Remote and low-cost intraocular pressure monitoring by deep learning of speckle patterns

Abstract

Intraocular pressure (IOP) measurements comprise an essential tool in modern medicine for the early diagnosis of glaucoma, the second leading cause of human blindness. The world's highest prevalence of glaucoma is in low-income countries. Current diagnostic methods require experience in running expensive equipment as well as the use of anesthetic eye drops. We present herein a remote photonic IOP biomonitoring method based on deep learning of secondary speckle patterns, captured by a fast camera, that are reflected from eye sclera stimulated by an external sound wave. By combining speckle pattern analysis with deep learning, high precision measurements are possible. The method was tested under artificially varying eye pressures on a series of 24 pig eyeballs, found to be similar to human eyes. As a low-cost procedure, it has the potential to meet clinical needs in low- and middle-income countries and at points of care everywhere.

Neurophotonics: a comprehensive review, current challenges and future trends

“…This approach involves the analysis of spatiotemporal back-scattered light to sense sound waves of brain blood vessels (Ozana et al, 2020). It has been used in the development of various biomedical applications, such as heart rate monitoring (Zalevsky et al, 2009), blood coagulation, pressure and oximetry measurements (Ozana et al, 2015;Golberg et al, 2018;Kalyuzhner et al, 2021) and melanoma detection (Ozana et al, 2016). A novel approach to remotely monitor human brain activation is presented by utilizing the detection of task-related hemodynamic changes (Ozana et al, 2020).…”