RETINAL STRUCTURAL CHANGES AFTER CESSATION OF PENTOSAN POLYSULFATE SODIUM.

PURPOSE: To quantify baseline and longitudinal structural changes post-cessation in patients with pentosan polysulfate sodium retinopathy. METHODS: This is a retrospective cohort study. Retinal thickness and volume of choroidal and hyperreflective retinal pigment epithelium excrescences were manually segmented from optical coherence tomography volume scans. Baseline measurements were compared against age-matched control subjects. Longitudinal measurements were performed on patients with follow-up data. RESULTS: Twenty-four eyes of 13 patients were included. At baseline, the mean total retinal thickness was lower in the pentosan polysulfate sodium retinopathy cohort than in age- and sex-matched control subjects (269.1 µ m vs. 290.2 µ m, P = 0.006). The median (range) of follow-up was 18.6 (4.1-34.7) months, with the mean last follow-up of 35.2 months after cessation. During the follow-up period, the thickness of the retina decreased significantly by 11.3 µ m (CI: 16.8, 5.8) ( P < 0.001), with an annual mean decrease of 6.70 µ m. However, the mean hyperreflective retinal pigment epithelium excrescence volume did not change significantly ( P = 0.140) over the follow-up period. CONCLUSION: After pentosan polysulfate sodium discontinuation, although retinal pigment epithelium excrescence volume do not change significantly, there continues to be a progressive long-term thinning of the retina, which continues at a rate greater than that associated with normal aging. Consequently, long-term follow-up is suggested to monitor patients with pentosan polysulfate sodium retinopathy.

ARTIFICIAL INTELLIGENCE FOR OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY-BASED DISEASE ACTIVITY PREDICTION IN AGE-RELATED MACULAR DEGENERATION.

PURPOSE: The authors hypothesize that optical coherence tomography angiography (OCTA)-visualized vascular morphology may be a predictor of choroidal neovascularization status in age-related macular degeneration (AMD). The authors thus evaluated the use of artificial intelligence (AI) to predict different stages of AMD disease based on OCTA en face 2D projections scans. METHODS: Retrospective cross-sectional study based on collected 2D OCTA data from 310 high-resolution scans. Based on OCT B-scan fluid and clinical status, OCTA was classified as normal, dry AMD, wet AMD active, and wet AMD in remission with no signs of activity. Two human experts graded the same test set, and a consensus grading between two experts was used for the prediction of four categories. RESULTS: The AI can achieve 80.36% accuracy on a four-category grading task with 2D OCTA projections. The sensitivity of prediction by AI was 0.7857 (active), 0.7142 (remission), 0.9286 (dry AMD), and 0.9286 (normal) and the specificity was 0.9524, 0.9524, 0.9286, and 0.9524, respectively. The sensitivity of prediction by human experts was 0.4286 active choroidal neovascularization, 0.2143 remission, 0.8571 dry AMD, and 0.8571 normal with specificity of 0.7619, 0.9286, 0.7857, and 0.9762, respectively. The overall AI classification prediction was significantly better than the human (odds ratio = 1.95, P = 0.0021). CONCLUSION: These data show that choroidal neovascularization morphology can be used to predict disease activity by AI; longitudinal studies are needed to better understand the evolution of choroidal neovascularization and features that predict reactivation. Future studies will be able to evaluate the additional predicative value of OCTA on top of other imaging characteristics (i.e., fluid location on OCT B scans) to help predict response to treatment.

Ultra-wide field and new wide field composite retinal image registration with AI-enabled pipeline and 3D distortion correction algorithm.

PURPOSE: This study aimed to compare a new Artificial Intelligence (AI) method to conventional mathematical warping in accurately overlaying peripheral retinal vessels from two different imaging devices: confocal scanning laser ophthalmoscope (cSLO) wide-field images and SLO ultra-wide field images. METHODS: Images were captured using the Heidelberg Spectralis 55-degree field-of-view and Optos ultra-wide field. The conventional mathematical warping was performed using Random Sample Consensus-Sample and Consensus sets (RANSAC-SC). This was compared to an AI alignment algorithm based on a one-way forward registration procedure consisting of full Convolutional Neural Networks (CNNs) with Outlier Rejection (OR CNN), as well as an iterative 3D camera pose optimization process (OR CNN + Distortion Correction [DC]). Images were provided in a checkerboard pattern, and peripheral vessels were graded in four quadrants based on alignment to the adjacent box. RESULTS: A total of 660 boxes were analysed from 55 eyes. Dice scores were compared between the three methods (RANSAC-SC/OR CNN/OR CNN + DC): 0.3341/0.4665/4784 for fold 1-2 and 0.3315/0.4494/4596 for fold 2-1 in composite images. The images composed using the OR CNN + DC have a median rating of 4 (out of 5) versus 2 using RANSAC-SC. The odds of getting a higher grading level are 4.8 times higher using our OR CNN + DC than RANSAC-SC (p < 0.0001). CONCLUSION: Peripheral retinal vessel alignment performed better using our AI algorithm than RANSAC-SC. This may help improve co-localizing retinal anatomy and pathology with our algorithm.

