Quantification of expected information gain in visual acuity and contrast sensitivity tests.

We make use of expected information gain to quantify the amount of knowledge obtained from measurements in a population. In the first application, we compared the expected information gain in the Snellen, ETDRS, and qVA visual acuity (VA) tests, as well as in the Pelli-Robson, CSV-1000, and qCSF contrast sensitivity (CS) tests. For the VA tests, ETDRS generated more expected information gain than Snellen. Additionally, the qVA test with 15 rows (or 45 optotypes) generated more expected information gain than ETDRS, whether scored with VA threshold alone or with both VA threshold and VA range. Regarding the CS tests, CSV-1000 generated more expected information gain than Pelli-Robson, and the qCSF test with 25 trials generated more expected information gain than CSV-1000, whether scored with AULCSF or with CSF at six spatial frequencies. The active learning-based qVA and qCSF tests have the potential to generate more expected information gain than traditional paper chart tests. Although we have specifically applied it to compare VA and CS tests, expected information gain is a general concept that can be used to compare measurements in any domain.

Collective endpoint of visual acuity and contrast sensitivity function from hierarchical Bayesian joint modeling.

Clinical trials typically analyze multiple endpoints for signals of efficacy. To improve signal detection for treatment effects using the high-dimensional data collected in trials, we developed a hierarchical Bayesian joint model (HBJM) to compute a five-dimensional collective endpoint (CE5D) of contrast sensitivity function (CSF) and visual acuity (VA). The HBJM analyzes row-by-row CSF and VA data across multiple conditions, and describes visual functions across a hierarchy of population, individuals, and tests. It generates joint posterior distributions of CE5D that combines CSF (peak gain, peak frequency, and bandwidth) and VA (threshold and range) parameters. The HBJM was applied to an existing dataset of 14 eyes, each tested with the quantitative VA and quantitative CSF procedures in four Bangerter foil conditions. The HBJM recovered strong correlations among CE5D components at all levels. With 15 qVA and 25 qCSF rows, it reduced the variance of the estimated components by 72% on average. Combining signals from VA and CSF and reducing noises, CE5D exhibited significantly higher sensitivity and accuracy in discriminating performance differences between foil conditions at both the group and test levels than the original tests. The HBJM extracts valuable information about covariance of CSF and VA parameters, improves precision of the estimated parameters, and increases the statistical power in detecting vision changes. By combining signals and reducing noise from multiple tests for detecting vision changes, the HBJM framework exhibits potential to increase statistical power for combining multi-modality data in ophthalmic trials.

Quantification of Expected Information Gain in Visual Acuity and Contrast Sensitivity Tests.

We introduce expected information gain to quantify measurements and apply it to compare visual acuity (VA) and contrast sensitivity (CS) tests. We simulated observers with parameters covered by the visual acuity and contrast sensitivity tests and observers based on distributions of normal observers tested in three luminance and four Bangerter foil conditions. We first generated the probability distributions of test scores for each individual in each population in the Snellen, ETDRS and qVA visual acuity tests and the Pelli-Robson, CSV-1000 and qCSF contrast sensitivity tests and constructed the probability distributions of all possible test scores of the entire population. We then computed expected information gain by subtracting expected residual entropy from the total entropy of the population. For acuity tests, ETDRS generated more expected information gain than Snellen; scored with VA threshold only or with both VA threshold and VA range, qVA with 15 rows (or 45 optotypes) generated more expected information gain than ETDRS. For contrast sensitivity tests, CSV-1000 generated more expected information gain than Pelli-Robson; scored with AULCSF or with CS at six spatial frequencies, qCSF with 25 trials generated more expected information gain than CSV-1000. The active learning based qVA and qCSF tests can generate more expected information than the traditional paper chart tests. Although we only applied it to compare visual acuity and contrast sensitivity tests, information gain is a general concept that can be used to compare measurements and data analytics in any domain.

Hierarchical Bayesian modeling of contrast sensitivity functions in a within-subject design.

