Multi-line Adaptive Perimetry (MAP): A New Procedure for Quantifying Visual Field Integrity for Rapid Assessment of Macular Diseases.

PURPOSE: In order to monitor visual defects associated with macular degeneration (MD), we present a new psychophysical assessment called multiline adaptive perimetry (MAP) that measures visual field integrity by simultaneously estimating regions associated with perceptual distortions (metamorphopsia) and visual sensitivity loss (scotoma). METHODS: We first ran simulations of MAP with a computerized model of a human observer to determine optimal test design characteristics. In experiment 1, predictions of the model were assessed by simulating metamorphopsia with an eye-tracking device with 20 healthy vision participants. In experiment 2, eight patients (16 eyes) with macular disease completed two MAP assessments separated by about 12 weeks, while a subset (10 eyes) also completed repeated Macular Integrity Assessment (MAIA) microperimetry and Amsler grid exams. RESULTS: Results revealed strong repeatability of MAP and high accuracy, sensitivity, and specificity (0.89, 0.81, and 0.90, respectively) in classifying patient eyes with severe visual impairment. We also found a significant relationship in terms of the spatial patterns of performance across visual field loci derived from MAP and MAIA microperimetry. However, there was a lack of correspondence between MAP and subjective Amsler grid reports in isolating perceptually distorted regions. CONCLUSIONS: These results highlight the validity and efficacy of MAP in producing quantitative maps of visual field disturbances, including simultaneous mapping of metamorphopsia and sensitivity impairment. TRANSLATIONAL RELEVANCE: Future work will be needed to assess applicability of this examination for potential early detection of MD symptoms and/or portable assessment on a home device or computer.

Effect of Varying Levels of Glare on Contrast Sensitivity Measurements of Young Healthy Individuals Under Photopic and Mesopic Vision.

Contrast sensitivity (CS), the ability to detect small spatial changes of luminance, is a fundamental aspect of vision. However, while visual acuity is commonly measured in eye clinics, CS is often not assessed. At issue is that tests of CS are not highly standardized in the field and that, in many cases, optotypes used are not sensitive enough to measure graduations of performance and visual abilities within the normal range. Here, in order to develop more sensitive measures of CS, we examined how CS is affected by different combinations of glare and ambient lighting in young healthy participants. We found that low levels of glare have a relatively small impact on vision under both photopic and mesopic conditions, while higher levels had significantly greater consequences on CS under mesopic conditions. Importantly, we found that the amount of glare induced by a standard built-in system (69 lux) was insufficient to induce CS reduction, but increasing to 125 lux with a custom system did cause a significant reduction and shift of CS in healthy individuals. This research provides important data that can help guide the use of CS measures that yield more sensitivity to characterize visual processing abilities in a variety of populations with ecological validity for non-ideal viewing conditions such as night time driving.

Corneal topography matching by iterative registration.

Videokeratography is used for the measurement of corneal topography in overlapping portions (or maps) which must later be joined together to form the overall topography of the cornea. The separate portions are measured from different viewpoints and therefore must be brought together by registration of measurement points in the regions of overlap. The central map is generally the most accurate, but all maps are measured with uncertainty that increases towards the periphery. It becomes the reference (or static) map, and the peripheral (or dynamic) maps must then be transformed by rotation and translation so that the overlapping portions are matched. The process known as registration, of determining the necessary transformation, is a well-understood procedure in image analysis and has been applied in several areas of science and engineering. In this article, direct search optimisation using the Nelder-Mead algorithm and several variants of the iterative closest/corresponding point routine are explained and applied to simulated and real clinical data. The measurement points on the static and dynamic maps are generally different so that it becomes necessary to interpolate, which is done using a truncated series of Zernike polynomials. The point-to-plane iterative closest/corresponding point variant has the advantage of releasing certain optimisation constraints that lead to persistent registration and alignment errors when other approaches are used. The point-to-plane iterative closest/corresponding point routine is found to be robust to measurement noise, insensitive to starting values of the transformation parameters and produces high-quality results when using real clinical data.

Novel fractal feature-based multiclass glaucoma detection and progression prediction.

We investigate the use of fractal analysis (FA) as the basis of a system for multiclass prediction of the progression of glaucoma. FA is applied to pseudo two-dimensional images converted from one-dimensional retinal nerve fiber layer (RNFL) data obtained from the eyes of normal subjects, and from subjects with progressive and non-progressive glaucoma. FA features are obtained using a box-counting method and a multi-fractional Brownian motion method that incorporates texture and multiresolution analyses. Both features are used for Gaussian kernel-based multiclass classification. Sensitivity, specificity, and area under receiver operating characteristic curve (AUROC) are computed for the FA features and for metrics obtained using wavelet-Fourier analysis (WFA) and fast-Fourier analysis (FFA). The AUROCs that predict progressors from non-progressors based on classifiers trained using a dataset comprised of non-progressors and ocular normal subjects are 0.70, 0.71 and 0.82 for WFA, FFA, and FA, respectively. The correct multiclass classification rates among progressors, non-progressors, and ocular normal subjects are 0.82, 0.86 and 0.88 for WFA, FFA, and FA, respectively. Simultaneous multiclass classification among progressors, non-progressors, and ocular normal subjects has not been previously described. The novel FA-based features achieve better performance with fewer features and less computational complexity than WFA and FFA.