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. 2026 Jun 30;6(9):101307. doi: 10.1016/j.xops.2026.101307

High-Resolution and Multimodal Structural Phenotyping of Intermediate and Advanced Non-Neovascular Age-Related Macular Degeneration

Chiara M Eandi 1,2,3,∗, Valerie C Snyder 4, Kate Grieve 1, Kiyoko Gocho 1, Celine Chaumette 1, D Castro-Farias 1, Nate Norberg 1, Grace Eddy 4, Thomas R Friberg 4, Andrew W Eller 4, Kunal K Dansingani 4, Joseph N Martel 4, Jay Chhablani 4, José-Alain Sahel 1,4, Michel Paques 1, Ethan A Rossi 4,5,6
PMCID: PMC13487613  PMID: 42621341

Abstract

Purpose

To characterize structural phenotypes in intermediate and advanced non-neovascular age-related macular degeneration (AMD) using flood-illumination adaptive optics (FIAO) and to explore their relationship with established multimodal imaging biomarkers, including OCT-based atrophy classifications.

Design

Prospective, cross-sectional observational study.

Subjects

A total of 208 eyes from 117 patients (mean age 74 years; 63% female) with intermediate AMD (iAMD) or geographic atrophy (GA) were enrolled. Eligible participants had visual acuity ≥20/63, refractive error between –6 and +2 diopters, and sufficient media clarity for high-quality imaging.

Methods

Participants underwent multimodal imaging, including color fundus photography, spectral-domain OCT, infrared reflectance, fundus autofluorescence, and FIAO. Structural phenotypes (drusen, subretinal drusenoid deposits, pigmentary changes, and GA margins) were assessed qualitatively across modalities. Flood-illumination adaptive optics features were compared with OCT-based atrophy classifications (incomplete and complete retinal pigment epithelium and outer retinal atrophy [iRORA and cRORA]) using descriptive analyses and cross-tabulation.

Main Outcome Measures

Prevalence and multimodal characteristics of AMD structural phenotypes, and their correspondence with FIAO features.

Results

Drusen and pigmentary abnormalities were detected in the majority of eyes across modalities, with variability in prevalence depending on imaging technique. Flood-illumination adaptive optics identified drusen in 70% of eyes revealing a consistent reflectivity pattern of hyperreflective borders and hyporeflective centers. Hyporeflective clumps corresponding to pigmentary changes were always observed on FIAO, frequently localizing to areas of atrophy or transition zones. Distinct FIAO-defined GA border phenotypes were observed, including well-demarcated and indistinct margins. In descriptive cross-tabulation, well-demarcated FIAO borders were more frequently associated with OCT-defined cRORA, whereas indistinct or hyporeflective margins were with iRORA.

Conclusions

Flood-illumination adaptive optics provides high-resolution visualization of structural features in iAMD and GA, revealing distinct patterns of drusen, pigmentary changes, and atrophic margins. While certain FIAO features show qualitative correspondence with OCT-defined atrophy stages, these relationships remain descriptive. Further quantitative and longitudinal studies are needed to determine their clinical relevance and role in disease characterization.

Financial Disclosure(s)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Keywords: Adaptive optics, Age-related macular degeneration, Drusen, Geographic atrophy, Pigment

Age-related macular degeneration (AMD) is the leading cause of blindness amongst the elderly in the developed world,1, 2, 3 with prevalence rising due to population aging. Several aspects of AMD are still poorly understood, partly due to a lack of appropriate imaging tools for studying this slowly progressing disease at a microscopic scale. The hallmark pathologic feature of early AMD is extracellular deposits, called drusen, both in the macula and peripheral retina.1,4 There are different types of drusen classified according to their location and size. In particular, subretinal pigment epithelium (RPE) deposits are classified based on size as small (<63 μm), intermediate (>63 and <125 μm), or large (>125 μm) according to the Beckman simplified clinical classification5 based on color fundus imaging. Subretinal drusenoid deposits (SDDs) are another type of deposit characterized by accumulation of material in the subretinal space with direct contact with photoreceptors. Subretinal drusenoid deposits are classified into 3 stages (stage 1–3) according to the thickness of the deposit visible on spectral-domain OCT (SD-OCT) scans.6 They are present in approximately one-quarter (23%) of healthy adults, and their prevalence increases with age and AMD severity in subjects with early to intermediate AMD (iAMD) (49% and 79%, respectively). Subretinal drusenoid deposit numbers, size, and distribution, particularly when accompanied by pigmentary changes, are associated with increased risk of progression to advanced disease.7, 8, 9, 10

While clinical classification systems based on color fundus imaging exist,5 they are not routinely applied in practice, and contemporary management relies on multimodal imaging.

OCT is the clinical gold standard for assessing retinal structure and identifying biomarkers of progression, including drusen characteristics, hyperreflective foci (HRFs), and alterations of the outer retina.11, 12, 13, 14, 15, 16, 17, 18, 19 More recently, the Classification of Atrophy Meeting consensus classification has standardized OCT features of atrophy, introducing entities such as incomplete and complete RPE and outer retinal atrophy (iRORA and cRORA), thereby improving the consistency of structural endpoints in AMD.12, 13, 14, 15, 16 Fundus autofluorescence (FAF), including both short-wavelength and near-infrared modalities, complements OCT by enabling sensitive detection of RPE dysfunction. It represents the gold standard for visualizing geographic atrophy (GA) and monitoring its progression.20,21

Age-related macular degeneration is a multifactorial disease, and its pathogenesis is not completely understood yet. Despite these advances, current clinical imaging modalities do not resolve cellular-scale changes, limiting our understanding of how microscopic alterations in RPE, photoreceptors, and pigment redistribution relate to established imaging biomarkers. In particular, the biological basis and spatial organization of pigmentary changes, recognized as key risk factors for progression,5 remain incompletely defined. Senescent RPE cells exhibit impaired function, including reduced autophagy and increased oxidative stress, which promote chronic inflammation and may contribute to disease progression.22,23 Genetic associations, particularly involving the complement pathway, further implicate inflammation in AMD,24, 25, 26, 27, 28 although the mechanisms linking these processes to structural retinal changes remain unclear. Recent evidence suggests that retinal melanophages may underline hyperreflective clumps (HRCs) and are associated with RPE dysmorphia,29 highlighting a potential cellular basis for observed imaging features.

Retinal pigment epithelium degeneration is a critical early event in outer retinal atrophy, disrupting photoreceptor support and the visual cycle. This process leads to the release and redistribution of intracellular components, including melanin and lipofuscin, which can trigger immune cell activation.22,30, 31, 32, 33 This in turn can lead to microglial cell activation and mononuclear phagocyte infiltration.34,35

Altered melanin compartmentalization, detectable with near-infrared FAF,30 may contribute to the pigmentary changes observed in AMD; however, the relationship between these cellular processes and clinically visible phenotypes remains poorly defined.

