Search

Spatial summation of perimetric stimuli has been used to derive conclusions about the spatial extent of retinal-cortical convergence, mostly from the size of the critical area of summation (Ricco’s area, RA) and critical number of Retinal Ganglion Cells (RGCs). However, spatial summation is known to change dynamically with stimulus duration. Conversely, temporal summation and critical duration also vary with stimulus size. Such an important and often neglected spatio-temporal interaction has important implications for modelling perimetric sensitivity in healthy observers and for formulating hypotheses for changes measured in disease. In this work, we performed experiments on visually heathy observers confirming the interaction of stimulus size and duration in determining summation responses in photopic conditions. We then propose a simplified computational model that captures these aspects of perimetric sensitivity by modelling thetotal retinal input, the combined effect of stimulus size, duration and retinal cones-to-RGC ratio. We additionally show that, in the macula, the enlargement of RA with eccentricity might not correspond to a constant critical number of RGCs, as often reported, but to a constant criticaltotal retinal input. We finally compare our results with previous literature and show possible implications for modelling disease, especially glaucoma.

to compare visual field (VF) progression between the 2 arms of the Treatment of Advanced Glaucoma Study (TAGS).Post hoc analysis of VF data from a 2-arm, multicenter, randomized controlled clinical trial.A total of 453 patients with newly diagnosed advanced open-angle glaucoma in at least 1 eye from 27 centers in the United Kingdom were randomized to either trabeculectomy (n = 227) or medication in their index eye (n = 226) and followed-up for 2 years with 2 24-2 VF tests at baseline, 4, 12, and 24 months. Data were analyzed for participants with a reliable VF (false positive rate15%) at baseline and at least 2 other time points. Average difference in rate of progression (RoP) was analyzed using a hierarchical Bayesian model. Time for each eye to progress from baseline beyond specific cut-offs (0.5, 1, 1.5, and 2 dB) was compared using survival analysis.This study analyzed 211 eyes in the trabeculectomy first arm and 203 eyes in the medication first arm. The average RoP (estimate [95% credible intervals]) was -0.59 [-0.88, -0.31] dB/year in the medication first arm and -0.40 [-0.67, -0.13] dB/year in the trabeculectomy first arm. The difference was not significant (Bayesian P-value = .353). More eyes progressed in the medication first arm, but this difference was not significant.There was no significant difference in the average RoP at 2 years.

To objectively grade posterior segment inflammation by measuring vitreous cells and haze on optical coherence tomography (OCT) scans and to compare OCT-based results with clinical grading.Evaluation of a diagnostic test.OCT scans of patients with uveitis were collected at 3 timepoints: with active (T0), clinically improving (T1), and resolved (T2) inflammation. At each visit, visual acuity and clinical grading of the vitreous haze (National Eye Institute [NEI] scale) were assessed. The density of vitreous cells was calculated on each OCT scan manually and automatically through a bespoke algorithm. Vitreous haze was indirectly measured on OCT scans by calculating the vitreous/retinal pigmented epithelium (VIT/RPE)-relative intensity manually and automatically. The variation of OCT-derived measurements over time was assessed. OCT-derived measurements were compared with clinical grading.A total of 222 scans from 74 eyes were analyzed. Both vitreous cell density and VIT/RPE-relative intensity significantly decreased over time. Cell density correlated with the clinical grading with a significant increase at each grade of the NEI scale. By contrast, the VIT/RPE-relative intensity was positively correlated with the clinical grade overall but there was no significant difference when comparing contiguous grades of the NEI scale. Infectious uveitis had a higher cell density. The intraclass correlation coefficient between manual and automatic assessment was 0.83 for cell density and 0.423 for the VIT/RPE-relative intensity.Posterior segment inflammation could be objectively graded through OCT scans. Vitreous cell density was assessed manually and automatically with good agreement and correlated better with NEI clinical grading compared with VIT/RPE-relative intensity.

