SBIR-STTR Award

This Small Business Innovation Research Phase I project comprises the development of a wearable system capable of accurately tracking glaucoma progression via continuous monitoring of intraocular pressure (IOP). Glaucoma is the leading cause of irreversible blindness in the world and affects nearly 80 million people worldwide. Using a novel wearable system, optometrists, ophthalmologists, and medical professionals across multiple disciplines can review the IOP trends at visits, monitor high-risk patients in real-time, and program patient-specific threshold alarms that will prevent irreversible blindness caused by glaucoma and related conditions. Through successful product commercialization, the novel wearable blindness prevention system (BPS) is expected to be adopted by approximately 2 million glaucoma patients annually. Users of the device will have continuous access to IOP data trends. Clinicians will be alerted when at-risk patients fail to comply with prescribed daily medicinal treatment (due to excessive IOP levels) and can continuously track the efficacy of prescribed IOP-lowering treatments. Finally, the IOP tracking capability of the system will allow improvements in the quality of care for general clinicians, medical specialists, and clinical researchers and will be a major player in the current move toward data-driven, value care. The intellectual merit of this project centers on laboratory feasibility testing of the fundamental concept behind the novel blindness prevention system technology - image-based pressure sensing - on a benchtop 3D-printed glaucoma simulator that is monitored using an alpha prototype of the system. The research plan includes testing of the feasibility of a complementary metal oxide semiconductor (CMOS) camera which fits inside a pair of eyeglass frames, plus processor, optics, and software, as a way to record pressure changes in eye tissue in a benchtop environment. The key objective is to accurately - and with high repeatability - detect unhealthy eye pressure levels in the simulator using an alpha prototype, a-BPS, that communicates with a standard mobile device. The target research outcome for the a-BPS is to successfully trigger a high-pressure alarm in a repeatable manner whenever eye pressure in the simulator exceeds a clinically-relevant threshold. It is anticipated that the Phase I research results will show successfully operation in 90% or more of the test samples. The output from the Phase I effort will be used as design inputs for a Phase II prototype, which will be wearable and will allow testing on humans. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The broader/commercial impact of this SBIR Phase II project aims to further advance novel wearable technology for glaucoma patients and prepare the technology for broad adoption. Glaucoma, the leading cause of irreversible blindness, has an unknown cause and affects more than 70 million people worldwide. Currently, there is no cure for glaucoma, but early can often save one?s vision. Eye pressure is the most commonly used measure for predicting and monitoring glaucoma. The wearable technology developed under this SBIR project will monitor eye pressure throughout the day and allow clinicians to provide a higher quality of care for at-risk patients. The technology uses photographs to measure how the eye stretches under high pressure. This project aims to adapt the imaging technology into stylish eyeglass frames and develop custom software for converting photographs to eye pressure measurements, informing providers and improving compliance associated with at-home medication. This project aims to advance a novel technology in which wearable eyeglass frames are used to track intraocular pressure (IOP) by imaging the level of pressure-induced mechanical strain associated with the tissue at the front of the eye - specifically, exposed sclera. IOP is, by far, the most commonly used metric for predicting glaucoma, the leading cause of irreversible blindness. In this project, a custom machine learning algorithm will identify characteristic patterns residing in small regions of the scleral images and, by tracking pressure-induced displacement of the regions, calculate IOP. The key objective of Phase II is to accurately measure IOP in real-world conditions with human in-vivo studies, incorporating necessary electronics in the frames. The technology will be further developed in order to improve correlation of the algorithm with conventional IOP captured during the image collection period.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.