Despite recent progress, current clinical tools lack the resolution to evaluate the phenotype at the level of single cells. Adaptive optics (AO) ophthalmoscopy36, 37, 38, 39, 40 enables in vivo imaging of the retina at near-cellular resolution.41, 42, 43, 44 Adaptive optics ophthalmoscopy has been implemented in both flood-illumination (flood-illumination AO [FlAO]) and scanning laser systems (AO scanning laser ophthalmoscopy [AOSLO]).36, 37, 38 Each implementation has inherent strengths and weaknesses. In particular, FlAO offers practical advantages for clinical research, including a larger field of view, distortion-free imaging, and availability in commercially standardized systems that facilitate multicenter studies. While its resolution is lower than that of AOSLO,39 FIAO uniquely supports robust, repeatable imaging over larger retinal areas and longitudinal assessment of disease dynamics.

Previous studies with AO imaging found photoreceptor loss in regions that appear unremarkable in subjects with iAMD44 and perturbation of cone reflectivity in AMD patients when compared to histology.45 Histological studies described the migrating process of RPE cells in AMD, which may, at least in part, explain the HRCs visualized on AO ophthalmoscopy.46 This perturbation might reflect changes in the underlying RPE mosaic and perhaps in the relative distribution of melanin across the RPE layer.47 Moreover, with FIAO time-lapse imaging, we have shown that atrophy progression can be measured across tiny areas and that highly mobile small hyporeflective structures precede and accompany the emergence and progression of GA.41,42 Imaging drusen with AO ophthalmoscopy can be challenging. Drusen arise in different sizes and locations that impact their visualization on AO ophthalmoscopy.48 Drusen displace photoreceptors, causing incident light to be differentially scattered depending on the orientation of the photoreceptors across the aspect of the drusen. We recently capitalized on this to show that by varying the gaze position in FIAO we can delineate the borders of drusen with high precision and even detect tiny drusen that are too small to be visualized using any other method.49

The purpose of this study is to define cellular-level structural phenotypes in iAMD and advanced non-neovascular AMD using FIAO and to relate these findings to established multimodal imaging biomarkers, including OCT features and Classification of Atrophy Meeting atrophy classifications,12 with a focus on drusen, pigmentary changes, and GA.

Methods

Participants

In this cross-sectional observational study, we prospectively enrolled patients with either iAMD or early signs of GA from the Quinze-Vingts National Ophthalmology Hospital, Paris, France and the University of Pittsburgh, Department of Ophthalmology, United States, over a period of 2 years (January 2018–December 2019). The study was approved by the institutional review boards of the Quinze-Vingts National Ophthalmology Hospital and the University of Pittsburgh and adhered to the tenets of the Declaration of Helsinki. Written informed consent was acquired before enrollment for each participant following a detailed explanation of experimental procedures both verbally and in writing.

The inclusion/exclusion criteria are specified in Table S1 (available at www.ophthalmologyscience.org). Briefly, participants with intermediate or advanced non-neovascular AMD had best-corrected visual acuity of at least 20/63; the ocular media were sufficiently clear to obtain good quality clinical imaging and FIAO. The exclusion criteria included refractive error > +2 diopters or > –6 D; and nystagmus, choroidal neovascularization, diabetic retinopathy, or other macular diseases such as central serous chorioretinopathy.

Clinical Examination and Image Acquisition

All participants underwent a routine ophthalmologic eye examination and clinical imaging. Best-corrected visual acuity was measured using the ETDRS chart (Snellen equivalent). Standard clinical imaging was performed after pupil dilation with tropicamide 1% and phenylephrine 2.5% ophthalmic solution and included color fundus photography (30° and 50°; Topcon TRC-50IX), dense SD-OCT (Volume scan macular 20×20, 25 sections, 9 frames; Volume scan macular 15 × 5, 131 sections, 9 frames; Vertical and horizontal lines macular, 24 frames; Spectralis HRA + OCT, Heidelberg Engineering), and SLO (infrared reflectance), blue FAF (excitation = 488 nm; Spectralis, Heidelberg Engineering). Spectral-domain OCT, infrared reflectance, and FAF images were all acquired over a 30-degree field of view centered on the fovea. Image quality was checked and image excluded if signal strength index was below 20 db.

Identical, commercially available FIAO systems (rtx1-e, Imagine Eyes) were used at each study site. The acquisition protocol was the same in the 2 centers, as follows: FIAO camera was focused on the cone photoreceptors and 9 4° × 4° images with 50% overlap at 9 locations surrounding and including the fovea were acquired, then formed into montages covering an 8° region centered on the fovea. Poor quality was defined as excessive blurring or poor contrast in the image. Clinical imaging and FIAO were performed on the same day or within 1 week to minimize any potential changes between sessions.

Multimodal Registration and Image Processing and Analysis

Flood-illumination adaptive optics montages were generated for all participants. Image registration was performed using i2k Retina x64 Pro (DualAlign LLC) installed on the rtx1 system. Multimodal en face images, including FlAO montages and clinical imaging modalities (color fundus photography, infrared reflectance, and blue autofluorescence [AF]), were subsequently aligned in Adobe Photoshop to generate composite multi-scale images. Registration was achieved through manual alignment based on local anatomical landmarks, primarily retinal vasculature.

Structural phenotypes were independently evaluated across multiscale images by 2 masked graders (C.M.E. and K.G.). In cases of disagreement, adjudication was performed by a senior retinal specialist (C.M.E.). Selected parameters were graded in a binary fashion (present/absent), and intergrader reliability was assessed using percent agreement (Table 1).

Table 1.

Findings of Different Biomarkers Analyzed on Eye-Basis with Different Imaging Modalities

Biomarker Total (n = 208), % iAMD (n = 167), % GA (n = 41), % Intergrader Reliability %
Color fundus
 Medium drusen 153, 74% 120,72% 33, 80% 96%
 Large drusen 127, 61% 105, 63% 22, 54% 98%
 Subretinal drusenoid deposits 20, 10% 16, 10% 4, 10% 96%
 Pigmentary abnormalities 156, 75% 124, 74% 32, 78% 88%
cSLO
 Geographic atrophy (FAF) 62, 30% 21, 13% 41, 100% 98%
 Subretinal drusenoid deposits 50, 24% 36, 22% 14, 34% 93%
SD-OCT
 Hyperreflective foci 98, 47% 67, 40% 31, 76% 97%
 Subretinal drusenoid deposits 33, 16% 26, 16% 7, 17% 99%
 ELM descent 62, 30% 67, 40% 39, 95% 93%
 cORA (complete outer retinal atrophy) 28, 13% 26, 16% 38, 93% 89%
 cRORA (complete RPE and outer retinal atrophy) 49, 24% 23, 14% 39, 95% 97%
 iRORA (incomplete RPE and outer retinal atrophy) 37, 18% 10, 6% 20, 49% 95%
 iORA (incomplete outer retinal atrophy) 102, 49% 10, 6% 16, 40% 88%
FIAO
 Drusen 145, 70% 123, 74% 22, 54% 88%
 GA with well-defined border 51, 25% 18, 11% 37, 90% 97%
 GA with fuzzy border 26, 13% 13, 8% 25, 61% 96%
 GA with marginal hyporeflectance 55, 26% 123, 74% 37, 90% 95%
 GA with marginal pigmented clumps 49, 24% 14, 8% 36, 88% 98%
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cSLO = confocal scanning laser ophthalmoscope; ELM = external limiting membrane; FAF = fundus autofluorescence; FIAO = flood-illumination adaptive optics; GA = geographic atrophy; iAMD = intermediate age-related macular degeneration; IR = infrared; RPE = retinal pigment epithelium; SD-OCT = spectral-domain OCT.

