation performed on 923 AREDS participants who progressed within 2 years, 901 who progressed within 1 year, and 2840 who did not progress within 2 years. For those found at risk of progression to late AMD, we further predicted the type (dry or wet) of the progression of late AMD. Results: For identification of early/none vs. intermediate/late (i.e., referral level) AMD, we achieved 99.2% accuracy. The prediction model for a 2-year incident late AMD (any) achieved 86.36% accuracy, with 66.88% for late dry and 67.15% for late wet AMD. For the NAT-2 dataset, the 2-year late AMD prediction accuracy was 84%. Conclusions: Validated color fundus photo-based models for AMD screening and risk prediction for late AMD are now ready for clinical testing and potential telemedical deployment. Translational Relevance: Noninvasive, highly accurate, and fast AI methods to screen for referral level AMD and to predict late AMD progression offer significant potential improvements in our care of this prevalent blinding disease.

Translational Retinal Imaging.

The diagnosis and treatment of medical retinal disease is now inseparable from retinal imaging in all its multimodal incarnations. The purpose of this article is to present a selection of very different retinal imaging techniques that are truly translational, in the sense that they are not only new, but can guide us to new understandings of disease processes or interventions that are not accessible by present methods. Quantitative autofluorescence imaging, now available for clinical investigation, has already fundamentally changed our understanding of the role of lipofuscin in age-related macular degeneration. Hyperspectral autofluorescence imaging is bench science poised not only to unravel the molecular basis of retinal pigment epithelium fluorescence, but also to be translated into a clinical camera for earliest detection of age-related macular degeneration. The ophthalmic endoscope for vitreous surgery is a radically new retinal imaging system that enables surgical approaches heretofore impossible while it captures subretinal images of living tissue. Remote retinal imaging coupled with deep learning artificial intelligence will transform the very fabric of future medical care.

A New and Improved Method for Automated Screening of Age-Related Macular Degeneration Using Ensemble Deep Neural Networks.

In this paper, we provide a new framework on deep learning based automated screening method for finding individuals at risk of developing Age-related Macular Degeneration (AMD). We studied the appropriateness of using the transfer learning to screen AMD by using color fundus images. We make use of the Age-Related Eye Disease Study (AREDS) dataset with nearly 150,000 images, which also provided qualitative grading information by expert graders and ophthalmologists. We use ensemble learning technique with two deep neural networks, namely, Inception-ResNet-V2 and Xception with a custom fine-tuning approach. For our study, we have identified two experiments that are most useful in the screening of AMD. First, we have categorized the images into two classes based on the clinical significance: None or early AMD and Intermediate or Advanced AMD. Second, we have categorized the images into four classes: No AMD, early AMD, Intermediate AMD and Advanced AMD. On AREDS dataset, we have achieved an accuracy of over 95.3% for two-class experiment with our ensemble method. With accuracies ranging from 86% (for four-class) to 95.3% (for two-class), we have demonstrated that the training of a deep neural network with the transfer of learned features with a sufficient number of images fares very well and is comparable to human grading.

Retinal Artery and Vein Classification for Automatic Vessel Caliber Grading.

Automated retinal artery and vein identification is a necessity to measure their caliber automatically and to achieve high efficiency and repeatability for a large number of images. In this paper, a novel framework for retinal artery and vein classification is provided. The proposed method utilizes the vessel crossover and color intensity profile which are the most significant features for artery and vein classification. The method first extracts retinal vascular network and then identify individual blood vessels for further classification as artery or vein. We apply deep learning algorithm based segmentation method to extract the retinal vascular network. We then identify each blood vessels to measure caliber that will be used for computing the Central Retinal Artery Equivalent (CRAE) and Central Retinal Vein Equivalent (CRVE). We map the vessel network and use the individual vessel crossover information, vessel color and intensity profile to identify individual vessel segment as artery and vein. We compared automatically classified artery and vein results with a human grader which showed an accuracy of 95%. We compare our results of caliber grading against an established semi-automated caliber grading system and protocol which showed a very high correlation of 0.85 and 0.92, for CRAE and CRVE respectively.

Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm.