Recent development of the quick contrast sensitivity function (qCSF) method has made it possible to obtain accurate, precise, and efficient contrast sensitivity function (CSF) assessment. To improve statistical inference on CSF changes in a within-subject design, we developed a hierarchical Bayesian model (HBM) to compute the joint distribution of CSF parameters and hyperparameters at test, subject, and population levels, utilizing information within- and between-subjects and experimental conditions. We evaluated the performance of the HBM relative to a non-hierarchical Bayesian inference procedure (BIP) on an existing CSF dataset of 112 subjects obtained with the qCSF method in three luminance conditions (Hou, Lesmes, Kim, Gu, Pitt, Myung, & Lu, 2016). We found that the average d's of the area under log CSF (AULCSF) and CSF parameters between pairs of luminance conditions at the test-level from the HBM were 33.5% and 103.3% greater than those from the BIP analysis of AULCSF. The increased d' resulted in greater statistical differences between experimental conditions across subjects. In addition, simulations showed that the HBM generated accurate and precise CSF parameter estimates. These results have strong implications for the application of HBM in clinical trials and patient care.

Quantifying Uncertainty of the Estimated Visual Acuity Behavioral Function With Hierarchical Bayesian Modeling.

Purpose: The goal of this study is to develop a hierarchical Bayesian model (HBM) to better quantify uncertainty in visual acuity (VA) tests by incorporating the relationship between VA threshold and range across multiple individuals and tests. Methods: The three-level HBM consisted of multiple two-dimensional Gaussian distributions of hyperparameters and parameters of the VA behavioral function (VABF) at the population, individual, and test levels. The model was applied to a dataset of quantitative VA (qVA) assessments of 14 eyes in 4 Bangerter foil conditions. We quantified uncertainties of the estimated VABF parameters (VA threshold and range) from the HBM and compared them with those from the qVA. Results: The HBM recovered covariances between VABF parameters and provided better fits to the data than the qVA. It reduced the uncertainty of their estimates by 4.2% to 45.8%. The reduction of uncertainty, on average, resulted in 3 fewer rows needed to reach a 95% accuracy in detecting a 0.15 logMAR change of VA threshold or both parameters than the qVA. Conclusions: The HBM utilized knowledge across individuals and tests in a single model and provided better quantification of the uncertainty of the estimated VABF, especially when the number of tested rows was relatively small. Translational Relevance: The HBM can increase the accuracy in detecting VA changes. Further research is necessary to evaluate its potential in clinical populations.

Evaluating the Performance of qVFM in Mapping the Visual Field of Simulated Observers With Eye Diseases.

PURPOSE: Recently, we developed a novel active learning framework, qVFM, to map visual functions in the visual field. The method has been implemented and validated in measuring light sensitivity and contrast sensitivity visual field maps (VFMs) of normal observers. In this study, we evaluated the performance of the qVFM method in mapping the light sensitivity VFM of simulated patients with peripheral scotoma, glaucoma, age-related macular degeneration (AMD), and cataract. METHODS: For each simulated patient, we sampled 100 locations (60 × 60 degrees) of the visual field and compared the performance of the qVFM method with a procedure that tests each location independently (the qYN method) in a cued Yes/No task. Two different switch modules, the distribution sampling method (DSM) and parameter delivering method (PDM), were implemented in the qVFM method. Simulated runs of 1,200 trials were used to compare the accuracy and precision of the qVFM-DSM, qVFM-PDM and qYN methods. RESULTS: The qVFM method with both switch modules can provide accurate, precise, and efficient assessments of the light sensitivity VFM for the simulated patients, with the qVFM-PDM method better at detecting VFM deficits in the simulated glaucoma. CONCLUSIONS: The qVFM method can be used to characterize residual vision of simulated ophthalmic patients. The study sets the stage for further investigation with real patients and potential translation of the method into clinical practice.

Normal- and Low-Luminance Automated Quantitative Contrast Sensitivity Assessment in Eyes With Age-Related Macular Degeneration.