Clinical imaging was graded on a per-eye basis. The presence of a phenotype was recorded if identified, regardless of its number or spatial distribution. Atrophy grading was conducted using dense OCT volume scans according to the Classification of Atrophy Meeting criteria.12 Association with FlAO findings was performed within selected regions of interest defined on a single OCT B-scan16 using cross-tabulation.

Statistical analyses were primarily descriptive, given the qualitative and exploratory nature of the study. Continuous variables are reported as mean ± standard deviation and range, while categorical variables are summarized as counts (n) and percentages (%). Given the descriptive design, no inferential statistics or hypothesis testing were performed. All analyses were performed using R version 4.1.0 (R Foundation for Statistical Computing).

Results

Study Population

Two hundred eight eyes of 117 consecutive patients (34 in Paris and 83 in Pittsburgh) with intermediate or advanced atrophic AMD were enrolled in this study. Twenty-six eyes of 234 (11%) were excluded from analysis due to poor FlAO image quality. All were White (except for 1 Asian female patient) with a mean age of 74 years (range, 57–90). There were 74 (63%) women. One hundred sixty-seven of 208 eyes (80%) were classified as intermediate stage (iAMD) and 41 of 208 eyes (20%) presented GA lesions according to the Beckman Simplified Clinical Classification System.5 The mean best-corrected visual acuity for the overall cohort was 20/50 Snellen equivalent (standard deviation ± 0.2 logarithm of the minimum angle of resolution; range 20/20–20/63). Baseline demographic and clinical characteristics, stratified by disease stage (iAMD vs. GA), are summarized in Table 2.

Table 2.

Demographic Data and Patient Characteristics of the Study Population Overall and Stratified per Eye by AMD Stage

Population (n = 117) Intermediate AMD Eyes (n = 167) GA Eyes (n = 41)
Gender male/female (%) 43/74 (37/63) 68/99 (41/59) 18/23 (44/56)
Mean age ± SD, range (years) 73.8 ± 8.3, 57-90 73.8 ± 8.6, 57-89 76.5 ± 6.9, 60-90
Mean BCVA ± SD, range 20/50 ± 0.2, 20/20–20/63 20/32 ± 0.2, 20/20–20/50 20/40 ± 0.2, 20/20–20/63
AREDS supplements usage (%) 85 (72.65%) 75 (45%) 21 (51%)
Smoking (current or former) (%) 49 (41.88%) 60 (36%) 15 (37%)
Hypertension (%) 48 (41%) 76 (45%) 22 (54%)
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AMD = age-related macular degeneration; AREDS = Age-Related Eye Disease Study; BCVA = best-corrected visual acuity; GA = geographic atrophy; SD = standard deviation.

Prevalence of Imaging Biomarkers across Modalities and Disease Stages

The prevalence of AMD biomarkers varied substantially depending on imaging modality and disease stage (Table 1). Notably, the proportion of eyes with GA differed between modalities: GA was identified in 41 eyes (20%) using Beckman color-based classification, compared with 62 eyes (30%) using FAF, reflecting the higher sensitivity of FAF in detecting early or subclinical atrophic changes. Unless otherwise specified, percentages are reported per total number of eyes (n = 208).

Across the cohort, medium-sized drusen were present in 74% of eyes and large drusen in 61% on color imaging. Subretinal drusenoid deposits showed marked variability by modality, ranging from 10% on color fundus photography to 24% with infrared reflectance imaging and 16% on SD-OCT.

When stratified by disease stage, most biomarkers were more prevalent in GA eyes. On FAF, GA was detected in 100% of GA eyes. On SD-OCT, advanced atrophic features such as cRORA and external limiting membrane descent were present in 95% of GA eyes, compared with 24% and 30% in the overall cohort, respectively. Hyperreflective foci (76%) were also more frequent in GA eyes, indicating greater structural disruption. Similarly, SDD were more commonly identified in GA eyes on infrared imaging (34%) compared with the overall cohort (24%).

Drusen

We classified drusen according to their size and type, according to the Beckman simplified clinical classification.5 All eyes exhibited ≥1 type of macular drusen. Small drusen (<63 microns), also called drupelets, are not thought to have pathological significance, but considered a normal aging-related finding; for this reason, we excluded them from our analysis.

On FIAO imaging, drusen were identified in 70% of eyes overall and in 54% of GA eyes. The appearance of drusen on FIAO varied with size and morphology. We consistently observed a hyperreflective halo with a hyporeflective center (Fig 1), with larger drusen demonstrating more pronounced hyperreflective borders (Fig 2), which can vary according to the gaze position and angle of incidence of the light on the retina, as previously reported.49 The pattern of stage 3 SDD on FIAO (Fig 3) is characterized by a double signal of hyperreflective and hyporeflective border.

Figure 1.

Figure 1

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Example of intermediate drusen (>63 and <125 microns). The light blue box in the scanning laser ophthalmoscopy IR 30 degrees corresponds to the area covered by color fundus (A), IR (B), FlAO (C), BAF (D), and NIRAF (F) images. The green line corresponds to the OCT B scan in (E) showing small undulation of the RPE with accumulation of hyperreflective material beneath the RPE. Medium size drusen (yellow arrowhead) appear as yellowish punctiform well defined deposits on color fundus (A) and as a round lesion with hyporeflective center and hyperreflective rim on IR (B), while they are barely visible as hypoautoflurescent dots on NIRAF (F) and not detectable on BAF (D). Their aspect on FlAO (C) is a hyperreflective halo with a hyporeflective center. BAF = blue autofluorescence; FIAO = flood-illumination adaptive optics; IR = infrared reflectance; NIRAF = near-infrared autofluorescence; RPE = retinal pigment epithelium.

Figure 2.

Figure 2

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Example of large drusen (>125 microns). The light blue box in the scanning laser ophthalmoscopy IR 30 degrees corresponds to the area covered by color fundus (A), flood-illumination adaptive optics (C), and IR (D) images. The green line corresponds to the OCT B scan in (B) showing undulation of the retinal pigment epithelium with accumulation of hyperreflective material between the retinal pigment epithelium and the Bruch’s membrane (arrowhead). Large drusen (light blue line) looks like a yellowish undefined margin deposit on color fundus (A) and hyperreflective area on IR (D). Flood-illumination adaptive optics (C) shows hyperreflectivity and high contrast along the borders. IR = infrared reflectance.

Figure 3.