Purpose: Vitreous haze (VH) is a key marker of inflammation in uveitis but limited by its subjectivity. Optical coherence tomography (OCT) has potential as an objective, noninvasive method for quantifying VH. We test the hypotheses that OCT can reliably quantify VH and the measurement is associated with slit-lamp based grading of VH. \ud \ud Methods: In this prospective study, participants underwent three repeated OCT macular scans to evaluate the within-eye reliability of the OCT vitreous intensity (VI). Association between OCT VI and clinical findings (including VH grade, phakic status, visual acuity [VA], anterior chamber cells, and macular thickness) were assessed. \ud \ud Results: One hundred nineteen participants were included (41 healthy participants, 32 patients with uveitis without VH, and 46 patients with uveitis with VH). Within-eye test reliability of OCT VI was high in healthy eyes and in all grades of VH (intraclass correlation coefficient [ICC] > 0.79). Average OCT VI was significantly different between healthy eyes and uveitic eyes without and uveitic eyes with VH, and was associated with increasing clinical VH grade (P < 0.05). OCT VI was significantly associated with VA, whereas clinical VH grading was not. Cataract was also associated with higher OCT VI (P = 0.03). \ud \ud Conclusions: OCT VI is a fast, noninvasive, objective, and automated method for measuring vitreous inflammation. It is associated with clinician grading of vitreous inflammation and VA, however, it can be affected by media opacities. \ud \ud Translational Relevance: OCT imaging for quantifying vitreous inflammation shows high within-eye repeatability and is associated with clinical grading of vitreous haze. OCT measurements are also associated with visual acuity but may be affected by structures anterior to the acquisition window, such as lens opacity and other anterior segment changes.

Purpose: To develop a Bayesian model (BM) for visual field (VF) progression accounting for the hierarchical, censored and heteroskedastic nature of the data. \ud \ud Methods: Three versions of a hierarchical BM were developed: a simple linear (Hi-linear); censored at 0 dB (Hi-censored); heteroskedastic censored (Hi-HSK). For the latter, we modeled the test variability according to VF sensitivity using a large test-retest cohort (1396 VFs, 146 eyes with glaucoma). We analyzed a large cohort of 44,371 VF tests from 3352 eyes from five glaucoma clinics. We quantified the bias in the estimated rate-of-progression, the detection of progression (Hit-rate [HR]), the median time-to-progression and the prediction error of future observations (mean absolute error [MAE]). HR and time-to-progression were compared at matched false-positive-rate (FPR), quantified using permutations of a separate test-retest cohort (360 tests, 30 eyes with glaucoma). BMs were compared to simple linear regression and Permutation-Analyses-of Pointwise-Linear-Regression. Differences in time-to-progression were tested using survival analysis.\ud \ud Results: Censored models showed the smallest bias in the rate-of-progression. The three BMs performed very similarly in terms of HR and time-to-progression and always better than the other methods. The average reduction in time-to-progression was 37% with the BMs (P < 0.001) at 5% FPR. MAE for prediction was very similar among methods.\ud \ud Conclusions: Bayesian hierarchical models improved the detection of VF progression. Accounting for censoring improves the precision of the estimates, but minimal effect is provided by accounting for heteroskedasticity.\ud \ud Translational Relevance: These results are relevant for quantification of VF progression in practice and for clinical trials.