PURPOSE: To assess the effectiveness of an active learning approach to measuring the contrast sensitivity function (CSF) in patients with various degrees of dry age-related macular degeneration (AMD) under multiple luminance conditions. DESIGN: Cross-sectional study. METHODS: Patients with AMD (26 intermediate AMD, 19 AMD with subretinal drusenoid deposits [SDD], 20 geographic atrophy [GA]) and 23 age-matched controls were tested with the Manifold Contrast Vision Meter (Adaptive Sensory Technology) and the qCSF algorithm, which applies active learning to estimate a model of the CSF's global shape. Testing was performed under conditions of standard and low luminance. For each AMD severity, the area under log CSF (AULCSF) and contrast sensitivities at individual spatial frequencies were calculated for analysis. Low-luminance deficits (LLDs) for visual acuity (VA) and AULCSF were calculated as the difference between standard and low luminance values. RESULTS: Progressive decreases in AULCSF were observed as disease severity increased. For standard luminance, pairwise comparisons revealed significant differences between control/intermediate AMD (P < .0005), control/SDD (P < .0005), control/GA (P < .0005), and intermediate AMD/GA (P < .005). Similarly, for low luminance, pairwise comparisons revealed significant differences between the controls and each disease group (all P < .0005), in addition to significant differences between intermediate AMD/SDD (P < .005), and intermediate AMD/GA (P < .005). No correlations were found between LLD VA and LLD AULCSF in any AMD groups. CONCLUSIONS: Contrast sensitivity measured via qCSF under both standard- and low-luminance conditions correlates with advancing stages of dry AMD. The interaction between luminance and contrast sensitivity appears to reflect a different aspect of visual function than the interaction between luminance and VA.

Psychophysical Validation of a Novel Active Learning Approach for Measuring the Visual Acuity Behavioral Function.

Purpose: To evaluate the performance of the quantitative visual acuity (qVA) method in measuring the visual acuity (VA) behavioral function. Methods: We evaluated qVA performance in terms of the accuracy, precision, and efficiency of the estimated VA threshold and range in Monte Carlo simulations and a psychophysical experiment. We also compared the estimated VA threshold from the qVA method with that from the Electronic Early Treatment Diabetic Retinopathy Study (E-ETDRS) and Freiburg Visual Acuity Text (FrACT) methods. Four repeated measures with all three methods were conducted in four Bangerter foil conditions in 14 eyes. Results: In both simulations and psychophysical experiment, the qVA method quantified the full acuity behavioral function with two psychometric parameters (VA threshold and VA range) with virtually no bias and with high precision and efficiency. There was a significant correlation between qVA estimates of VA threshold and range in the psychophysical experiment. In addition, qVA threshold estimates were highly correlated with those from the E-ETDRS and FrACT methods. Conclusions: The qVA method can provide an accurate, precise, and efficient assessment of the full acuity behavioral function with both VA threshold and range. Translational Relevance: The qVA method can accurately, precisely, and efficiently assess the full VA behavioral function. Further research will evaluate the potential value of these rich measures for both clinical research and patient care.

Measuring Contrast Sensitivity Function With Active Learning in Retinal Vein Occlusion: A New Endpoint of Visual Function.

BACKGROUND AND OBJECTIVE: To characterize contrast sensitivity function (CSF) in patients with retinal vein occlusion (RVO) compared to age-matched controls using novel computerized contrast sensitivity (CS) testing with active learning algorithms. PATIENTS AND METHODS: CSF was prospectively measured in RVO patients with visual acuity (VA) greater than 20/200 and age-matched controls using the novel Manifold Contrast Vision Meter implementing quantitative CSF testing. Outcomes included area under the Log CSF (AULCSF), contrast acuity (CA), and CS thresholds at 1, 1.5, 3, 12, and 18 cycles per degree (cpd). A sub-analysis was performed on RVO eyes with good acuity (VA ≥ 20/30). RESULTS: Twenty-two eyes with RVO and 63 control eyes were included. Mean AULCSF (± standard deviation) in RVO eyes was 0.817 (0.28) compared to 1.217 (0.28) in controls (P < .0001). Mean contrast acuity in the RVO group was 1.054 (0.19) versus 1.286 ± 0.16 in controls (P < .0001). For individual spatial frequencies, CS loss at 6.0 cpd was most prominent in the RVO group. In 10 RVO eyes with VA of 20/30 or greater, mean AULCSF was 0.978 versus 1.217 in control eyes. (P = .008). CONCLUSIONS: CSF in eyes with RVO was found to be significantly reduced compared to age-matched controls. CSF seems to be a promising visual function endpoint with potential applications in clinical practice and future clinical trials. [Ophthalmic Surg Lasers Imaging Retina. 2020;51:392-400.].

New Technologies for Outcome Measures in Glaucoma: Review by the European Vision Institute Special Interest Focus Group.

Glaucoma is the leading cause of irreversible blindness worldwide, with an increasing prevalence. The complexity of the disease has been a major challenge in moving the field forward with regard to both pathophysiological insight and treatment. In this context, discussing possible outcome measures in glaucoma trials is of utmost importance and clinical relevance. A recent meeting of the European Vision Institute (EVI) special interest focus group was held on "New Technologies for Outcome Measures in Retina and Glaucoma," addressing both functional and structural outcomes, as well as translational hot topics in glaucoma and retina research. In conjunction with the published literature, this review summarizes the meeting focusing on glaucoma.