Figure 3

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Example of SDD. The light blue box in the SLO IR 30 degrees corresponds to the area covered by color fundus (A), IR (B), FlAO (C), BAF (D), and NIRAF (F) images. The green line corresponds to the OCT B scan in (E) showing irregular deposits between the RPE and the outer segments of photoreceptors with alteration of the ellipsoid zone and thickening of the RPE, located mostly on the perifoveal area and with an aspect of saw teeth in correspondence of stage 3 SDD. Subretinal drusenoid deposit (light green arrowhead) are barely visible on color photos (A), while on IR (B) they appear as round well-defined punctiform hyperreflective lesions with hyporeflective margins. On BAF (D), the aspect is typically of fine granular hypofluorescence, while on NIRAF (F), they are not visible. The pattern of stage 3 SDD on FlAO (C) is characterized by a double signal of hyperreflective and hyporeflective continuous ring with central iso or hypereflectivity. BAF = blue autofluorescence; FIAO = flood-illumination adaptive optics; NIRAF = near-infrared autofluorescence; RPE = retinal pigment epithelium; SDD = subretinal drusenoid deposit; SLO = scanning laser ophthalmoscopy.

Pigmentary Changes

Pigmentary abnormalities (Fig 4) were observed in 75% of eyes on color fundus photography with similar prevalence between iAMD and GA eyes. Pigment appearance varies by imaging modality. Pigment could manifest as areas of hyperautofluorescence on both blue and near infrared FAF yet the pattern and brightness varied between the 2 (as shown in Fig 4). Spectral-domain OCT B-scans precisely visualize pigmented clumps as HRF in 47% of eyes located in different layers both internal and external and mostly originating from the RPE layer. On FIAO imaging, pigmentary changes consistently appeared as HRCs, which were particularly concentrated at or near the margins of atrophic lesions that have a well-defined border.

Figure 4.

Figure 4

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Example of pigmentary changes. The light blue box in the SLO IR 30 degrees corresponds to the area covered by color fundus (A), IR (B), FlAO (C), BAF (D), and NIRAF (F) images. The green line corresponds to the OCT B scan in (E) showing serous pigment epithelium detachment and hyperreflective foci located in different retinal layers both internal and external with alteration of the RPE and ellipsoid layers. Distribution and shape (light green arrow) of the pigmentary changes are well visible on color fundus (A). They appear hyperreflective on IR (B) and hyperautofluorescent on both BAF (D) and NIRAF (F). On FlAO (C), multiple hyporeflective clumps are observed in correspondence to the pigmentary changes. BAF = blue autofluorescence; FIAO = flood-illumination adaptive optics; IR = infrared reflectance; NIRAF = near-infrared autofluorescence; RPE = retinal pigment epithelium; SLO = scanning laser ophthalmoscopy.

Margins of GA

On FIAO imaging, GA margins were categorized into distinct phenotypes, including well-defined borders, fuzzy borders, marginal hyporeflectance, and marginal pigmented clumps (Table 1). Well-defined borders were observed in 25% of all eyes and in 90% of GA eyes, while fuzzy borders were present in 13% of all eyes and 61% of GA eyes.

To quantitatively assess the relationship between FIAO border phenotypes and OCT-defined atrophy, a per-eye cross-tabulation was performed (Table 3). Well-defined FIAO borders were strongly associated with cRORA, being present in 67% of cRORA eyes, compared to 30% of iRORA eyes. Conversely, fuzzy borders and hyporeflective margins were more frequently observed in eyes with iRORA, 75% and 59%, respectively (Figs 5 and 6).

Table 3.

Per-Eye FlAO Border Categories versus OCT Findings

FlAO Well-Defined Border FlAO HRCs FlAO Fuzzy Border FlAO Hyporeflective Border
cRORA n = 49
n eyes, %		 33, 67% 30, 61% 21, 43% 27, 55%
iRORA n = 37
n eyes, %		 11, 30% 9, 24% 28, 75% 22, 59%
ELM descending n = 62 n eyes, % 50, 81% 48, 77% 33, 53% 51, 82%
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cRORA = complete retinal pigment epithelium and outer retinal atrophy; iRORA = incomplete retinal pigment epithelium and outer retinal atrophy; ELM = external limiting membrane; FIAO = flood-illumination adaptive optics; HRC = hyporeflective clump.

Figure 5.

Figure 5

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Example of GA area and cRORA. The light blue box in the SLO IR 30 degrees corresponds to the area covered by color fundus (A), IR (B), FlAO (C), BAF (D), and NIRAF (F) images. The green line corresponds to the OCT B scan in (E). Geographic atrophy (light blue dashes line) is characterized by the loss of the outer retina and is visible with all imaging modalities. In color fundus photography (A), GA appears as a discolored demarcated area. In IR SLO (B), GA area is hyperreflective surrounded by a hyporeflective margin, while on BAF (D) and NIRAF (F) it appears as a hypoautofluorescent zone with a hyperautofluorescent margin corresponding to the transition zone. Spectral-domain OCT B-scans (E) clearly show the cRORA alterations with the typical hypertransmission signal (light blue line). The FlAO (C) shows distinct well demarcated pattern of the border of the GA area with accumulation of hyperreflective clumps at the margin corresponding to the cRORA. BAF = blue autofluorescence; cRORA = complete retinal pigment epithelium and outer retinal atrophy; FIAO = flood-illumination adaptive optics; GA = geographic atrophy; IR = infrared reflectance; NIRAF = near-infrared autofluorescence; SLO = scanning laser ophthalmoscopy.

Figure 6.

Figure 6

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Example of GA area and iRORA. The light blue box in the SLO infrared reflectance 30 degrees corresponds to the area covered by color fundus (A), infrared reflectance (B), FlAO (C), and BAF (D) images. The green line corresponds to the OCT B scan in (E) showing the iRORA alterations with the typical hypertransmission signal. The FlAO (C) shows indistinct fuzzy pattern (light green arrowhead) of the border of the GA area corresponding to the iRORA. BAF = blue autofluorescence; FIAO = flood-illumination adaptive optics; GA = geographic atrophy; iRORA = incomplete retinal pigment epithelium and outer retinal atrophy; SLO = scanning laser ophthalmoscopy.

External limiting membrane descent showed a strong association with FIAO features, particularly hyporeflective margins (82%) and well-defined borders (81%), supporting its role as a marker of the transition zone in atrophy (Fig 7).

Figure 7.

Figure 7

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Example of transition zone and ELM descent. The light blue box in the SLO infrared reflectance 30 degrees corresponds to the area covered by color fundus (A), FlAO (C), and NIRAF (D). The green line corresponds to the OCT B scan in (B) showing ELM descent (light green arrowheads) corresponding to the transition zone. The FlAO (C) shows indistinct hyporeflective margins with hyporeflectance clumps (yellow arrowhead). ELM = external limiting membrane; FIAO = flood-illumination adaptive optics; NIRAF = near-infrared autofluorescence; SLO = scanning laser ophthalmoscopy.

