Are Current Methods of Measuring Dark Adaptation Effective in Detecting the Onset and Progression of Age-Related Macular Degeneration? A Systematic Literature Review

Assessment of the Classification of Age-Related Macular Degeneration Severity from the Northern Ireland Sensory Ageing Study Using a Measure of Dark Adaptation

Purpose

To assess the differences in rod-mediated dark adaptation (RMDA) between different grades of age-related macular degeneration (AMD) severity using an OCT-based criterion compared with those of AMD severity using the Beckman color fundus photography (CFP)-based classification and to assess the association between the presence of subretinal drusenoid deposits (SDDs) and RMDA at different grades of AMD severity using an OCT-based classification.

Design

Cross-sectional study.

Participants

Participants from the Northern Ireland Sensory Ageing study (Queen's University Belfast).

Methods

Complete RMDA (rod-intercept time [RIT]) data, CFP, and spectral-domain OCT images were extracted. Participants were stratified into 4 Beckman groups (omitting late-stage AMD) and 3 OCT-based groups. The presence and stage of SDDs were identified using OCT.

Main Outcome Measures

Rod-intercept time data (age-corrected).

Results

Data from 459 participants (median [interquartile range] age, 65 [59-71] years) were stratified by both the classifications. Subretinal drusenoid deposits were detected in 109 eyes. The median (interquartile range) RMDA for the Beckman classification (Beckman 0-3, with 3 being intermediate age-related macular degeneration [iAMD]) groups was 6.0 (4.5-8.7), 6.6 (4.7-10.5), 5.7 (4.4-7.4), and 13.2 (6-21.1) minutes, respectively. OCT classifications OCT0-OCT2 yielded different median (interquartile range) values: 5.8 (4.5-8.5), 8.4 (5.2-13.3), and 11.1 (5.3-20.1) minutes, respectively. After correcting for age, eyes in Beckman 3 (iAMD) had statistically significantly worse RMDA than eyes in the other Beckman groups (P ≤ 0.005 for all), with no statistically significant differences between the other Beckman groups. Similarly, after age correction, eyes in OCT2 had worse RMDA than eyes in OCT0 (P ≤ 0.001) and OCT1 (P < 0.01); however, there was no statistically significant difference between eyes in OCT0 and eyes in OCT1 (P = 0.195). The presence of SDDs was associated with worse RMDA in OCT2 (P < 0.01) but not in OCT1 (P = 0.285).

Conclusions

Eyes with a structural definition of iAMD have delayed RMDA, regardless of whether a CFP- or OCT-based criterion is used. In this study, after correcting for age, the RMDA did not differ between groups of eyes defined to have early AMD or normal aging, regardless of the classification. The presence of SDDs has some effect on RMDA at different grades of AMD severity.

Deep Learning-Based Modeling of the Dark Adaptation Curve for Robust Parameter Estimation

Purpose

This study investigates deep-learning (DL) sequence modeling techniques to reliably fit dark adaptation (DA) curves and estimate their key parameters in patients with age-related macular degeneration (AMD) to improve robustness and curve predictions.

Methods

A long-short-term memory autoencoder was used as the DL method to model the DA curve. The performance was compared against the classical nonlinear regression method using goodness-of-fit and repeatability metrics. Experiments were performed to predict the latter portion of the curve using data from early measurements. The prediction accuracy was quantified as the rod intercept time (RIT) prediction error between predicted and actual curves.

Results

The two models had comparable goodness-of-fit measures, with root mean squared error (RMSE; SD) = 0.11 (0.04) log-units (LU) for the classical model and RMSE = 0.13 (0.06) LU for the DL model. Repeatability of the curve fits evaluated after introduction of random perturbations, and after performing repeated testing, demonstrated superiority of the DL method, especially among parameters related to cone decay. The DL method exhibited superior ability to predict the curve and RIT using points prior to -2 LU, with 3.1 ± 3.1 minutes RIT prediction error, compared to 19.1 ± 18.6 minutes RIT error for the classical method.

Conclusions

The parameters obtained from the DL method demonstrated superior robustness as well as predictability of the curve. These could provide important advances in using multiple DA curve parameters to characterize AMD severity.

Translational Relevance

Dark adaptation is an important functional measure in studies of AMD and curve modeling using DL methods can lead to improved clinical trial end points.