A novel Bayesian adaptive method for mapping the visual field.

Measuring visual functions such as light and contrast sensitivity, visual acuity, reading speed, and crowding across retinal locations provides visual-field maps (VFMs) that are extremely valuable for detecting and managing eye diseases. Although mapping light sensitivity is a standard glaucoma test, the measurement is often noisy (Keltner et al., 2000). Mapping other visual functions is even more challenging. To improve the precision of light-sensitivity mapping and enable other VFM assessments, we developed a novel hybrid Bayesian adaptive testing framework, the qVFM method. The method combines a global module for preliminary assessment of the VFM's shape and a local module for assessing individual visual-field locations. This study validates the qVFM method in measuring light sensitivity across the visual field. In both simulation and psychophysics studies, we sampled 100 visual-field locations (60° × 60°) and compared the performance of qVFM with the qYN procedure (Lesmes et al., 2015) that measured light sensitivity at each location independently. In the simulations, a simulated observer was tested monocularly for 1,000 runs with 1,200 trials/run, to compare the accuracy and precision of the two methods. In the experiments, data were collected from 12 eyes (six left, six right) of six human subjects. Subjects were cued to report the presence or absence of a target stimulus, with the luminance and location of the target adaptively selected in each trial. Both simulations and a psychological experiment showed that the qVFM method can provide accurate, precise, and efficient mapping of light sensitivity. This method can be extended to map other visual functions, with potential clinical signals for monitoring vision loss, evaluating therapeutic interventions, and developing effective rehabilitation for low vision.

Comparing Spatial Contrast Sensitivity Functions Measured With Digit and Grating Stimuli.

PURPOSE: The contrast sensitivity function (CSF) is measured traditionally with grating stimuli. Recently, we introduced a new set of digit stimuli to improve the efficiency of CSF tests for people unfamiliar with the Latin alphabet. Given the significant differences between grating and digit stimuli, we conducted this study to evaluate whether the estimated CSFs from the digit test are equivalent to those from the grating test. METHODS: The CSFs of five young (with Psi) and five older (with quick CSF [qCSF]) participants were measured with a two-alternative forced choice (2AFC) grating orientation identification task and a 10-digit identification task. The CSFs obtained from the two tasks were compared. RESULTS: The estimated CSFs from the two tasks matched well after controlling for stimulus types and performance levels. The root mean square error (RMSE) between the CSFs from the two tasks was 0.093 ± 0.029 (300 trials) and 0.131 ± 0.016 (100 trials) log10 units for young and older observers, respectively. To reach the same standard deviation (0.1 log10 units), the digit CSF test required fewer trials/less time than the classic grating CSF for young (60 vs. 90 trials) and older (15 vs. 21 trials) observers. The complicated behavioral responses of the observer in the 10-AFC digit identification task can be accounted by a model that consists of digit similarity and one single parameter of sensory noise (χ2[99] = 3.42, P = 0.999). CONCLUSIONS: The estimated CSFs from the digit test highly matched those obtained from the grating test; however, the digit test is much more efficient. TRANSLATIONAL RELEVANCE: The digit CSF test provides a compatible assessment of the CSF as the traditional grating CSF test with more efficiency.

Binocular Summation and Suppression of Contrast Sensitivity in Strabismus, Fusion and Amblyopia.

Purpose: Amblyopia and strabismus affect 2%-5% of the population and cause a broad range of visual deficits. The response to treatment is generally assessed using visual acuity, which is an insensitive measure of visual function and may, therefore, underestimate binocular vision gains in these patients. On the other hand, the contrast sensitivity function (CSF) generally takes longer to assess than visual acuity, but it is better correlated with improvement in a range of visual tasks and, notably, with improvements in binocular vision. The present study aims to assess monocular and binocular CSFs in amblyopia and strabismus patients. Methods: Both monocular CSFs and the binocular CSF were assessed for subjects with amblyopia (n = 11), strabismus without amblyopia (n = 20), and normally sighted controls (n = 24) using a tablet-based implementation of the quick CSF, which can assess a full CSF in <3 min. Binocular summation was evaluated against a baseline model of simple probability summation. Results: The CSF of amblyopic eyes was impaired at mid-to-high spatial frequencies compared to fellow eyes, strabismic eyes without amblyopia, and control eyes. Binocular contrast summation exceeded probability summation in controls, but not in subjects with amblyopia (with or without strabismus) or strabismus without amblyopia who were able to fuse at the test distance. Binocular summation was less than probability summation in strabismic subjects who were unable to fuse. Conclusions: We conclude that monocular and binocular contrast sensitivity deficits define important characteristics of amblyopia and strabismus that are not captured by visual acuity alone and can be measured efficiently using the quick CSF.

