Abstract
BackgroundReliable detection of central fixation and eye alignment is essential in the diagnosis of amblyopia (“lazy eye”), which can lead to blindness. Our lab has developed and reported earlier a pediatric vision screener that performs scanning of the retina around the fovea and analyzes changes in the polarization state of light as the scan progresses. Depending on the direction of gaze and the instrument design, the screener produces several signal frequencies that can be utilized in the detection of central fixation. The objective of this study was to compare artificial neural networks with classical statistical methods, with respect to their ability to detect central fixation reliably.MethodsA classical feedforward, pattern recognition, two-layer neural network architecture was used, consisting of one hidden layer and one output layer. The network has four inputs, representing normalized spectral powers at four signal frequencies generated during retinal birefringence scanning. The hidden layer contains four neurons. The output suggests presence or absence of central fixation. Backpropagation was used to train the network, using the gradient descent algorithm and the cross-entropy error as the performance function. The network was trained, validated and tested on a set of controlled calibration data obtained from 600 measurements from ten eyes in a previous study, and was additionally tested on a clinical set of 78 eyes, independently diagnosed by an ophthalmologist.ResultsIn the first part of this study, a neural network was designed around the calibration set. With a proper architecture and training, the network provided performance that was comparable to classical statistical methods, allowing perfect separation between the central and paracentral fixation data, with both the sensitivity and the specificity of the instrument being 100%. In the second part of the study, the neural network was applied to the clinical data. It allowed reliable separation between normal subjects and affected subjects, its accuracy again matching that of the statistical methods.ConclusionWith a proper choice of a neural network architecture and a good, uncontaminated training data set, the artificial neural network can be an efficient classification tool for detecting central fixation based on retinal birefringence scanning.
Highlights
Reliable detection of central fixation and eye alignment is essential in the diagnosis of amblyopia (“lazy eye”), which can lead to blindness
Using a computer model involving all polarization-changing components of the system, including the Henle fibers and the cornea, we found that by spinning the half wave plate (HWP) 9/16-ths as fast as the circular scan, strong signals are generated that are odd multiples of half of the scanning frequency [17]
This study confirmed that spectral powers at several signal frequencies obtained with retinal birefringence scanning around the human fovea can be used to detect central fixation reliably
Summary
Reliable detection of central fixation and eye alignment is essential in the diagnosis of amblyopia (“lazy eye”), which can lead to blindness. Our laboratory has been developing novel technologies for detecting accurate eye alignment directly, by exploiting the birefringence (a property that changes the polarization state of light) of the uniquely arranged nerve fibers (Henle fibers) surrounding the fovea. We employed retinal birefringence scanning (RBS), a technique that uses the changes in the polarization of light returning from the eye, to detect the projection into space of the array of Henle fibers surrounding the fovea [3,4,5]. An early version of the “pediatric vision screener” (PVS) was designed in our lab and tested at the Boston Children’s Hospital, [6,7,8,9,10] This prototype device has been developed into a commercial instrument that detects eye alignment (REBIScan, Boston, MA, USA)
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