Extent and Topography of Subretinal Drusenoid Deposits Associate With Rod-Mediated Vision in Aging and AMD: ALSTAR2 Baseline

Abstract

Purpose: In AMD, rod-mediated dark adaptation (RMDA) at 5° eccentricity is slower in eyes with subretinal drusenoid deposits (SDDs) than in eyes without. Here we quantified SDD burden using supervised deep learning for comparison to vision and photoreceptor topography.

Methods: In persons ≥60 years from the Alabama Study on Early Age-Related Macular Degeneration 2, normal, early AMD, and intermediate AMD eyes were classified by the AREDS nine-step system. A convolutional neural network was trained on 55°-wide near-infrared reflectance images for SDD segmentation. Trained graders annotated ground truth (SDD yes/no). Predicted and true datasets agreed (Dice coefficient, 0.92). Inference was manually proofread using optical coherence tomography. The mean SDD area (mm2) was compared among diagnostic groups (linear regression) and to vision (age-adjusted Spearman correlations). Fundus autofluorescence images were used to mask large vessels in SDD maps.

Results: In 428 eyes of 428 persons (normal, 218; early AMD, 120; intermediate AMD, 90), the mean SDD area differed by AMD severity (P < 0.0001): 0.16 ± 0.87 (normal), 2.48 ± 11.23 (early AMD), 11.97 ± 13.33 (intermediate AMD). Greater SDD area was associated with worse RMDA (r = 0.27; P < 0.0001), mesopic (r = -0.13; P = 0.02) and scotopic sensitivity (r = -0.17; P < 0.001). SDD topography peaked at 5° superior, extended beyond the Early Treatment of Diabetic Retinopathy Study grid and optic nerve, then decreased.

Conclusions: SDD area is associated with degraded rod-mediated vision. RMDA 5° (superior retina) probes where SDD is maximal, closer to the foveal center than the rod peak at 3 to 6 mm (10.4°-20.8°) superior and the further eccentric peak of rod:cone ratio. Topographic data imply that factors in addition to rod density influence SDD formation.

Figure 1.

Workflow to generate composite maps of SDD. The individual steps for creating maps of SDD. Five representative iAMD eyes are shown. (A) ETDRS grid is 6 mm in diameter. (A, B) Inference using the trained deep learning model to detect SDD pixels was applied to native 55° NIR images (A) segmented areas of SDD (B, light blue). (C) This segmentation is verified by a trained grader (author MA) on OCT imaging. (D) The final SDD segmentation is expressed as a binarized fundus image showing areas of SDD presence (white) and absence (black). (E) Native 55° FAF images matched to each NIR image are used to produce vessel masks. (F) An automated thresholding mechanism is applied to identify major retinal vessels depending on their grey value (black overlay). (G) Vessel masks are extracted from the original FAF images. The SDD maps and vessel masks are manually registered according to the position of the ONH and foveal center (not shown). (H) A central area of 1.25 mm in diameter centered on the fovea was excluded from the vessel masks, to avoid pixels that were low intensity owing to the macular xanthophyll pigment (red arrow). (I) Exemplary depiction of corresponding SDD maps and vessel masks in five eyes are combined to display the absolute number of pixels graded as SDD and not masked by retinal vessels. (J) In five exemplary cases, each pixel is color coded to the proportion of eyes labeled as having SDD and not masked by retinal vessels, relative to the total number of pixels not masked by retinal vessels. Scale bar in (A), 1 mm. Scale bar in (C), 200 µm, Color scale in (I): Unitless, count of eyes showing SDD. Color scale in (J) Unitless, proportion of eyes showing SDD.

Figure 2.

SDD distribution compared with rod distribution and rod:cone ratio. (A) Heatmap represents the localized prevalence of SDD showing the proportion of eyes with SDD at each pixel. Taking together SDD distribution of all eligible eyes (N = 212), the map reveals the highest area of SDD within and beyond the superior ETDRS subfields (parafovea and perifovea) and at the RMDA test location (gray circle). The dotted line describes the sETDRS grid, which encompasses the areas of highest rod density. Hardly any SDD are found at the foveal center. SDD extend to the nasal side of the ONH. (B) The highest density of rods can be found beyond the conventional ETDRS grid, within the sETDRS grid. The peak of rod density is marked by the black arrow head. The rod distribution does not match the SDD distribution precisely; however, the area of lowest SDD abundance (foveal center) corresponds with the lowest density of rods. (C) Rod:cone ratio is highest in the midperiphery (8–10 mm from the foveal center), whereas SDD burden rolls off at greater eccentricities from the peak shown here. Rod:cone ratio within the conventional ETDRS grid corresponds well with SDD morphology. Color scale in (B) displays 1000 cells/mm². sETDRS, supplement ETDRS.

Figure 3.

SDD distribution in 212 study and fellow eyes separated by disease group. (A, B) SDD manifest first near the ONH. (C) iAMD eyes show the highest proportion of SDD. SDD extend beyond the ETDRS grid and to the nasal side of the ONH. Color scale: Proportion of eyes showing SDD, per pixel.

Figure 4.

SDD where rods are sparse. (A–C) Multimodal imaging of a left eye of an 83-year-old male participant. AREDS-9 step disease stage 5 (iAMD). (A) True color confocal fundus image (iCare, Oy, Finland; inset 200% magnification) shows subfoveal SDD (red arrowhead). (B) Near-infrared imaging. Inset shows subfoveal SDD. Green line shows location of the OCT B-Scan. (C) OCT B-scan shows subfoveal stage 3 SDD (inset 200% magnification, red arrowhead). (D–F) Multimodal imaging of a left eye of an 80-year-old male participant. AREDS-9 step disease stage 3 (eAMD). (D) True color confocal fundus image (inset 200%) shows peripapillary SDD (red arrowhead). (E) Near infrared image. Inset shows peripapillary SDD (red arrowhead). Green line shows location of the OCT B-scan. (F) OCT B-scan shows subfoveal stage 3 SDD (inset 200% magnification, red arrowhead).