IMPROVING NON-CONTACT TONOMETRY: A DEEP NEURAL NETWORK BASED METHOD FOR CORNEAL DEFORMATION MAPPING

Abstract

Glaucoma, a disease characterized by increased intraocular pressure (IOP), is one of the leading causes of preventable blindness worldwide. Accurate measurement of IOP is essential in monitoring glaucomatous progression in order to deliver treatment and prevent long-term vision loss. Currently, non-contact tonometry, known as an "air-puff test", is a common diagnostic method despite its inaccessibility, discomfort, high cost, and reliance on a trained professional. To improve upon these shortcomings, we designed a cheaper tonometer integrating a novel depth-mapping neural network with a custom air-puff generation system. We deformed porcine corneas with a controlled-intensity air-puff while imaging the deformation with a single stationary camera-- a contrast to the standard Scheimpflug method. From the footage, our neural network predicted a three-dimensional map of corneal deformations. The network was able to predict a general negative trend between the IOP and the corneal deformation extracted. We compared our results to accepted literature deformation values and ground truth footage, allowing us to determine that the deformation amplitudes were physically plausible. With a more robust imaging setup, we present a promising alternative to traditional IOP measurement methods. Future studies should make the simulated footage more representative of clinical conditions to increase the generalizability of the neural network. Additionally, anatomical differences between porcine and human eyes as well as corneal variability due to socio-demographic differences must be addressed for our results to be applied to clinical settings.