Purpose: To provide structural and functional evidence of inner retinal loss in diabetes prior to vascular changes and interpret the structure-function relationship in the context of an established neural model.Methods: Data from one eye of 505 participants (134 with diabetes and no clinically evident vascular alterations of the retina) were included in this analysis. The data were collected as part of a large population-based study. Functional tests included best-corrected visual acuity, Pelli-Robson contrast sensitivity, mesopic microperimetry, and frequency doubling technology perimetry (FDT). Macular optical coherence tomography volume scans were collected for all participants. To interpret the structure-function relationship in the context of a neural model, ganglion cell layer (GCL) thickness was converted to local ganglion cell (GC) counts.Results: The GCL and inner plexiform layer were significantly thinner in participants with diabetes (P < 0.05), with no significant differences in the macular retinal nerve fiber layer or the outer retina. All functional tests except microperimetry showed a significant loss in diabetic patients (P < 0.05). Both FDT and microperimetry showed a significant relationship with the GC count (P < 0.05), consistent with predictions from a neural model for partial summation conditions. However, the FDT captured additional significant damage (P = 0.03) unexplained by the structural loss.Conclusions: Functional and structural measurements support early neuronal loss in diabetes. The structure-function relationship follows the predictions from an established neural model. Functional tests could be improved to operate in total summation conditions in the macula, becoming more sensitive to early loss.

The area of the foveal avascular zone (FAZ) from en face images of optical coherence tomography angiography (OCTA) is one of the most common measurement based on this technology. However, its use in clinic is limited by the high variation of the FAZ area across normal subjects, while the calculation of the volumetric measurement of the FAZ is limited by the high noise that characterizes OCTA scans. We designed an algorithm that exploits the higher signal-to-noise ratio of en face images to efficiently identify the capillary network of the inner retina in 3-dimensions (3D), under the assumption that the capillaries in separate plexuses do not overlap. The network is then processed with morphological operations to identify the 3D FAZ within the bounding segmentations of the inner retina. The FAZ volume and area in different plexuses were calculated for a dataset of 430 eyes. Then, the measurements were analyzed using linear mixed effect models to identify differences between three groups of eyes: healthy, diabetic without diabetic retinopathy (DR) and diabetic with DR. Results showed significant differences in the FAZ volume between the different groups but not in the area measurements. These results suggest that volumetric FAZ could be a better diagnostic detector than the planar FAZ. The efficient methodology that we introduced could allow the fast calculation of the FAZ volume in clinics, as well as providing the 3D segmentation of the capillary network of the inner retina., 6 pages, 3 figures, submitted to IEEE I2MTC 2021 conference

Purpose: To provide a consistent implementation of a retinal ganglion cell (RGC) displacement model proposed by Drasdo et al. for macular structure-function analysis, customizable by axial length (AL).\ud \ud Methods: The effect of axial length on the shape of the inner retina was measured on 235 optical coherence tomography (OCT) scans from healthy eyes, to provide evidence for geometric scaling of structures with eye size. Following this assumption, we applied the Drasdo model to map perimetric stimuli on the radially displaced RGCs using two different methods: Method 1 only displaced the center of the stimuli; Method 2 applied the displacement to every point on the edge of the stimuli. We compared the accuracy of the two methods by calculating, for each stimulus, the number of expected RGC receptive fields and the number RGCs calculated from the histology map, expected to be equivalent. The same calculation was repeated on RGC density maps derived from 28 OCT scans from 28 young healthy subjects (age < 40 years) to confirm our results on clinically available measurements.\ud \ud Results: The size of the retinal structures significantly increased with AL (P < 0.001) and was well predicted by geometric scaling. Method 1 systematically underestimated the RGC counts by as much as 60%. No bias was observed with Method 2.\ud \ud Conclusions: The Drasdo model can effectively account for AL assuming geometric scaling. Method 2 should be used for structure-function analyses.\ud \ud Translational Relevance: We developed a free web App in Shiny R to make our results available for researchers.