Pigmented HRCs on FIAO were commonly associated with cRORA (61%) and external limiting membrane descent (77%), indicating that these features may reflect advanced structural disruption at the atrophic border.

Discussion

Our study describes common structural phenotypic characteristics of iAMD and advanced non-neovascular AMD through high-resolution multimodal imaging techniques. We focused here on 3 key well known diseases biomarkes: drusen, pigmentary changes, and GA margins. By integrating FIAO with established clinical imaging modalities, we aimed to relate microscopic phenotypes to current clinical classifications contributing to a deeper understanding of AMD pathology. Importantly, our findings should be interpreted as hypothesis-generating, given the predominantly qualitative nature of the analysis.

First result of this study is the confirmation of well-established knowledge that the prevalence of AMD biomarkers varies substantially across imaging modalities and disease stages. In particular, GA was more frequently detected using FAF than color fundus photography, consistent with the higher sensitivity of FAF for early or subclinical atrophic changes. Similarly, features such as SDD and HRF showed marked variability depending on the modality used, highlighting the importance of a multimodal approach for comprehensive phenotyping. These findings are in line with previous studies demonstrating that individual imaging techniques capture complementary aspects of AMD pathology.7,11, 12, 13, 14, 15, 16

One of the main contributions of this work is the systematic comparison between FIAO-derived GA border phenotypes and OCT-defined atrophy stages. While prior studies have described AO features in AMD,40,42,50 correlations with standardized OCT-based classifications13, 14, 15, 16 have remained limited. In our cohort, distinct or well-defined FIAO borders were more frequently associated with cRORA, whereas indistinct or hyporeflective margins were more often observed in incomplete atrophy (iRORA) and transition zones. The strong association between FIAO features and external external limiting membrane descent further supports the interpretation that these imaging patterns reflect different stages of atrophic progression. However, these associations remain descriptive and require validation in longitudinal and quantitative studies.

Regarding drusen, our observations confirm previous reports that multimodal imaging improves detection and characterization compared to color fundus photography alone.7,11, 12, 13, 14, 15, 16, 17, 18, 19 Additionally, our study underscores the significance of FIAO in visualizing subtle changes associated with drusen, overcoming resolution challenges posed by conventional imaging modalities.36, 37, 38, 39, 40 In fact, FIAO can detect drusen with high variability in contrast and morphology according to size and type.

In the literature, drusen detection and contrast in en face modalities including FIAO and AOSLO are not always concordant.42,48,50,51 In fact, the gaze position variation plays an important role on contrast reflectivity mostly of smaller and intermediate size drusen.

We recently developed an image acquisition and processing method to enhance the contrast of drusen from FIAO images acquired at different gaze positions.49 Using this method, more drusen were detected including those of smaller dimensions than those captured with the clinical gold standard technique, giving this approach potential to detect the development of small drusen in early-stage AMD. Directional imaging and the Stiles–Crawford effect are another important factor responsible for the contrast of drusen. Photoreceptor misalignment secondary to the presence of deposits might alter their waveguiding, thus reducing their backscattered light and creating variations in drusen contrast with gaze position. This appears principally at the borders of the drusen where there is a transition from normal pointing to directionally altered photoreceptors and seems to be consistent with the hyperreflective halo.52

Similarly, the characteristic multimodal imaging appearance of stage 3 SDD aligns with previously reported findings, supporting the reproducibility of these imaging signatures, while the characteristic double hyperreflective and hyporeflective halo we observed on FIAO is partly in accordance with what is reported by other authors.48 In fact, in our cohort, all SDDs presented a double hyper- and hyporeflective band, while Querques et al48 described a continuous/discontinuous halo on 14 images from 8 eyes with SDDs using FIAO. Stage 1 and 2 SDD are not discriminated, while only stage 3 presents this typical aspect probably secondary to the dislocation of cone outer segments.45,49 Recently published results of the Pinnacle Study reported similar results regarding medium and large drusen as well as stage 3 SDD, while we did not evaluate earlier stages of SDDs.51 These results confirm the reproducibility of this technique and the potential impact on clinical findings at earlier stages of the disease.

Pigmentary changes represent another key feature of AMD progression. In agreement with prior literature, HRF on OCT were frequently observed, particularly in GA eyes, and corresponded to HRCs on FIAO, typically accumulating at the margin of the GA area.42 On a longitudinal time-lapse study, we observed that some of the clumps migrate, and their pathway might depend on the various locations within the retina layers.41

While these structures are commonly attributed to migrating or transdifferentiated RPE cells,34,47,53,54 increasing evidence from immunohistological studies suggests a potential contribution from melanin-laden inflammatory cells (melanophages).29,55 Our multimodal observations are consistent with this hypothesis but do not allow definitive identification of the underlying cellular origin. These observations resonate with emerging evidence highlighting the role of inflammation, complement activation, and RPE dysfunction in AMD pathophysiology.23,29,56, 57, 58, 59 Thus, the biological interpretation of these features remains speculative and warrants further investigation combining imaging with histological or molecular approaches.

At the level of GA margins, FIAO enabled detailed visualization of transition zones that are not fully captured by conventional imaging. The accumulation of HRCs and their association with well-defined borders may reflect dynamic processes occurring at the interface between viable and atrophic retina.41, 42, 43,58

In our cohort, we observed HRCs mostly accumulated in correspondence of the well-defined borders. This is also the area of greatest progression of GA, and therefore different hypothesis could explain the possible nature of these HRCs. Histological studies show the presence of migrating subducted RPE cells,46 while near infrared AF images revealed intracellular melanosomes suggesting melanin loaded cells.42 We recently evaluated the AF of HRCs using fluorescence AOSLO combined with FIAO and showed that most HRCs are autofluorescent in the near infrared, consistent with the hypothesis that they contain AF organelles, most likely melanin acquired through phagocytosis of degenerated RPE.60

These observations are consistent with previous AO studies42,58 suggesting that cellular-scale changes at GA margins may precede or accompany lesion progression. If confirmed, such features could represent potential imaging biomarkers for monitoring disease progression or treatment response.

Despite these insights, several limitations should be considered. First, the study is cross-sectional, precluding assessment of temporal relationships or predictive value of the observed features. Second, the analysis is primarily qualitative, which limits the ability to define reproducible structural phenotypes or establish robust associations between imaging biomarkers and disease stage. The absence of quantitative measurements represents an important limitation. Future studies incorporating standardized quantitative metrics and automated analysis pipelines will be essential to validate and extend these findings.

Third, attempts to quantify cone photoreceptors using FIAO were unsuccessful due to the inability of both automated algorithms and manual grading to reliably distinguish cones from pathological structures in diseased retina. This highlights an important technical limitation of FIAO in AMD and underscores the need for multimodal validation, including OCT and higher-resolution AO modalities such as AOSLO.

Finally, the study population was almost exclusively White, which may limit the generalizability of the findings to more diverse populations. Given known differences in AMD prevalence, phenotype, and genetic risk across ethnic groups, future studies including more diverse cohorts will be important to assess the broader applicability of these imaging biomarkers. Moreover, it would be important to have the genetic analysis in a larger cohort to correlate cellular phenotype with genetic risk of disease progression.