In this paper, we propose a novel classification model for automatically identifying individuals with age-related macular degeneration (AMD) or Diabetic Macular Edema (DME) using retinal features from Spectral Domain Optical Coherence Tomography (SD-OCT) images. Our classification method uses retinal features such as the thickness of the retina and the thickness of the individual retinal layers, and the volume of the pathologies such as drusen and hyper-reflective intra-retinal spots. We extract automatically, ten clinically important retinal features by segmenting individual SD-OCT images for classification purposes. The effectiveness of the extracted features is evaluated using several classification methods such as Random Forrest on 251 (59 normal, 177 AMD and 15 DME) subjects. We have performed 15-fold cross-validation tests for three phenotypes; DME, AMD and normal cases using these data sets and achieved accuracy of more than 95% on each data set with the classification method using Random Forrest. When we trained the system as a two-class problem of normal and eye with pathology, using the Random Forrest classifier, we obtained an accuracy of more than 96%. The area under the receiver operating characteristic curve (AUC) finds a value of 0.99 for each dataset. We have also shown the performance of four state-of-the-methods for classification the eye participants and found that our proposed method showed the best accuracy.

An automated method for choroidal thickness measurement from Enhanced Depth Imaging Optical Coherence Tomography images.

The choroid is vascular tissue located underneath the retina and supplies oxygen to the outer retina; any damage to this tissue can be a precursor to retinal diseases. This paper presents an automated method of choroidal segmentation from Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT) images. The Dijkstra shortest path algorithm is used to segment the choroid-sclera interface (CSI), the outermost border of the choroid. A novel intensity-normalisation technique that is based on the depth of the choroid is used to equalise the intensity of all non-vessel pixels in the choroid region. The outer boundary of choroidal vessel and CSI are determined approximately and incorporated to the edge weight of the CSI segmentation to choose optimal edge weights. This method is tested on 190 B-scans of 10 subjects against choroid thickness (CTh) results produced manually by two graders. For comparison, results obtained by two state-of-the-art automated methods and our proposed method are compared against the manual grading, and our proposed method performed the best. The mean root-mean-square error (RMSE) for finding the CSI boundary by our method is 7.71±6.29 pixels, which is significantly lower than the RMSE for the two other state-of-the-art methods (36.17±11.97 pixels and 44.19±19.51 pixels). The correlation coefficient for our method is 0.76, and 0.51 and 0.66 for the other two state-of-the-art methods. The interclass correlation coefficients are 0.72, 0.43 and 0.56 respectively. Our method is highly accurate, robust, reliable and consistent. This identification can enable to quantify the biomarkers of the choroidin large scale study for assessing, monitoring disease progression as well as early detection of retinal diseases. Identification of the boundary can help to determine the loss or change of choroid, which can be used as features for the automatic determination of the stages of retinal diseases.

Modulation of Retinal Arteriolar Central Reflection by APOE Genotype.

OBJECTIVE: This study investigated the retinal arteriolar central reflex (CR, the central reflection observed in photographs of retinal vessels), which may provide information about micro-vascular health in the retina and also the brain, due to the homology between these vascular networks. The study also describes a novel computer based semi-automated technique that accurately quantifies retinal arteriolar CR and vessel width, and calculates the CR to vessel width ratio (CRR) from digital retinal photographs. METHODS: Digital retinal photographs were collected from participants in the Australian Imaging, Biomarkers and Lifestyle study of ageing (AIBL), including 25 participants diagnosed with Alzheimer's disease (AD) (age 72.4 ± 7.5 yrs, 12 male, 13 female) and 123 elderly participants without dementia (cognitively normals: CN) (age 71.6 ± 5.6 yrs, 55 male, 68 female). Using a sub-cohort of 144 (22 AD, 122 CN) with the novel CRR measures, we identified significantly higher CRR levels in AD participants (mean CRR 0.253 (SD 0.04)) as compared with CN's (mean CRR 0.231 (SD 0.04), p = 0.025). Adjustment for APOE ε4 allele status however, reduced the significance (p = 0.081). CRR was significantly higher in APOE ε4 allele carriers (mean CRR 0.254 (SD 0.03) as compared with non-carriers (mean CRR 0.224 (SD 0.05), p < 0.0001). RESULTS: These data indicate that CRR is strongly linked to APOE ε4 status and exhibits a weaker, independent trend with AD diagnosis. The retina may be useful as a novel model for non-invasive monitoring of the effects of APOE ε4 on the central nervous system, particularly in cerebrovascular disease.

Automatic Identification of Pathology-Distorted Retinal Layer Boundaries Using SD-OCT Imaging.