Efficacy and accuracy of artificial intelligence to overlay multimodal images from different optical instruments in patients with retinitis pigmentosa.

BACKGROUND: Retinitis pigmentosa (RP) represents a group of progressive, genetically heterogenous blinding diseases. Recently, relationships between measures of retinal function and structure are needed to help identify outcome measures or biomarkers for clinical trials. The ability to align retinal multimodal images, taken on different platforms, will allow better understanding of this relationship. We investigate the efficacy of artificial intelligence (AI) in overlaying different multimodal retinal images in RP patients. METHODS: We overlayed infrared images from microperimetry on near-infra-red images from scanning laser ophthalmoscope and spectral domain optical coherence tomography in RP patients using manual alignment and AI. The AI adopted a two-step framework and was trained on a separate dataset. Manual alignment was performed using in-house software that allowed labelling of six key points located at vessel bifurcations. Manual overlay was considered successful if the distance between same key points on the overlayed images was ≤1/2°. RESULTS: Fifty-seven eyes of 32 patients were included in the analysis. AI was significantly more accurate and successful in aligning images compared to manual alignment as confirmed by linear mixed-effects modelling (p < 0.001). A receiver operating characteristic analysis, used to compute the area under the curve of the AI (0.991) and manual (0.835) Dice coefficients in relation to their respective 'truth' values, found AI significantly more accurate in the overlay (p < 0.001). CONCLUSION: AI was significantly more accurate than manual alignment in overlaying multimodal retinal imaging in RP patients and showed the potential to use AI algorithms for future multimodal clinical and research applications.

Accuracy and Time Comparison Between Side-by-Side and Artificial Intelligence Overlayed Images.

BACKGROUND AND OBJECTIVE: The purpose of this study was to evaluate the accuracy and the time to find a lesion, taken in different platforms, color fundus photographs and infrared scanning laser ophthalmoscope images, using the traditional side-by-side (SBS) colocalization technique to an artificial intelligence (AI)-assisted technique. PATIENTS AND METHODS: Fifty-three pathological lesions were studied in 11 eyes. Images were aligned using SBS and AI overlaid methods. The location of each color fundus lesion on the corresponding infrared scanning laser ophthalmoscope image was analyzed twice, one time for each method, on different days, for two specialists, in random order. The outcomes for each method were measured and recorded by an independent observer. RESULTS: The colocalization AI method was superior to the conventional in accuracy and time (P < .001), with a mean time to colocalize 37% faster. The error rate using AI was 0% compared with 18% in SBS measurements. CONCLUSIONS: AI permitted a more accurate and faster colocalization of pathologic lesions than the conventional method. [Ophthalmic Surg Lasers Imaging Retina 2023;54:108-113.].

Automated Detection of Posterior Vitreous Detachment on OCT Using Computer Vision and Deep Learning Algorithms.