In summary, we performed an ultrahigh-resolution analysis of phenotypic characteristics of an iAMD and advanced non-neovascular AMD cohort. Based on FIAO images, we described the drusen pattern typical for different types of drusen. We identified HRCs mostly located in the transition zone near the well-defined margin of the GA and corresponding to pigment redistribution as hallmark of disease. Finally, we reported detailed cellular characteristics of the GA borders and related these findings to their entities across clinical modalities. Further studies with longitudinal data will better define the role of these characteristics in the progression of the disease.

Manuscript no. XOPS-D-26-00043.

Footnotes

Supplemental material available atwww.ophthalmologyscience.org.

Preliminary results were presented as oral presentation at the XXVI biennial meeting of the International Society for Eye Research (Buenos Aires, Argentina, October 20-24, 2024).

Disclosure(s):

All authors have completed and submitted the ICMJE disclosures form.

The author(s) have made the following disclosure(s):

K.G.: Leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid – ARVO Annual Meeting Program Committee.

T.R.F.: Receipt of equipment, materials, drugs, medical writing, gifts or other services – Refreshments $88.79 Alimera.

J.A.S.: Royalties or licenses – Patent Royalties, Gensight; Consultant – Avista Therapeutics, LightStone Ventures, Essilor Luxottica, Tenpoint, UPMC Enterprises; Honoraria – Nature Neuroscience/ Dompe; Travel expenses – Gilbert Foundation, Foundation Fighting Blindness, Essilor Luxottica; Patents planned, issued or pending – Allotopic Expression, Rod-derived Cone Viability Factor and related patent; Stock or stock options – GenSight Biologics, Science Corp (former Pixium Vision), Sparing Vision, Prophesee, Tilak Healthcare, Avista, Tenpoint, SharpEye, Cilensee, Netramind, Injectsense.

E.A.R.: Honoraria – University of Waterloo; Travel expenses – Robotrak Technologies, Inc. support for attending conference in Nanjing, China; Patents planned, issued or pending – US 10,092,181, US 10,123,697, US 10,772,496.

This work was supported by a grant from the Edward N. & Della L. Thome Memorial Foundation (to J-A.S.). This work was also supported by NIH Grant R01EY030517 (to E.A.R.), NIH CORE Grant EY08098, the Eye and Ear Foundation of Pittsburgh, an unrestricted grant to the University of Pittsburgh Department of Ophthalmology from Research to Prevent Blindness, The sponsor or funding organization had no role in the design or conduct of this research.

HUMAN SUBJECTS: Human subjects were included in this study. The study was approved by the institutional review boards of the Quinze-Vingts National Ophthalmology Hospital and the University of Pittsburgh and adhered to the tenets of the Declaration of Helsinki. Written informed consent was acquired before enrollment for each participant following a detailed explanation of experimental procedures both verbally and in writing.

No animal subjects were used in this study.

Author Contributions:

Conception and design: Eandi, Grieve, Paques, Rossi

Analysis and interpretation: Eandi, Grieve, Gocho, Castro-Farias, Eddy, Friberg, Eller, Dansingani, Martel, Chhablani, Paques, Rossi

Data collection: Eandi, Snyder, Grieve, Chaumette, Paques, Rossi

Obtained funding: Grieve, Sahel, Paques, Rossi

Overall responsibility: Eandi, Snyder, Grieve, Sahel, Rossi

Supplementary Data

Table S1 and S2
mmc1.pdf (124.5KB, pdf)