Quantifying intraocular inflammation is crucial in managing uveitis patients. We assessed the minimum B-scan density for reliable automated vitreous intensity (VI) assessment, using a novel approach based on optical coherence tomography (OCT). OCT volume scans centered on the macula were retrospectively collected in patients with uveitis. Nine B-scans per volume scan at fixed locations were automatically analyzed. The following B-scan selections were compared against the average score of 9 B-scans per volume scan as a reference standard: 1/3/5/7 central scans (1c/3c/5c/7c), 3 widely distributed scans (3w). Image data of 49 patients (31 females) were included. The median VI was 0.029 (IQR: 0.032). The intra-class-correlation coefficient of the VI across the 9 B-scans was 0.923. The median difference from the reference standard ranged between 0.001 (7c) and 0.006 (1c). It was significantly lower for scan selection 3w than 5c, p(adjusted) = 0.022, and lower for selection 7c than 3w, p(adjusted) = 0.003. The scan selections 7c and 3w showed the two highest areas under the receiver operating curve (0.985 and 0.965, respectively). Three widely distributed B-scans are sufficient to quantify VI reliably. Highest reliability was achieved using 7 central B-scans. Automated quantification of VI in uveitis is reliable and requires only few OCT B-scans.

Aims/hypothesis\ud Diabetic retinopathy is characterised by morphological lesions related to disturbances in retinal blood flow. It has previously been shown that the early development of retinal lesions temporal to the fovea may predict the development of treatment-requiring diabetic maculopathy. The aim of this study was to map accurately the area where lesions could predict progression to vision-threatening retinopathy.\ud \ud Methods\ud The predictive value of the location of the earliest red lesions representing haemorrhages and/or microaneurysms was studied by comparing their occurrence in a group of individuals later developing vision-threatening diabetic retinopathy with that in a group matched with respect to diabetes type, age, sex and age of onset of diabetes mellitus who did not develop vision-threatening diabetic retinopathy during a similar observation period.\ud \ud Results\ud The probability of progression to vision-threatening diabetic retinopathy was higher in a circular area temporal to the fovea, and the occurrence of the first lesions in this area was predictive of the development of vision-threatening diabetic retinopathy. The calculated peak value showed that the risk of progression was 39.5% higher than the average. There was no significant difference in the early distribution of lesions in participants later developing diabetic maculopathy or proliferative diabetic retinopathy.\ud \ud Conclusions/interpretation\ud The location of early red lesions in diabetic retinopathy is predictive of whether or not individuals will later develop vision-threatening diabetic retinopathy. This evidence should be incorporated into risk models used to recommend control intervals in screening programmes for diabetic retinopathy.

Purpose: To develop a method for automated detection and progression analysis of chorioretinal atrophic lesions using the combined information of standard infrared (IR) and autofluorescence (AF) fundus images.\ud \ud Methods: Eighteen eyes (from 16 subjects) with punctate inner choroidopathy were analyzed. Macular IR and blue AF images were acquired in all eyes with a Spectralis HRA+OCT device (Heidelberg Engineering, Heidelberg, Germany). Two clinical experts manually segmented chorioretinal lesions on the AF image. AF images were aligned to the corresponding IR. Two random forest models were trained to classify pixels of lesions, one based on the AF image only, the other based on the aligned IR-AF. The models were validated using a leave-one-out cross-validation and were tested against the manual segmentation to compare their performance. A time series from one eye was identified and used to evaluate the method based on the IR-AF in a case study.\ud \ud Results: The method based on the AF images correctly classified 95% of the pixels (i.e., in vs. out of the lesion) with a Dice's coefficient of 0.80. The method based on the combined IR-AF correctly classified 96% of the pixels with a Dice's coefficient of 0.84.\ud \ud Conclusions: The automated segmentation of chorioretinal lesions using IR and AF shows closer alignment to manual segmentation than the same method based on AF only. Merging information from multimodal images improves the automatic and objective segmentation of chorioretinal lesions even when based on a small dataset.\ud \ud Translational Relevance: Merged information from multimodal images improves segmentation performance of chorioretinal lesions.