Measuring the Contrast Sensitivity Function Using the qCSF Method With 10 Digits.

PURPOSE: The Bayesian adaptive quick contrast sensitivity function (qCSF) method with a 10-letter identification task provides an efficient CSF assessment. However, large populations are unfamiliar with letters and cannot benefit from this test. To overcome the barrier, we conducted this study. METHOD: A new font for digits (0∼9) was created. The digits were then filtered with a raised cosine filter, rescaled to different sizes to cover spatial frequencies from 0.5 to 16 cycles per degree (cpd), and used as stimuli in a 10-alternative forced choice (10AFC) digit identification task. With the 10AFC digit identification task, the CSFs of five young and five old observers were measured using the qCSF and Psi methods. The estimates from the latter served as reference. RESULTS: The new digit font showed significantly improved similarity structure, Levene's test, F(1, 88) = 6.36, P = 0.014. With the 10-digit identification task, the CSFs obtained with the qCSF method matched well with those obtained with the Psi method (root mean square error [RMSE] = 0.053 log10 units). With approximately 30 trials, the precision of the qCSF method reached 0.1 log10 units. With approximately 75 trials, the precision of the CSFs obtained with the qCSF was comparable to that of the CSFs measured by the Psi method in 150 trials. CONCLUSIONS: The qCSF with the 10 digit identification task is validated for both young and old observers. TRANSLATIONAL RELEVANCE: The qCSF method with the 10-digit identification task provides an efficient and precise CSF test especially for people who are unfamiliar with letters.

Bayesian adaptive assessment of the reading function for vision: The qReading method.

Reading is a fundamental skill that can be significantly affected by visual disabilities. Reading performance, which typically is measured as reading speed with a reading chart, is a key endpoint for quantifying normal or abnormal vision. Despite its importance for clinical vision, existing reading tests for vision are time consuming and difficult to administer. Here, we propose a Bayesian adaptive method, the qReading method, for automated assessment of the reading speed versus print size function. We implemented the qReading method with a word/nonword lexical decision task and validated the method with computer simulations and a psychophysical experiment. Computer simulations showed that both the interrun standard deviation and intrarun half width of the 68.2% credible interval of the estimated reading speeds from the qReading method were less than 0.1 log10 units after 150 trials, with a bias of 0.05 log10 units. In the psychophysical experiment, reading functions measured by the qReading and Psi methods (Kontsevich & Tyler, 1999) in a word/nonword lexical decision task were compared. The estimated reading functions obtained with the qReading and Psi methods were highly correlated (r = 0.966 ± 0.004, p < 0.01). The precision of the qReading method with 225 trials was comparable to that of the Psi method with 450 trials. We conclude that the qReading method can precisely and accurately assess the reading function in much reduced time, with great promise in both basic research and clinical applications.

qPR: An adaptive partial-report procedure based on Bayesian inference.

Iconic memory is best assessed with the partial report procedure in which an array of letters appears briefly on the screen and a poststimulus cue directs the observer to report the identity of the cued letter(s). Typically, 6-8 cue delays or 600-800 trials are tested to measure the iconic memory decay function. Here we develop a quick partial report, or qPR, procedure based on a Bayesian adaptive framework to estimate the iconic memory decay function with much reduced testing time. The iconic memory decay function is characterized by an exponential function and a joint probability distribution of its three parameters. Starting with a prior of the parameters, the method selects the stimulus to maximize the expected information gain in the next test trial. It then updates the posterior probability distribution of the parameters based on the observer's response using Bayesian inference. The procedure is reiterated until either the total number of trials or the precision of the parameter estimates reaches a certain criterion. Simulation studies showed that only 100 trials were necessary to reach an average absolute bias of 0.026 and a precision of 0.070 (both in terms of probability correct). A psychophysical validation experiment showed that estimates of the iconic memory decay function obtained with 100 qPR trials exhibited good precision (the half width of the 68.2% credible interval = 0.055) and excellent agreement with those obtained with 1,600 trials of the conventional method of constant stimuli procedure (RMSE = 0.063). Quick partial-report relieves the data collection burden in characterizing iconic memory and makes it possible to assess iconic memory in clinical populations.