Dark Adaptation and Its Role in Age-Related Macular Degeneration

Dark adaptation (DA) refers to the slow recovery of visual sensitivity in darkness following exposure to intense or prolonged illumination, which bleaches a significant amount of the rhodopsin. This natural process also offers an opportunity to understand cellular function in the outer retina and evaluate for presence of disease. How our eyes adapt to darkness can be a key indicator of retinal health, which can be altered in the presence of certain diseases, such as age-related macular degeneration (AMD). A specific focus on clinical aspects of DA measurement and its significance to furthering our understanding of AMD has revealed essential findings underlying the pathobiology of the disease. The process of dark adaptation involves phototransduction taking place mainly between the photoreceptor outer segments and the retinal pigment epithelial (RPE) layer. DA occurs over a large range of luminance and is modulated by both cone and rod photoreceptors. In the photopic ranges, rods are saturated and cone cells adapt to the high luminance levels. However, under scotopic ranges, cones are unable to respond to the dim luminance and rods modulate the responses to lower levels of light as they can respond to even a single photon. Since the cone visual cycle is also based on the Muller cells, measuring the impairment in rod-based dark adaptation is thought to be particularly relevant to diseases such as AMD, which involves both photoreceptors and RPE. Dark adaptation parameters are metrics derived from curve-fitting dark adaptation sensitivities over time and can represent specific cellular function. Parameters such as the cone-rod break (CRB) and rod intercept time (RIT) are particularly sensitive to changes in the outer retina. There is some structural and functional continuum between normal aging and the AMD pathology. Many studies have shown an increase of the rod intercept time (RIT), i.e., delays in rod-mediated DA in AMD patients with increasing disease severity determined by increased drusen grade, pigment changes and the presence of subretinal drusenoid deposits (SDD) and association with certain morphological features in the peripheral retina. Specifications of spatial testing location, repeatability of the testing, ease and availability of the testing device in clinical settings, and test duration in elderly population are also important. We provide a detailed overview in light of all these factors.

The role of dark adaptation in understanding early AMD

The main aim of the paper is to discuss current knowledge on how Age Related Macular Degeneration (AMD) affects Dark Adaptation (DA). The paper is divided into three parts. Firstly, we outline some of the molecular mechanisms that control DA. Secondly, we review the psychophysical issues and the corresponding analytical techniques. Finally, we characterise the link between slowed DA and the morphological abnormalities in early AMD. Historically, DA has been regarded as too cumbersome for widespread clinical application. Yet the technique is extremely useful; it is widely accepted that the psychophysically obtained slope of the second rod-mediated phase of the dark adaptation function is an accurate assay of photoreceptor pigment regeneration kinetics. Technological developments have prompted new ways of generating the DA curve, but analytical problems remain. A simple potential solution to these, based on the application of a novel fast mathematical algorithm, is presented. This allows the calculation of the parameters of the DA curve in real time. Improving current management of AMD will depend on identifying a satisfactory endpoint for evaluating future therapeutic strategies. This must be implemented before the onset of severe disease. Morphological changes progress too slowly to act as a satisfactory endpoint for new therapies whereas functional changes, such as those seen in DA, may have more potential in this regard. It is important to recognise, however, that the functional changes are not confined to rods and that building a mathematical model of the DA curve enables the separation of rod and cone dysfunction and allows more versatility in terms of the range of disease severity that can be monitored. Examples are presented that show how analysing the DA curve into its constituent components can improve our understanding of the morphological changes in early AMD.

Optimising assessment of dark adaptation data using time to event analysis

In age-related macular degeneration (AMD) research, dark adaptation has been found to be a promising functional measurement. In more severe cases of AMD, dark adaptation cannot always be recorded within a maximum allowed time for the test (~ 20–30 min). These data are recorded either as censored data-points (data capped at the maximum test time) or as an estimated recovery time based on the trend observed from the data recorded within the maximum recording time. Therefore, dark adaptation data can have unusual attributes that may not be handled by standard statistical techniques. Here we show time-to-event analysis is a more powerful method for analysis of rod-intercept time data in measuring dark adaptation. For example, at 80% power (at α = 0.05) sample sizes were estimated to be 20 and 61 with uncapped (uncensored) and capped (censored) data using a standard t-test; these values improved to 12 and 38 when using the proposed time-to-event analysis. Our method can accommodate both skewed data and censored data points and offers the advantage of significantly reducing sample sizes when planning studies where this functional test is an outcome measure. The latter is important because designing trials and studies more efficiently equates to newer treatments likely being examined more efficiently.