Diabetic retinopathy (DR) is a chronic progressive sight-threatening disease of the retinal microvasculature associated with prolonged high elevated blood sugar levels (hyperglycemia) as well as other linked conditions such as hypertension. DR can be asymptomatic for long periods and as the disease progresses, patients can experience mild sight impairments. However, late stages of the disease can lead to severe sight- threatening conditions and potential blindness, if untreated. Vision loss can occur secondary to either leak in the center of the retina (macula)—leading to diabetic macular edema—or due to the development of new vessels that can bleed inside the eye. DR can manifest in forms of blurred and/or fluctuating vision, dark spots fluctuating during saccades, empty vision areas, and reduced vision. From a clinical perspective, DR can be classified as a nonproliferative stage (NPDR) or as a more ad- vanced proliferative stage (PDR). In NPDR, damages occur in the inner retinal blood vessels, which leak of fluids onto the retina, resulting in edema and swelling [1]. In this scenario, Frank [2] defines lesions as the major sign of retinopathy, and clusters them as microaneurysms (MAs), hemorrhages (HMs), hard and soft exudates (EXs), intraretinal microvascular abnormalities (IRMA), and venous beading (VB). NPDR can be graded as mild, moderate, or severe. Mild NPDR occurs with at least one MA. Moderate NPDR occurs with scattered retinal deep dot and blot HMs, and/or cotton wool spots CWS (soft exudates) and IRMAs but not as extensive as severe NPDR. The diagnosis of severe NPDR is based on the 4:2:1 rule of the ETDRS [2a]. Furthermore, IRMA can be present in each quadrant. With respect to PDR, abnormal development and angiogenesis of new vessels are the main representative character- istics of the disease: new blood vessels can grow in different areas of retinal surface or at the surface of the optic disc. The former are defined as New Vessels Elsewhere (NVE), the latter as New Vessels on Disc (NVD). These abnormal new vessels can easily bleed and are considered as the major leading cause of DR-related blindness.

Diabetic retinopathy (DR) is an asymptotic complication of diabetes and the leading cause of preventable blindness in the working-age population. Early detection and treatment of DR is critical to avoid vision loss. Exudates are one of the earliest and most prevalent signs of DR. In this work, we propose a novel two-stage method for the detection and segmentation of exudates in fundus photographs. In the first stage, a fully convolutional neural network architecture is trained to segment exudates using small image patches. Next, an auxilary codebook is built from network's intermediate layer output using incremental principal component analysis. Finally, outputs of both systems are combined to produce final result. Compared to other methods, the proposed algorithm does not require computation of candidate regions or removal of other anatomical structures. Furthermore, a transfer learning approach was applied to improve the performance of the system. The proposed method was evaluated using publicly available E-Ophtha datasets. It achieved better results than the state-of-the-art methods in terms of sensitivity and specificity metrics. The proposed method accomplished better results using a diseased//not diseased evaluation scenario which indicates its applicability for screening purposes. Simplicity, performance, efficiency and robustness of the proposed method demonstrate its suitability for diabetic retinopathy screening applications.

Purpose To describe and evaluate a free, online tool for automatically segmenting optical coherence tomography (OCT) images from different devices and computing summary measures such as retinal thickness. Methods ReLayer (https://relayer.online) is an online platform to which OCT scan images can be uploaded and analyzed. Results can be downloaded as plaintext (.csv) files. The segmentation method includes a novel, one-dimensional active contour model, designed to locate the inner limiting membrane, inner/outer segment, and retinal pigment epithelium. The method, designed for B-scans from Heidelberg Engineering Spectralis, was adapted for Topcon 3D OCT-2000 and OptoVue AngioVue. The method was applied to scans from healthy and pathological eyes, and was validated against segmentation by the manufacturers, the IOWA Reference Algorithms, and manual segmentation. Results Segmentation of a B-scan took ≤1 second. In healthy eyes, mean difference in retinal thickness from ReLayer and the reference standard was below the resolution of the Spectralis and 3D OCT-2000, and slightly above the resolution of the AngioVue. In pathological eyes, ReLayer performed similarly to IOWA (P = 0.97) and better than Spectralis (P < 0.001). Conclusions A free online platform (ReLayer) is capable of segmenting OCT scans with similar speed, accuracy, and reliability as the other tested algorithms, but offers greater accessibility. ReLayer could represent a valuable tool for researchers requiring the full segmentation, often not made available by commercial software. Translational Relevance A free online platform (ReLayer) provides free, accessible segmentation of OCT images: data often not available via existing commercial software.

