David E. Freund

Direct summation of fields for light scattering by fibrils with applications to normal corneas

A statistical analysis based on a direct summation of electric fields scattered by individual fibrils is developed to calculate corneal scattering from the fibril distributions shown in electron micrographs. Unlike previous analyses, the method of direct summation of fields can be used for swollen and scarred corneas as well as normal corneas. The method is tested by applying it to the case of the normal rabbit cornea. The results are in good agreement with those obtained using the radial distribution formulation.

Structure and properties of collagen vitrigel membranes for ocular repair and regeneration applications

The frequency of ocular injuries on the battlefield has been steadily increasing during recent conflicts. Combat-related eye injuries are difficult to treat and solutions requiring donor tissue are not ideal and are often not readily available. Collagen vitrigels have previously been developed for corneal reconstruction, but increased transparency and mechanical strength are desired for improved vision and ease of handling. In this study, by systematically varying vitrification temperature, relative humidity and time, the collagen vitrigel synthesis conditions were optimized to yield the best combination of high transparency and high mechanical strength. Optical, mechanical, and thermal properties were characterized for each set of conditions to evaluate the effects of the vitrification parameters on material properties. Changes in denaturing temperature and collagen fibril morphology were evaluated to correlate properties with structure. Collagen vitrigels with transmittance up to 90%, tensile strength up to 12 MPa, and denaturing temperatures that significantly exceed the eye/body temperature have been synthesized at 40 °C and 40% relative humidity for one week. This optimal set of conditions enabled improvements of 100% in tensile strength and 11% in transmittance, compared to the previously developed collagen vitrigels.

A new bistatic model for electromagnetic scattering from perfectly conducting random surfaces: numerical evaluation and comparison with SPM

Abstract This paper is a companion to our previous contribution deriving a new approximate bistatic model for electromagnetic scattering from perfectly conducting rough surfaces. We evaluate this model numerically and compare it with an ‘exact’ numerical solution of the scattering problem. This comparison shows good agreement between our approximation and numerical solution for a wide range of incident and scattering angles. However, for horizontal-incident horizontal-scattered polarization (HH-pol), the model exhibits strong deviation from the ‘exact’ solution for near-grazing scattering angles. The model shows a similar divergence at HH-pol when compared with the small-perturbation method (SPM). The cause of this divergence is explained. During the SPM comparison, we noticed that the integral equation method model also does not reproduce the SPM limit except for forward and backscatter geometries. We propose in this paper a simple modification of our model to ensure agreement with the bistatic SPM approximation when applicable, and show that the modified model also yields close agreement with numerical computations even when the surface roughness does not satisfy the SPM condition.

Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks

Importance Age-related macular degeneration (AMD) affects millions of people throughout the world. The intermediate stage may go undetected, as it typically is asymptomatic. However, the preferred practice patterns for AMD recommend identifying individuals with this stage of the disease to educate how to monitor for the early detection of the choroidal neovascular stage before substantial vision loss has occurred and to consider dietary supplements that might reduce the risk of the disease progressing from the intermediate to the advanced stage. Identification, though, can be time-intensive and requires expertly trained individuals. Objective To develop methods for automatically detecting AMD from fundus images using a novel application of deep learning methods to the automated assessment of these images and to leverage artificial intelligence advances. Design, Setting, and Participants Deep convolutional neural networks that are explicitly trained for performing automated AMD grading were compared with an alternate deep learning method that used transfer learning and universal features and with a trained clinical grader. Age-related macular degeneration automated detection was applied to a 2-class classification problem in which the task was to distinguish the disease-free/early stages from the referable intermediate/advanced stages. Using several experiments that entailed different data partitioning, the performance of the machine algorithms and human graders in evaluating over 130 000 images that were deidentified with respect to age, sex, and race/ethnicity from 4613 patients against a gold standard included in the National Institutes of Health Age-related Eye Disease Study data set was evaluated. Main Outcomes and Measures Accuracy, receiver operating characteristics and area under the curve, and kappa score. Results The deep convolutional neural network method yielded accuracy (SD) that ranged between 88.4% (0.5%) and 91.6% (0.1%), the area under the receiver operating characteristic curve was between 0.94 and 0.96, and kappa coefficient (SD) between 0.764 (0.010) and 0.829 (0.003), which indicated a substantial agreement with the gold standard Age-related Eye Disease Study data set. Conclusions and Relevance Applying a deep learning–based automated assessment of AMD from fundus images can produce results that are similar to human performance levels. This study demonstrates that automated algorithms could play a role that is independent of expert human graders in the current management of AMD and could address the costs of screening or monitoring, access to health care, and the assessment of novel treatments that address the development or progression of AMD.

Automatic screening of age-related macular degeneration and retinal abnormalities

We describe a novel approach for screening retinal imagery to detect evidence of abnormalities. In this paper, we focus our efforts on age-related macular degeneration (AMD), a pathology that may often go undetected in the early or intermediate stages, and can lead to a neovascular form often resulting in blindness, if untreated. Our strategy for retinal anomaly detection is to employ a single class classifier applied to fundus imagery. We use a multiresolution locally-adaptive scheme that identifies both normal and anomalous regions within the retina. We do this by using a hybrid parametric/non-parametric characterization of the support of the probability distribution of normal retinal tissue in color and intensity feature space. We apply this approach to screen for evidence of AMD on a dataset of 66 healthy and pathological cases and found a detection sensitivity and specificity of 95% and 96%.

Density of sea surface specular points

An analytical expression is derived for the number density of sea surface specular points, λρ. Unlike previous results, the present result is not limited to small wave slopes and is valid for arbitrary source and receiver location as well as for arbitrary sea surface displacement. When the source is not in the horizon, it is shown that one immediate outcome from this generalization is that the standard method for obtaining the total number of specular points over the entire (flat) sea surface is invalid. Numerical results are also given for λρ as a function of wind speed and sun elevation.