OBJECTIVE: We propose an effective automatic method for identification of four retinal layer boundaries from the spectral domain optical coherence tomography images in the presence and absence of pathologies and morphological changes due to disease. METHODS: The approach first finds an approximate location of three reference layers and then uses these to bound the search space for the actual layers, which is achieved by modeling the problem as a graph and applying Dijkstra's shortest path algorithm. The edge weight between nodes is determined using pixel distance, slope similarity to a reference, and nonassociativity of the layers, which is designed to overcome the distorting effects that pathology can play in the boundary determination. RESULTS: The accuracy of our method was evaluated on three different datasets. It outperforms the current five state-of-the-art methods. On average, the mean and standard deviation of the root-mean-square error in the form of mean ± standard deviation in pixels for our method is 1.57 ± 0.69, which is lower than compared to the existing top five methods of 16.17 ± 22.64, 6.66 ± 9.11, 5.70 ± 10.54, 3.69 ± 2.04, and 2.29 ± 1.54. CONCLUSION: Our method is highly accurate, robust, reliable, and consistent. This identification can enable to quantify the biomarkers of the retina in large-scale study for assessing, monitoring disease progression, as well as early detection of retinal diseases. SIGNIFICANCE: Identification of these boundaries can help to determine the loss of neuroretinal cells or layers and the presence of retinal pathology, which can be used as features for the automatic determination of the stages of retinal diseases.

A novel computer aided quantification method of focal arteriolar narrowing using colour retinal image.

We present a novel method for the quantification of focal arteriolar narrowing (FAN) in human retina, a precursor for hypertension, stroke and other cardiovascular diseases. A reliable and robust arteriolar boundary mapping method is proposed where intensity, gradient and spatial prior knowledge about the arteriolar shape is incorporated into a graph based optimization method to obtain the arteriolar boundary. Following the mapping of the arteriolar boundaries, arteriolar widths are analysed to quantify the severity of focal arteriolar narrowing (FAN). We evaluate our proposed method on a dataset of 116 retinal arteriolar segments which are manually graded by two expert graders. The experimental results indicate a strong correlation between the quantified FAN measurement scores provided by our method and two experts graded FAN severity levels. Our proposed FAN measurement score: percent narrowing (PN) shows high correlation (Spearman correlation coefficient of 0.82(p<0.0001) for grader-1 and 0.84(p<0.0001) for grade-2) with the manually graded FAN severity levels provided by two expert graders. In addition to that, the proposed method shows better reproducibility (Spearman correlation coefficient ρ=0.92(p<0.0001)) compared to two expert graders ( [Formula: see text] (p<0.0001) and [Formula: see text] ) in two successive sessions. The quantitative measurements provided by the proposed method can help us to establish a more reliable link between FAN and known systemic and eye diseases.

Spatial and Spectral Characterization of Human Retinal Pigment Epithelium Fluorophore Families by Ex Vivo Hyperspectral Autofluorescence Imaging.

PURPOSE: Discovery of candidate spectra for abundant fluorophore families in human retinal pigment epithelium (RPE) by ex vivo hyperspectral imaging. METHODS: Hyperspectral autofluorescence emission images were captured between 420 and 720 nm (10-nm intervals), at two excitation bands (436-460, 480-510 nm), from three locations (fovea, perifovea, near-periphery) in 20 normal RPE/Bruch's membrane (BrM) flatmounts. Mathematical factorization extracted a BrM spectrum (S0) and abundant lipofuscin/melanolipofuscin (LF/ML) spectra of RPE origin (S1, S2, S3) from each tissue. RESULTS: Smooth spectra S1 to S3, with perinuclear localization consistent with LF/ML at all three retinal locations and both excitations in 14 eyes (84 datasets), were included in the analysis. The mean peak emissions of S0, S1, and S2 at λex 436 nm were, respectively, 495 ± 14, 535 ± 17, and 576 ± 20 nm. S3 was generally trimodal, with peaks at either 580, 620, or 650 nm (peak mode, 650 nm). At λex 480 nm, S0, S1, and S2 were red-shifted to 526 ± 9, 553 ± 10, and 588 ± 23 nm, and S3 was again trimodal (peak mode, 620 nm). S1 often split into two spectra, S1A and S1B. S3 strongly colocalized with melanin. There were no significant differences across age, sex, or retinal location. CONCLUSIONS: There appear to be at least three families of abundant RPE fluorophores that are ubiquitous across age, retinal location, and sex in this sample of healthy eyes. Further molecular characterization by imaging mass spectrometry and localization via super-resolution microscopy should elucidate normal and abnormal RPE physiology involving fluorophores. TRANSLATIONAL RELEVANCE: Our results help establish hyperspectral autofluorescence imaging of the human retinal pigment epithelium as a useful tool for investigating retinal health and disease.