The effects of eye disease cannot be depicted accurately using traditional media. Consequently, public understanding of eye disease is often poor. We present a VR/AR system for simulating common visual impairments, including disability glare, spatial distortions (Metamorphopsia), the selective blurring and filling-in of information across the visual field, and color vision deficits. Unlike most existing simulators, the simulations are informed by patients' self-reported symptoms, can be quantitatively manipulated to provide custom disease profiles, and support gaze-contingent presentation (i.e., when using a VR/AR headset that contains eye-tracking technology, such as the Fove0). Such a simulator could be used as a teaching/empathy aid, or as a tool for evaluating the accessibility of new products and environments.

PURPOSE: It has previously been shown that the intervals between screening examinations for diabetic retinopathy can be optimized by including individual risk factors for the development of the disease in the risk assessment. However, in some cases, the risk model calculating the screening interval may recommend a different interval than an experienced clinician. The purpose of this study was to evaluate the influence of factors unrelated to diabetic retinopathy and the distribution of lesions for discrepancies between decisions made by the clinician and the risk model.METHODS: Therefore, fundus photographs from 90 screening examinations where the recommendations of the clinician and a risk model had been discrepant were evaluated. Forty features were defined to describe the type and location of the lesions, and classification and ranking techniques were used to assess whether the features could predict the discrepancy between the grader and the risk model.RESULTS: Suspicion of tumours, retinal degeneration and vascular diseases other than diabetic retinopathy could explain why the clinician recommended shorter examination intervals than the model. Additionally, the regional distribution of microaneurysms/dot haemorrhages was important for defining a photograph as belonging to the group where both the clinician and the risk model had recommended a short screening interval as opposed to the other decision alternatives.CONCLUSIONS: Features unrelated to diabetic retinopathy and the regional distribution of retinal lesions may affect the recommendation of the examination interval during screening for diabetic retinopathy. The development of automated computerized algorithms for extracting information about the type and location of retinal lesions could be expected to further optimize examination intervals during screening for diabetic retinopathy.

BACKGROUND AND PURPOSE: The occurrence of intermediate uveitis, which is characterized by the presence of vitreous haze (VH), in patients with multiple sclerosis (MS) may be a sign of coexistent inflammatory central nervous system (CNS) disease activity. Using an automated algorithm to quantify VH on optical coherence tomography (OCT) scans, the aim was to investigate whether VH in MS patients is associated with signs of inflammatory CNS disease activity. \ud \ud METHODS: Vitreous haze was quantified on OCT macular volume scans of 290 MS patients and 85 healthy controls (HCs). The relationship between VH and clinical, retinal OCT and magnetic resonance imaging parameters of inflammatory disease activity was investigated using generalized estimating equations. \ud \ud RESULTS: Mean VH scores did not differ between patients and HCs (P = 0.629). Six patients (2.1%) showed values higher than the highest of the controls by HCs. VH scores did not differ between the different disease types or between eyes with and without a history of optic neuritis (P = 0.132). VH was not associated with inner nuclear layer volume on OCT (P = 0.233), cerebral T2 lesion load on magnetic resonance imaging (P = 0.416) or the development of new relapses (P = 0.205).\ud \ud CONCLUSION: In this study, OCT-based automated VH estimation did not detect increased vitreous inflammation in MS patients compared to HCs and did not find an association with CNS inflammatory burden.