Objective: To develop automated algorithms for the detection of posterior vitreous detachment (PVD) using OCT imaging. Design: Evaluation of a diagnostic test or technology. Subjects: Overall, 42 385 consecutive OCT images (865 volumetric OCT scans) obtained with Heidelberg Spectralis from 865 eyes from 464 patients at an academic retina clinic between October 2020 and December 2021 were retrospectively reviewed. Methods: We developed a customized computer vision algorithm based on image filtering and edge detection to detect the posterior vitreous cortex for the determination of PVD status. A second deep learning (DL) image classification model based on convolutional neural networks and ResNet-50 architecture was also trained to identify PVD status from OCT images. The training dataset consisted of 674 OCT volume scans (33 026 OCT images), while the validation testing set consisted of 73 OCT volume scans (3577 OCT images). Overall, 118 OCT volume scans (5782 OCT images) were used as a separate external testing dataset. Main Outcome Measures: Accuracy, sensitivity, specificity, F1-scores, and area under the receiver operator characteristic curves (AUROCs) were measured to assess the performance of the automated algorithms. Results: Both the customized computer vision algorithm and DL model results were largely in agreement with the PVD status labeled by trained graders. The DL approach achieved an accuracy of 90.7% and an F1-score of 0.932 with a sensitivity of 100% and a specificity of 74.5% for PVD detection from an OCT volume scan. The AUROC was 89% at the image level and 96% at the volume level for the DL model. The customized computer vision algorithm attained an accuracy of 89.5% and an F1-score of 0.912 with a sensitivity of 91.9% and a specificity of 86.1% on the same task. Conclusions: Both the computer vision algorithm and the DL model applied on OCT imaging enabled reliable detection of PVD status, demonstrating the potential for OCT-based automated PVD status classification to assist with vitreoretinal surgical planning. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Robust AMD Stage Grading with Exclusively OCTA Modality Leveraging 3D Volume.

Age-related Macular Degeneration (AMD) is a degenerative eye disease that causes central vision loss. Optical Coherence Tomography Angiography (OCTA) is an emerging imaging modality that aids in the diagnosis of AMD by displaying the pathogenic vessels in the subretinal space. In this paper, we investigate the effectiveness of OCTA from the view of deep classifiers. To the best of our knowledge, this is the first study that solely uses OCTA for AMD stage grading. By developing a 2D classifier based on OCTA projections, we identify that segmentation errors in retinal layers significantly affect the accuracy of classification. To address this issue, we propose analyzing 3D OCTA volumes directly using a 2D convolutional neural network trained with additional projection supervision. Our experimental results show that we achieve over 80% accuracy on a four-stage grading task on both error-free and error-prone test sets, which is significantly higher than 60%, the accuracy of human experts. This demonstrates that OCTA provides sufficient information for AMD stage grading and the proposed 3D volume analyzer is more robust when dealing with OCTA data with segmentation errors.

ACCURATE REGISTRATION BETWEEN ULTRA-WIDE-FIELD AND NARROW ANGLE RETINA IMAGES WITH 3D EYEBALL SHAPE OPTIMIZATION.

The Ultra-Wide-Field (UWF) retina images have attracted wide attentions in recent years in the study of retina. However, accurate registration between the UWF images and the other types of retina images could be challenging due to the distortion in the peripheral areas of an UWF image, which a 2D warping can not handle. In this paper, we propose a novel 3D distortion correction method which sets up a 3D projection model and optimizes a dense 3D retina mesh to correct the distortion in the UWF image. The corrected UWF image can then be accurately aligned to the target image using 2D alignment methods. The experimental results show that our proposed method outperforms the state-of-the-art method by 30%.

A Mouse Model with Ablated Asparaginase and Isoaspartyl Peptidase 1 (Asrgl1) Develops Early Onset Retinal Degeneration (RD) Recapitulating the Human Phenotype.

We previously identified a homozygous G178R mutation in human ASRGL1 (hASRGL1) through whole-exome analysis responsible for early onset retinal degeneration (RD) in patients with cone-rod dystrophy. The mutant G178R ASRGL1 expressed in Cos-7 cells showed altered localization, while the mutant ASRGL1 in E. coli lacked the autocatalytic activity needed to generate the active protein. To evaluate the effect of impaired ASRGL1 function on the retina in vivo, we generated a mouse model with c.578_579insAGAAA (NM_001083926.2) mutation (Asrgl1mut/mut) through the CRISPR/Cas9 methodology. The expression of ASGRL1 and its asparaginase activity were undetectable in the retina of Asrgl1mut/mut mice. The ophthalmic evaluation of Asrgl1mut/mut mice showed a significant and progressive decrease in scotopic electroretinographic (ERG) response observed at an early age of 3 months followed by a decrease in photopic response around 5 months compared with age-matched wildtype mice. Immunostaining and RT-PCR analyses with rod and cone cell markers revealed a loss of cone outer segments and a significant decrease in the expression of Rhodopsin, Opn1sw, and Opn1mw at 3 months in Asrgl1mut/mut mice compared with age-matched wildtype mice. Importantly, the retinal phenotype of Asrgl1mut/mut mice is consistent with the phenotype observed in patients harboring the G178R mutation in ASRGL1 confirming a critical role of ASRGL1 in the retina and the contribution of ASRGL1 mutations in retinal degeneration.

