This research will assess the technical feasibility of developing a new and unique miniature ophthalmic intraocular pressure (IOP) sensor, to be retrofitted into the cannula of a conventional Glaucoma Drainage Device (GDD) and surgically implanted under the sclera for the purpose of continuous measurement and transmission of these IOP readings to a remote receiver. Current technology does not allow for real-time, continuous monitoring of IOP (24/7, diurnal and nocturnal readings). The long-term objective of this project is to enable physicians to study, diagnose and more clearly understand the response mechanism relating to IOP that can lead to more effective development and titration of glaucoma medications or other timely therapeutic options. Elevated and fluctuating IOP over a 24/7 period is an independent and proven risk factor for eye damage in the short term and glaucoma progressions in the long term, which if left unchecked or unmanaged could lead to blindness. This Phase II research is dedicated to developing technologies that assist physicians to more effectively treat the glaucoma patient. This is inline with the mission of the National Eye Institute. Under Phase II, specific aims include system microminiaturization, packaging and biocompatibility. Research and development will specifically involve designing, fabricating, and integrating various MEMS and ASIC chips into a unitized singular micro scale chip to perform a number of systems activities (i.e. power transfer [RF-ID], chip interrogation, IOP data capture and storage, wireless data transfer, remediation, resolution, etc.) involving methods researched and determined in the successful portion of Phase I. These sequential methods include design input, product development, product integration (MEMS sensor, ASIC & biocompatible materials) and product validation and testing. In conclusion, with this telemetric IOP sensing technology, physicians will be able to measure the effects that medications and life-style factors have on the IOP of their patients in a real- time and continuous model. ? ? ? ?