Purpose Reduction of workload in the detection of microaneurysms (MA) from retinal photographs is crucial for the diagnosis and screening of diabetic retinopathy. Automatic algorithms for the detection of retinal lesions can help reduce human intervention especially when the lesions are present in large numbers. Methods Two methods for lesion detection were combined in a single algorithm, one based on the analyses of the contrast between dark peak-points and surrounding circular regions, and a second one based on the correlation between the intensity values in the photographs and a MA-template. The two individual methods and the two methods combined were tested separately to compare their performance on retinal images from 26 high-risk patients. Results Both individual lesion-detection methods missed clustered MAs. With the exclusion of grouped lesions, the two methods combined showed higher sensitivity and precision than the contrast and template methods alone, identifying 22% and 13% more lesions respectively. Conclusions The combination of the two methods can provide repeatable detection of unclustered MAs in photographs from high-risk patients. Manual intervention is only required to select grouped MAs and to adjust the automatic results, considerably reducing human workload.

Purpose:To investigate the structure–function relationship in eyes with drusen with mesopic and scotopic microperimetry. Methods:We analyzed structural and functional data from 43 eyes with drusen. Functional data were acquired with mesopic and scotopic two-color (red and cyan) microperimetry. Normative values were calculated using data from 56 healthy eyes. Structural measurements were green autofluorescence and dense macular optical coherence tomography scans. The latter were used to calculate the retinal pigment epithelium elevation (RPE-E) and the photoreceptor reflectivity ratio (PRR). The pointwise structure–function relationship was measured with linear mixed models having the log-transformed structural parameters as predictors and the sensitivity loss (SL, deviation from normal) as the response variable. Results:In the univariable analysis, the structural predictors were all significantly correlated (P< 0.05) with the SL in the mesopic and scotopic tests. In a multivariable model, mesopic microperimetry yielded the best structure–function relationship. All predictors were significant (P< 0.05), but the predictive power was weak (bestR2= 0.09). The relationship was improved when analyzing locations with abnormal RPE-E (bestR2= 0.18). Conclusions:Mesopic microperimetry shows better structure–function relationship compared to scotopic microperimetry; the relationship is weak, likely due to the early functional damage and the small number of tested locations affected by drusen. The relationship is stronger when locations with drusen are isolated for the mesopic and scotopic cyan test. Translational Relevance:These results could be useful to devise integrated structure–function methods to detect disease progression in intermediate age-related macular degeneration.

Purpose: To investigate the structure���function relationship in eyes with drusen with mesopic and scotopic microperimetry. Methods: We analyzed structural and functional data from 43 eyes with drusen. Functional data were acquired with mesopic and scotopic two-color (red and cyan) microperimetry. Normative values were calculated using data from 56 healthy eyes. Structural measurements were green autofluorescence and dense macular optical coherence tomography scans. The latter were used to calculate the retinal pigment epithelium elevation (RPE-E) and the photoreceptor reflectivity ratio (PRR). The pointwise structure���function relationship was measured with linear mixed models having the log-transformed structural parameters as predictors and the sensitivity loss (SL, deviation from normal) as the response variable. Results: In the univariable analysis, the structural predictors were all significantly correlated (P < 0.05) with the SL in the mesopic and scotopic tests. In a multivariable model, mesopic microperimetry yielded the best structure���function relationship. All predictors were significant (P < 0.05), but the predictive power was weak (best R2 = 0.09). The relationship was improved when analyzing locations with abnormal RPE-E (best R2 = 0.18). Conclusions: Mesopic microperimetry shows better structure���function relationship compared to scotopic microperimetry; the relationship is weak, likely due to the early functional damage and the small number of tested locations affected by drusen. The relationship is stronger when locations with drusen are isolated for the mesopic and scotopic cyan test. Translational Relevance: These results could be useful to devise integrated structure���function methods to detect disease progression in intermediate age-related macular degeneration., DPC and GM received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant 116076 (Macustar). This joint undertaking receives support from the European Union's Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations (EFPIA). The funding for the NICOLA and NISA studies are reported in the acknowledgments as part of a mandatory statement for these studies. The article was first published in TVST. The final publication is available at https://tvst.arvojournals.org/article.aspx?articleid=2772110