References

  • 1.Zarbin M.A., Casaroli-Marano R.P., Rosenfeld P.J. Age-related macular degeneration: clinical findings, histopathology and imaging techniques. Dev Ophthalmol. 2014;53:1–32. doi: 10.1159/000358536. [DOI] [PubMed] [Google Scholar]
  • 2.Klein R., Peto T., Bird A., Vannewkirk M.R. The epidemiology of age-related macular degeneration. Am J Ophthalmol. 2004;137:486–495. doi: 10.1016/j.ajo.2003.11.069. [DOI] [PubMed] [Google Scholar]
  • 3.Congdon N., O'Colmain B., Klaver C.C.W., et al. Causes and prevalence of visual impairment among adults in the United States. Arch Ophthalmol. 2004;122:477–485. doi: 10.1001/archopht.122.4.477. [DOI] [PubMed] [Google Scholar]
  • 4.Tan C.S., Heussen F., Sadda S.R. Peripheral autofluorescence and clinical findings in neovascular and non-neovascular age-related macular degeneration. Ophthalmology. 2013;120:1271–1277. doi: 10.1016/j.ophtha.2012.12.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Ferris F.L., 3rd, Wilkinson C.P., Bird A., et al. Clinical classification of age-related macular degeneration. Ophthalmology. 2013;120:844–851. doi: 10.1016/j.ophtha.2012.10.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Zweifel S.A., Imamura Y., Spaide T.C., et al. Prevalence and significance of subretinal drusenoid deposits (reticular pseudodrusen) in age-related macular degeneration. Ophthalmology. 2010;117:1775–1781. doi: 10.1016/j.ophtha.2010.01.027. [DOI] [PubMed] [Google Scholar]
  • 7.Spaide R.F., Ooto S., Curcio C.A. Subretinal drusenoid deposits AKA pseudodrusen. Surv Ophthalmol. 2018;63:782–815. doi: 10.1016/j.survophthal.2018.05.005. [DOI] [PubMed] [Google Scholar]
  • 8.Xu X., Wang X., Sadda S.R., Zhang Y. Subtype-differentiated impacts of subretinal drusenoid deposits on photoreceptors revealed by adaptive optics scanning laser ophthalmoscopy. Graefes Arch Clin Exp Ophthalmol. 2020;258:1931–1940. doi: 10.1007/s00417-020-04774-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Spaide R.F. Improving the age-related macular degeneration construct. A new classification system. Retina. 2018;38:891–899. doi: 10.1097/IAE.0000000000001732. [DOI] [PubMed] [Google Scholar]
  • 10.Spaide R.F. Outer retinal atrophy after regression of subretinal drusenoid deposits as a newly recognized form of late age-related macular degeneration. Retina. 2013;33:1800–1808. doi: 10.1097/IAE.0b013e31829c3765. [DOI] [PubMed] [Google Scholar]
  • 11.Khan K.N., Mahroo O.A., Khan R.S., et al. Differentiating drusen: Drusen and drusen-like appearances associated with ageing, age-related macular degeneration, inherited eye disease and other pathological processes. Prog Retin Eye Res. 2016;53:70–106. doi: 10.1016/j.preteyeres.2016.04.008. [DOI] [PubMed] [Google Scholar]
  • 12.Sadda S.R., Guymer R., Holz F.G., et al. Consensus definition for atrophy associated with age-related macular degeneration on OCT: classification of atrophy report 3. Ophthalmology. 2018;125:537–548. doi: 10.1016/j.ophtha.2017.09.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Holz F.G., Sadda S.R., Staurenghi G., et al. Imaging protocols in clinical studies in advanced age-related macular degeneration: recommendations from classification of atrophy consensus meetings. Ophthalmology. 2017;124:464–478. doi: 10.1016/j.ophtha.2016.12.002. [DOI] [PubMed] [Google Scholar]
  • 14.Guymer R.H., Rosenfeld P.J., Curcio C.A., et al. Incomplete retinal pigment epithelial and outer retinal atrophy in age-related macular degeneration: classification of atrophy meeting report 4. Ophthalmology. 2020;127:394–409. doi: 10.1016/j.ophtha.2019.09.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Jaffe G.J., Chakravarthy U., Freund K.B., et al. Imaging features associated with progression to geographic atrophy in age-related macular degeneration: classification of atrophy meeting report 5. Ophthalmol Retina. 2021;5:855–867. doi: 10.1016/j.oret.2020.12.009. [DOI] [PubMed] [Google Scholar]
  • 16.Wu Z., Pfau M., Blodi B.A., et al. OCT signs of early atrophy in age-related macular degeneration: interreader agreement: classification of atrophy meetings report 6. Ophthalmol Retina. 2022;6:4–14. doi: 10.1016/j.oret.2021.03.008. [DOI] [PubMed] [Google Scholar]
  • 17.Christenbury J.G., Folgar F.A., O'Connell R.V., et al. Progression of intermediate age-related macular degeneration with proliferation and inner retinal migration of hyperreflective foci. Ophthalmology. 2013;120:1038–1045. doi: 10.1016/j.ophtha.2012.10.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Sadda S.R., Abdelfattah N.S., Lei J., et al. Spectral-domain OCT analysis of risk factors for macular atrophy development in the HARBOR study for neovascular age-related macular degeneration. Ophthalmology. 2020;127:1360–1370. doi: 10.1016/j.ophtha.2020.03.031. [DOI] [PubMed] [Google Scholar]
  • 19.Nassisi M., Lei J., Abdelfattah N.S., et al. OCT risk factors for development of late age-related macular degeneration in the fellow eyes of patients enrolled in the HARBOR study. Ophthalmology. 2019;126:1667–1674. doi: 10.1016/j.ophtha.2019.05.016. [DOI] [PubMed] [Google Scholar]
  • 20.Bindewald A., Schmitz-Valckenberg S., Jorzik J.J., et al. Classification of abnormal fundus autofluorescence patterns in the junctional zone of geographic atrophy in patients with age related macular degeneration. Br J Ophthalmol. 2005;89:874–878. doi: 10.1136/bjo.2004.057794. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Sparrow J.R., Dowling J.E., Bok D. Understanding RPE lipofuscin. Invest Ophthalmol Vis Sci. 2013;54:8325–8326. doi: 10.1167/iovs.13-13214. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Hageman G.S., Luthert P.J., Victor Chong N.H., et al. An integrated hypothesis that considers drusen as biomarkers of immune-mediated processes at the RPE-Bruch’s membrane interface in aging and age-related macular degeneration. Prog Retin Eye Res. 2001;20:705–732. doi: 10.1016/s1350-9462(01)00010-6. [DOI] [PubMed] [Google Scholar]
  • 23.van Lookeren Campagne M., Strauss E.C., Yaspan B.L. Age-related macular degeneration: complement in action. Immunobiology. 2016;221:733–739. doi: 10.1016/j.imbio.2015.11.007. [DOI] [PubMed] [Google Scholar]
  • 24.Wang X., Zhang Y., Zhang M.-N. Complement factor B polymorphism (rs641153) and susceptibility to age-related macular degeneration: evidence from published studies. Int J Ophthalmol. 2013;6:861–867. doi: 10.3980/j.issn.2222-3959.2013.06.21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Ferrington D.A., Kapphahn R.J., Leary M.M., et al. Increased retinal mtDNA damage in the CFH variant associated with age-related macular degeneration. Exp Eye Res. 2016;145:269–277. doi: 10.1016/j.exer.2016.01.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Maller J., George S., Purcell S., et al. Common variation in three genes, including a noncoding variant in CFH, strongly influences risk of age-related macular degeneration. Nat Genet. 2006;38:1055–1059. doi: 10.1038/ng1873. [DOI] [PubMed] [Google Scholar]
  • 27.Seddon J.M., Francis P.J., George S., et al. Association of CFH Y402H and LOC387715 A69S with progression of age-related macular degeneration. JAMA. 2007;297:1793–1800. doi: 10.1001/jama.297.16.1793. [DOI] [PubMed] [Google Scholar]
  • 28.Augustin S., Lam M., Lavalette S., et al. Melanophages give rise to hyperreflective foci in AMD, a disease-progression marker. J Neuroinflammation. 2023;20:28. doi: 10.1186/s12974-023-02699-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Weiter J.J., Delori F.C., Wing G.L., Fitch K.A. Relationship of senile macular degeneration to ocular pigmentation. Am J Ophthalmol. 1985;99:185–187. doi: 10.1016/0002-9394(85)90230-2. [DOI] [PubMed] [Google Scholar]
  • 30.Schmitz-Valckenberg S., Lara D., Nizari S., et al. Localisation and significance of in vivo near-infrared autofluorescent signal in retinal imaging. Br J Ophthalmol. 2011;95:1134–1139. doi: 10.1136/bjo.2010.189498. [DOI] [PubMed] [Google Scholar]
