SBIR-STTR Award

Non-Invasive Through-The-Eyelid Tonometer for Frequent Eye Pressure Measurements
Award last edited on: 9/15/2017

Sponsored Program
STTR
Awarding Agency
NSF
Total Award Amount
$225,000
Award Phase
1
Solicitation Topic Code
SH
Principal Investigator
Peter P Polyvas

Company Information

EPV Sensors LLC (AKA: EPVSENSORS LLC)

4949 E Alta Vista Street
Tucson, AZ 85721
   (520) 241-5759
   info@epvsensors.com
   www.epvsensors.com

Research Institution

University of Alabama

Phase I

Contract Number: 1521481
Start Date: 7/1/2015    Completed: 6/30/2016
Phase I year
2015
Phase I Amount
$225,000
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project will be in the ability to better screen for and manage glaucoma. Americans over the age of 40 need to be screened for elevated eye pressure which may lead to glaucoma and blindness. This STTR project aims to develop a non-invasive low-cost instrument that will allow frequent eye pressure measurements through the eye lid. Approximately 4 million Americans suffer from elevated eye pressure and require regular monitoring, the technology developed under this grant will improve public health, reduce cost of eye pressure measurements and will lead to an annual market opportunity in excess of 1B US dollars. Given the universal need for convenient eye pressure measurement systems a similar market opportunity exists in Europe and the developed countries of Asia and Latin America. The proposed project will develop a patient-specific calibration method. Through-the-eye lid tactile tonometry is an indirect pressure measurement technique. While universal calibration may still produce relative pressure measurements, a patient specific calibration will improve the accuracy to a level comparable with existing instruments such as Goldman applanation tonometers. The proposed research will examine the ability of a single measurement to produce an accurate patient-specific calibration. The proposed approach utilizes an artificial neural network trained a-priory by a finite element model of the human eye capturing easy-to-measure model parameters. It is anticipated that the accuracy of the technique can reach +/- 5 mmHg or better with respect to Goldman applanation tonometry.

Phase II

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Start Date: 00/00/00    Completed: 00/00/00
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