The three classes of photoreceptors cones, rods, and intrinsically photosensitive retinal ganglion cells (ipRGCs) contribute to the pupillary light reflex (PLR). The Silent Substitution technique has been proposed to analyze the contribution of individual photoreceptor to PLR. Through the usage of properly selected pairs of light stimuli, this approach allows stimulating a single class of photoreceptors while keeping the activation of all the others constant. In this way, it is possible to understand the single photoreceptors class effect on both image-forming and non-image-forming functions of the human eyes. In this work, a simple approach to perform the Silent Substitution technique is presented and tested. The instrumentation has been designed with four primaries RGBY fiber-coupled LEDs and a double lens system to achieve a Maxwellian-View like optical system. Preliminary tests were conducted on three volunteers in which the PLRs induced by melanopic and chloropic stimulation were measured. The preliminary results confirm the expectations, the light-adapted pupil diameter is principally regulated by the activation level of the melanopsin-expressing ipRGCs photoreceptors, while conedriven induced pupil responses to peak in color contrast are transitory, reverting to the light-adapted baseline pupil diameter more rapidly in respect to melanopsin counterpart.
Van Herick technique is a qualitative tool for assessing the anterior chamber angle and can be exploited as a simple screening alternative to gonioscopy. In our previous papers, we presented a novel instrument able to automatically perform the Van Herick manoeuvre. Therefore, to fully automate the screening method from the acquired images, it is still necessary to automatically determine the Van Herick grade. In this paper, we present a deep learning algorithm for automatically determining the Van Herick grade. In particular, the performances of three different Convolutional Neural Networks have been verified by acquiring the eye images of 80 patients. All the networks return the Van Herick grade classification with sufficient accuracy for a screening system and, after proper training, can offer a real-time response.
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