Evaluating the performance of the quick CSF method in detecting contrast sensitivity function changes.

The contrast sensitivity function (CSF) has shown promise as a functional vision endpoint for monitoring the changes in functional vision that accompany eye disease or its treatment. However, detecting CSF changes with precision and efficiency at both the individual and group levels is very challenging. By exploiting the Bayesian foundation of the quick CSF method (Lesmes, Lu, Baek, & Albright, 2010), we developed and evaluated metrics for detecting CSF changes at both the individual and group levels. A 10-letter identification task was used to assess the systematic changes in the CSF measured in three luminance conditions in 112 naïve normal observers. The data from the large sample allowed us to estimate the test-retest reliability of the quick CSF procedure and evaluate its performance in detecting CSF changes at both the individual and group levels. The test-retest reliability reached 0.974 with 50 trials. In 50 trials, the quick CSF method can detect a medium 0.30 log unit area under log CSF change with 94.0% accuracy at the individual observer level. At the group level, a power analysis based on the empirical distribution of CSF changes from the large sample showed that a very small area under log CSF change (0.025 log unit) could be detected by the quick CSF method with 112 observers and 50 trials. These results make it plausible to apply the method to monitor the progression of visual diseases or treatment effects on individual patients and greatly reduce the time, sample size, and costs in clinical trials at the group level.

A hierarchical Bayesian approach to adaptive vision testing: A case study with the contrast sensitivity function.

Measurement efficiency is of concern when a large number of observations are required to obtain reliable estimates for parametric models of vision. The standard entropy-based Bayesian adaptive testing procedures addressed the issue by selecting the most informative stimulus in sequential experimental trials. Noninformative, diffuse priors were commonly used in those tests. Hierarchical adaptive design optimization (HADO; Kim, Pitt, Lu, Steyvers, & Myung, 2014) further improves the efficiency of the standard Bayesian adaptive testing procedures by constructing an informative prior using data from observers who have already participated in the experiment. The present study represents an empirical validation of HADO in estimating the human contrast sensitivity function. The results show that HADO significantly improves the accuracy and precision of parameter estimates, and therefore requires many fewer observations to obtain reliable inference about contrast sensitivity, compared to the method of quick contrast sensitivity function (Lesmes, Lu, Baek, & Albright, 2010), which uses the standard Bayesian procedure. The improvement with HADO was maintained even when the prior was constructed from heterogeneous populations or a relatively small number of observers. These results of this case study support the conclusion that HADO can be used in Bayesian adaptive testing by replacing noninformative, diffuse priors with statistically justified informative priors without introducing unwanted bias.

Development of pattern vision following early and extended blindness.

Visual plasticity peaks during early critical periods of normal visual development. Studies in animals and humans provide converging evidence that gains in visual function are minimal and deficits are most severe when visual deprivation persists beyond the critical period. Here we demonstrate visual development in a unique sample of patients who experienced extended early-onset blindness (beginning before 1 y of age and lasting 8-17 y) before removal of bilateral cataracts. These patients show surprising improvements in contrast sensitivity, an assay of basic spatial vision. We find that contrast sensitivity development is independent of the age of sight onset and that individual rates of improvement can exceed those exhibited by normally developing infants. These results reveal that the visual system can retain considerable plasticity, even after early blindness that extends beyond critical periods.

Visual Function Endpoints to Enable Dry AMD Clinical Trials.

The slow progression of non-exudative age-related macular degeneration (dry AMD) presents challenges for drug discovery. The standard endpoint used for ophthalmic clinical trials, best-corrected visual acuity, is insensitive to the early stages and slow progression of dry AMD. Effective drug discovery for dry AMD treatments will therefore require novel applications of more effective visual function endpoints. This review will present candidates for visual function endpoints for dry AMD clinical trials. The promising visual assessments include contrast sensitivity, reading speed, microperimetry, and dark adaptation. Their adoption as exploratory endpoints in future trials will be critical for determining their accuracy, precision, and applicability, and ultimately determine their value for drug discovery.