Two-Step Registration on Multi-Modal Retinal Images via Deep Neural Networks.

Multi-modal retinal image registration plays an important role in the ophthalmological diagnosis process. The conventional methods lack robustness in aligning multi-modal images of various imaging qualities. Deep-learning methods have not been widely developed for this task, especially for the coarse-to-fine registration pipeline. To handle this task, we propose a two-step method based on deep convolutional networks, including a coarse alignment step and a fine alignment step. In the coarse alignment step, a global registration matrix is estimated by three sequentially connected networks for vessel segmentation, feature detection and description, and outlier rejection, respectively. In the fine alignment step, a deformable registration network is set up to find pixel-wise correspondence between a target image and a coarsely aligned image from the previous step to further improve the alignment accuracy. Particularly, an unsupervised learning framework is proposed to handle the difficulties of inconsistent modalities and lack of labeled training data for the fine alignment step. The proposed framework first changes multi-modal images into a same modality through modality transformers, and then adopts photometric consistency loss and smoothness loss to train the deformable registration network. The experimental results show that the proposed method achieves state-of-the-art results in Dice metrics and is more robust in challenging cases.

Perspective Distortion Correction for Multi-Modal Registration between Ultra-Widefield and Narrow-Angle Retinal Images.

Multi-modal retinal image registration between 2D Ultra-Widefield (UWF) and narrow-angle (NA) images has not been well-studied, since most existing methods mainly focus on NA image alignment. The stereographic projection model used in UWF imaging causes strong distortions in peripheral areas, which leads to inferior alignment quality. We propose a distortion correction method that remaps the UWF images based on estimated camera view points of NA images. In addition, we set up a CNN-based registration pipeline for UWF and NA images, which consists of the distortion correction method and three networks for vessel segmentation, feature detection and matching, and outlier rejection. Experimental results on our collected dataset shows the effectiveness of the proposed pipeline and the distortion correction method.

Assessing the validity of a cross-platform retinal image segmentation tool in normal and diseased retina.

Comparing automated retinal layer segmentation using proprietary software (Heidelberg Spectralis HRA + OCT) and cross-platform Optical Coherence Tomography (OCT) segmentation software (Orion). Image segmentations of normal and diseased (iAMD, DME) eyes were performed using both softwares and then compared to the 'gold standard' of manual segmentation. A qualitative assessment and quantitative (layer volume) comparison of segmentations were performed. Segmented images from the two softwares were graded by two masked graders and in cases with difference, a senior retina specialist made a final independent decisive grading. Cross-platform software was significantly better than the proprietary software in the segmentation of NFL and INL layers in Normal eyes. It generated significantly better segmentation only for NFL in iAMD and for INL and OPL layers in DME eyes. In normal eyes, all retinal layer volumes calculated by the two softwares were moderate-strongly correlated except OUTLY. In iAMD eyes, GCIPL, INL, ONL, INLY, TRV layer volumes were moderate-strongly correlated between softwares. In eyes with DME, all layer volume values were moderate-strongly correlated between softwares. Cross-platform software can be used reliably in research settings to study the retinal layers as it compares well against manual segmentation and the commonly used proprietary software for both normal and diseased eyes.

Robust Content-Adaptive Global Registration for Multimodal Retinal Images Using Weakly Supervised Deep-Learning Framework.

Multimodal retinal imaging plays an important role in ophthalmology. We propose a content-adaptive multimodal retinal image registration method in this paper that focuses on the globally coarse alignment and includes three weakly supervised neural networks for vessel segmentation, feature detection and description, and outlier rejection. We apply the proposed framework to register color fundus images with infrared reflectance and fluorescein angiography images, and compare it with several conventional and deep learning methods. Our proposed framework demonstrates a significant improvement in robustness and accuracy reflected by a higher success rate and Dice coefficient compared with other methods.

LEARNING TO CORRECT AXIAL MOTION IN OCT FOR 3D RETINAL IMAGING.

Optical Coherence Tomography (OCT) is a powerful technique for non-invasive 3D imaging of biological tissues at high resolution that has revolutionized retinal imaging. A major challenge in OCT imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose a convolutional neural network that learns to correct axial motion in OCT based on a single volumetric scan. The proposed method is able to correct large motion, while preserving the overall curvature of the retina. The experimental results show significant improvements in visual quality as well as overall error compared to the conventional methods in both normal and disease cases.