  • 31.Schraermeyer U., Heimann K. Current understanding on the role of retinal pigment epithelium and its pigmentation. Pigment Cell Res. 1999;12:219–236. doi: 10.1111/j.1600-0749.1999.tb00755.x. [DOI] [PubMed] [Google Scholar]
  • 32.Cideciyan A.V., Swider M., Jacobson S.G. Autofluorescence imaging with near-infrared excitation: normalization by reflectance to reduce signal from choroidal fluorophores. Invest Ophthalmol Vis Sci. 2015;56:3393–3406. doi: 10.1167/iovs.15-16726. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Cao D., Leong B., Messinger J.D., et al. Optical coherence tomography progression indicators in age-related macular degeneration, include transdifferentiated retinal pigment epithelium. Invest Ophthalmol Vis Sci. 2021;62:34. doi: 10.1167/iovs.62.10.34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Guillonneau X., Eandi C.M., Paques M., et al. On phagocytes and macular degeneration. Prog Retin Eye Res. 2017;61:98–128. doi: 10.1016/j.preteyeres.2017.06.002. [DOI] [PubMed] [Google Scholar]
  • 35.Godara P., Dubis A.M., Roorda A., et al. Adaptive optics retinal imaging: emerging clinical applications. Optom Vis Sci. 2010;87:930–941. doi: 10.1097/OPX.0b013e3181ff9a8b. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Rossi E.A., Chung M., Dubra A., et al. Imaging retinal mosaics in the living eye. Eye (Lond) 2011;25:301–308. doi: 10.1038/eye.2010.221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Williams D.R. Imaging single cells in the living retina. Vis Res. 2011;51:1379–1396. doi: 10.1016/j.visres.2011.05.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Roorda A., Romero-Borja F., Donnelly Iii W., et al. Adaptive optics scanning laser ophthalmoscopy. Opt Express. 2002;10:405–412. doi: 10.1364/oe.10.000405. [DOI] [PubMed] [Google Scholar]
  • 39.Liang J., Williams D.R., Miller D.T. Supernormal vision and high-resolution retinal imaging through adaptive optics. J Opt Soc Am A Opt Image Sci Vis. 1997;14:2884–2892. doi: 10.1364/josaa.14.002884. [DOI] [PubMed] [Google Scholar]
  • 40.Gocho K., Sarda V., Falah S., et al. Adaptive optics imaging of geographic atrophy. Invest Ophthalmol Vis Sci. 2013;54:3673–3680. doi: 10.1167/iovs.12-10672. [DOI] [PubMed] [Google Scholar]
  • 41.Paques M., Norberg N., Chaumette C., et al. Long term time-lapse imaging of geographic atrophy: a pilot study. Front Med. 2022;9 doi: 10.3389/fmed.2022.868163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Paques M., Meimon S., Rossant F., et al. Adaptive optics ophthalmoscopy: application to age-related macular degeneration and vascular diseases. Prog Retin Eye Res. 2018;66:1–16. doi: 10.1016/j.preteyeres.2018.07.001. [DOI] [PubMed] [Google Scholar]
  • 43.Vienola K.V., Zhang M., Snyder V.C., et al. Microstructure of the retinal pigment epithelium near-infrared autofluorescence in healthy young eyes and in patients with AMD. Sci Rep. 2020;10:9561. doi: 10.1038/s41598-020-66581-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Wang X., Sadda S.R., Ip M.S., et al. In vivo longitudinal measurement of cone photoreceptor density in intermediate age-related macular degeneration. Am J Ophthalmol. 2023;248:60–75. doi: 10.1016/j.ajo.2022.11.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Zhang Y., Wang X., Rivero E.B., et al. Photoreceptor perturbation around subretinal drusenoid deposits as revealed by adaptive optics scanning laser ophthalmoscopy. Am J Ophthalmol. 2014;158:584–596.e1. doi: 10.1016/j.ajo.2014.05.038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Zanzottera E.C., Messinger J.D., Ach T., et al. Subducted and melanotic cells in advanced age-related macular degeneration are derived from retinal pigment epithelium. Invest Ophthalmol Vis Sci. 2015;56:3269–3278. doi: 10.1167/iovs.15-16432. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Rossi E.A., Rangel-Fonseca P., Parkins K., et al. In vivo imaging of retinal pigment epithelium cells in age related macular degeneration. Biomed Opt Express. 2013;4:2527–2539. doi: 10.1364/BOE.4.002527. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Querques G., Kamami-Levy C., Blanco-Garavito R., et al. Appearance of medium–large drusen and reticular pseudodrusen on adaptive optics in age-related macular degeneration. Br J Ophthalmol. 2014;98:1522–1527. doi: 10.1136/bjophthalmol-2014-305455. [DOI] [PubMed] [Google Scholar]
  • 49.Rossi E.A., Norberg N., Eandi C., et al. A new method for visualizing drusen and their progression in flood-illumination adaptive optics ophthalmoscopy. Transl Vis Sci Technol. 2021;10:19. doi: 10.1167/tvst.10.14.19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Morgan J.I.W., Chui T.Y.P., Grieve K. Twenty-five years of clinical applications using adaptive optics ophthalmoscopy ∖[Invited∖] Biomed Opt Express. 2023;14:387–428. doi: 10.1364/BOE.472274. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Hagag A.M., Holmes C., Raza A., et al. Features of intermediate and late dry age-related macular degeneration on adaptive optics ophthalmoscopy: Pinnacle Study Report 8. Eye. 2025;39(6):1211–1216. doi: 10.1038/s41433-025-03607-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Miloudi C., Rossant F., Bloch I., et al. The negative cone mosaic: a new manifestation of the optical stiles-crawford effect in normal eyes. Invest Ophthalmol Vis Sci. 2015;56:7043–7050. doi: 10.1167/iovs.15-17022. [DOI] [PubMed] [Google Scholar]
  • 53.Nassisi M., Fan W., Shi Y., et al. Quantity of intraretinal hyperreflective foci in patients with intermediate age-related macular degeneration correlates with 1-year progression. Invest Ophthalmol Vis Sci. 2018;59:3431–3439. doi: 10.1167/iovs.18-24143. [DOI] [PubMed] [Google Scholar]
  • 54.Altay L., Scholz P., Schick T., et al. Association of hyperreflective foci present in early forms of age-related macular degeneration with known age-related macular degeneration risk polymorphisms. Invest Ophthalmol Vis Sci. 2016;57:4315–4320. doi: 10.1167/iovs.15-18855. [DOI] [PubMed] [Google Scholar]
  • 55.Calippe B., Augustin S., Beguier F., et al. Complement factor H inhibits CD47-mediated resolution of inflammation. Immunity. 2017;46:261–272. doi: 10.1016/j.immuni.2017.01.006. [DOI] [PubMed] [Google Scholar]
  • 56.Sennlaub F., Auvynet C., Calippe B., et al. CCR2(+) monocytes infiltrate atrophic lesions in age-related macular disease and mediate photoreceptor degeneration in experimental subretinal inflammation in Cx3cr1 deficient mice. EMBO Mol Med. 2013;5:1775–1793. doi: 10.1002/emmm.201302692. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Beguier F., Housset M., Roubeix C., et al. The 10q26 risk haplotype of age-related macular degeneration aggravates subretinal inflammation by impairing monocyte elimination. Immunity. 2020;53:429–441.e8. doi: 10.1016/j.immuni.2020.07.021. [DOI] [PubMed] [Google Scholar]
  • 58.Saßmannshausen M., Behning C., Weinz J., et al. Characteristics and spatial distribution of structural features in age-related macular degeneration: a macustar study report. Ophthalmol Retina. 2023;7:420–430. doi: 10.1016/j.oret.2022.12.007. [DOI] [PubMed] [Google Scholar]
  • 59.Guymer R.H., Wu Z., Hodgson L.A.B., et al. Subthreshold nanosecond laser intervention in age-related macular degeneration: the LEAD randomized controlled clinical trial. Ophthalmology. 2019;126:829–838. doi: 10.1016/j.ophtha.2018.09.015. [DOI] [PubMed] [Google Scholar]
  • 60.Borella Y., Danielsen N., Markle E.M., et al. Are the hypo-reflective clumps associated with age-related macular degeneration in adaptive optics ophthalmoscopy autofluorescent? Invest Ophthalmol Vis Sci. 2024;65:28. doi: 10.1167/iovs.65.10.28. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1 and S2
mmc1.pdf (124.5KB, pdf)

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