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.
Primary Angle Closure Glaucoma occurs more frequently in people with a narrower limbal anterior chamber depth (LACD) condition. Nowadays, clinical gold standard as an assessment technique, i.e. gonioscopy, is invasive and complex, whereas Van Herick (VH) technique is non-invasive, but subjective. The instrument, we propose, can automatically performs the VH procedure using a blue laser line, a piezo-actuator, and an image recognition algorithm embedded on a Raspberry Pi board. Preliminary measurements have been carried out on volunteers, and the results proved the feasibility of our approach. The final instrument unveils a high potential for early-stage diagnosis and screening applications.
Pigment dispersion syndrome and pigmentary glaucoma are
investigated by a scanning instrument based on dynamic light
scattering technique. The measurements are oriented to evaluate
the various conjectures about the pathogenesis of pigmentary
glaucoma and to establish a diagnostic tool that may be used for
an early detection of this type of glaucoma.
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