Artificial Intelligence for Automated Overlay of Fundus Camera and Scanning Laser Ophthalmoscope Images.

Purpose: The purpose of this study was to evaluate the ability to align two types of retinal images taken on different platforms; color fundus (CF) photographs and infrared scanning laser ophthalmoscope (IR SLO) images using mathematical warping and artificial intelligence (AI). Methods: We collected 109 matched pairs of CF and IR SLO images. An AI algorithm utilizing two separate networks was developed. A style transfer network (STN) was used to segment vessel structures. A registration network was used to align the segmented images to each. Neither network used a ground truth dataset. A conventional image warping algorithm was used as a control. Software displayed image pairs as a 5 × 5 checkerboard grid composed of alternating subimages. This technique permitted vessel alignment determination by human observers and 5 masked graders evaluated alignment by the AI and conventional warping in 25 fields for each image. Results: Our new AI method was superior to conventional warping at generating vessel alignment as judged by masked human graders (P < 0.0001). The average number of good/excellent matches increased from 90.5% to 94.4% with AI method. Conclusions: AI permitted a more accurate overlay of CF and IR SLO images than conventional mathematical warping. This is a first step toward developing an AI that could allow overlay of all types of fundus images by utilizing vascular landmarks. Translational Relevance: The ability to align and overlay imaging data from multiple instruments and manufacturers will permit better analysis of this complex data helping understand disease and predict treatment.

REPRODUCIBILITY OF MACULAR PIGMENT OPTICAL DENSITY MEASUREMENT BY TWO-WAVELENGTH AUTOFLUORESCENCE IN A CLINICAL SETTING.

PURPOSE: Macular pigment, composed of lutein, zeaxanthin, and meso-zeaxanthin, is postulated to protect against age-related macular degeneration, likely because of filtering blue light and its antioxidant properties. Macular pigment optical density (MPOD) is reported to be associated with macular function evaluated by visual acuity and multifocal electroretinogram. Given the importance of macular pigment, reliable and accurate measurement methods are important. The main purpose of this study is to determine the reproducibility of MPOD measurement by two-wavelength autofluorescence method using scanning laser ophthalmoscopy. METHODS: Sixty-eight eyes of 39 persons were enrolled in the study, including 11 normal eyes, 16 eyes with wet age-related macular degeneration, 16 eyes with dry age-related macular degeneration, 11 eyes with macular edema due to diabetic mellitus, branch retinal vein occlusion or macular telangiectasia, and 14 eyes with tractional maculopathy, including vitreomacular traction, epiretinal membrane, or macular hole. MPOD was measured with a two-wavelength (488 and 514 nm) autofluorescence method with the Spectralis HRA + OCT after pupil dilation. The measurement was repeated for each eye 10 minutes later. The analysis of variance and Bland-Altman plot were used to assess the reproducibility between the two measurements. RESULTS: The mean MPOD at eccentricities of 1° and 2° was 0.36 ± 0.17 (range: 0.04-0.69) and 0.15 ± 0.08 (range: -0.03 to 0.35) for the first measurement and 0.35 ± 0.17 (range: 0.02-0.68) and 0.15 ± 0.08 (range: -0.01 to 0.33) for the second measurement, respectively. The difference between the 2 measurements was not statistically significant, and the Bland-Altman plot showed 7.4% and 5.9% points outside the 95% limits of agreement, indicating an overall excellent reproducibility. Similarly, there is no significant difference between the first and second measurements of MPOD volume within eccentricities of 1°, 2°, and 6° radius, and the Bland-Altman plot showed 8.8%, 2.9%, and 4.4% points outside the 95% limits of agreement, respectively. The data for the reproducibility did not differ significantly among the various disease and normal eyes. CONCLUSION: Under routine examination conditions with pupil dilation, MPOD measurement by two-wavelength autofluorescence method showed a high reproducibility.

Comparative analysis of the retinal microvasculature visualized with fluorescein angiography and the retinal function imager.

PURPOSE: To assess the visualization of the retinal microvasculature with intravenous fluorescein angiography (IVFA) compared to the Retinal Function Imager (RFI). DESIGN: Multicenter, retrospective, observational case series. METHODS: Seven normal eyes and 26 eyes with various ocular diseases were imaged with both IVFA and the RFI. The ability to assess vessel loops, vertical collateral vessels, the size of the foveal avascular zone (FAZ), and degree of vessel branching were compared between IVFA and RFI images. RESULTS: The RFI visualized a greater number of vessel loops (1.3 vs 0.4 per eye) and vertical collateral vessels (4.42 vs 0.97 per eye) than IVFA. On average, higher order of vessel branching was seen with the RFI compared to IVFA (5.2 vs 4.6). The foveal avascular zone (FAZ) was more clearly delineated using the RFI and was significantly smaller when measured on RFI (0.35 vs 0.75 mm(2)). CONCLUSIONS: RFI, a noninvasive retinal imaging instrument, revealed vessel loops, vertical collateral vessels, the area of the FAZ, and order of vessel branching in greater detail than IVFA. This instrument may be helpful in understanding dynamic retinal vascular changes in a number of common ocular diseases, as well as in normal eyes.

Effect of change in drusen evolution on photoreceptor inner segment/outer segment junction.

PURPOSE: To evaluate the integrity of photoreceptor inner segment/outer segment (IS/OS) junction after change of drusen size in age-related macular degeneration using spectral-domain optical coherence tomography. METHODS: Drusen volume raster scans were performed with the Spectralis spectral-domain optical coherence tomography (Heidelberg Engineering) through 2,624 drusen in 14 eyes with clinically dry age-related macular degeneration, which had been longitudinally followed-up between 23 and 28 months without intervention (mean, 26.3 months). All eyes had Early Treatment Diabetic Retinopathy Study visual acuity. A total of 416 of 2,624 drusen were analyzed. RESULTS: Of 416 drusen, 83 (20%) were found to have regressed spontaneously (Group A), 212 (51%) showed no change in size (Group B), and 121 (29%) progressed (Group C). Mean drusen size of all drusen was 63.7 ± 25.7 µm. Cross-sectional analysis of drusen morphology showed a correlation between drusen size and disrupted IS/OS junction/photoreceptor integrity (r = -0.48, P < 0.001). Of the drusen that regressed over time, there was intact IS/OS junction integrity. Even drusen that caused a major disruption showed IS/OS restoration in 74% of the drusen (P < 0.001). CONCLUSION: Progression of drusen shows structural disruption of the IS/OS junction. After drusen regression, the IS/OS junction is either able to restore as drusen regress or was artifactitiously compressed and not initially visible because of the initial drusen compression of the IS/OS junctional line. Therefore, drusen evolution may play an important role in affecting the photoreceptor IS/OS junction integrity.

The microperimetry of resolved cotton-wool spots in eyes of patients with hypertension and diabetes mellitus.

BACKGROUND: Retinal cotton-wool spots (CWSs) are an important manifestation of retinovascular disease in hypertension (HTN) and diabetes mellitus (DM). Conventional automated perimetry data have suggested relative scotomas in resolved CWSs; however, this has not been well delineated using microperimetry. This study evaluates the retinal sensitivity in documented resolved CWSs using microperimetry. METHODS: Retinal CWSs that resolved after 10 to 119 months (median, 51 months) and normal control areas were photographed to document baseline lesions. Eye-tracking, image-stabilized microperimetry with simultaneous scanning laser ophthalmoscopy was performed over resolved CWSs, adjacent uninvolved areas near the lesion, and in location-matched normal patients (age-matched). RESULTS: A total of 16 eyes in patients with DM or HTN (34 resolved CWSs) and 16 normal control eyes (34 areas) were imaged. The mean (SD) sensitivity of resolved CWSs in the eyes of patients with HTN and DM was 11.67 (3.88) dB and 7.21 (5.48) dB, respectively. For adjacent control areas in the eyes of patients with HTN and DM, the mean (SD) sensitivity was 14.00 (2.89) dB and 11.80 (3.45) dB, respectively. Retinal sensitivity was significantly lower in areas of resolved CWSs than in the surrounding controls for patients with HTN (P = .01) and those with DM (P < .001). Scotomas in patients with DM were denser than those of patients with HTN (P < .05). CONCLUSIONS: Cotton-wool spots in patients with DM and HTN leave permanent relative scotomas detected by microperimetry. Scotomas are denser in eyes of patients with DM than in those with HTN. In addition, among patients with DM, adjacent retinas not involved with CWSs have lower retinal sensitivity than in age-matched controls.