A Model for Predicting 2-Year Risk of Incident Late Age-related Macular Degeneration

NIH RePORTER · NIH · R44 · $45,000 · view on reporter.nih.gov ↗

Abstract

PROJECT SUMMARY Age-related macular degeneration (AMD), in the dry or wet form, is the leading cause of vision loss in the developed countries. The Age-Related Eye Disease Study (AREDS) showed that specific antioxidant vitamin supplementation reduces the risk of progression from intermediate stages to late AMD and maintains visual acuity in approximately 25% of patients. While treatment of wet AMD with Intraocular injections can be effective in maintaining vision, such treatments are costly and may be associated with significant cardiovascular risks, or even progression of dry AMD. Hence, it is critical to identify patients at the earlier stages. Unfortunately, there is no effective, automated screening tool to accomplish this, and the patients themselves may be asymptomatic. The goal of this SBIR Direct-to-Phase II proposal is to provide such tool. We have demonstrated the feasibility of AMD screening software ‘iPredictTM’ by successfully identifying 98.1% of individuals with early or intermediate stage AMD. iPredictTM also successfully predicted which individuals would develop late AMD within one year with 87.8% accuracy and two years with 88.4% accuracy. iPredictTM has prototype components for image analysis and machine learning. We also developed a HIPAA compliant telemedicine platform which will enable iPredictTM to perform large-scale screening from remote and rural areas. In order to bring the product to market, these components need to be integrated and tested which is the aim of our proposed Direct-to-Phase II proposal. We aim to develop the finished product which will be ready for the market. We also aim to evaluate the efficacy of iPredictTM in a clinical setup. The AMD preventative market is estimated around $5.4 billion in the U.S. alone. iPredictTM will capture the major market share with its best accuracy and be the first prediction tool for AMD. We aim to commercialize iPredictTM for the screening and prevention of AMD, saving millions of citizens from blindness and reduced quality of life. With iPredictTM’s improvements in speed of delivery, cost of care, and ease of access, the product will be a significant addition to the healthcare system. The iPredictTM’s telemedicine platform will allow large-scale screening from remote/rural areas, primary care clinics, optometry offices and ophthalmology clinics.

Key facts

NIH application ID
10320271
Project number
3R44EY031202-01A1S1
Recipient
IHEALTHSCREEN, INC.
Principal Investigator
Alauddin Bhuiyan
Activity code
R44
Funding institute
NIH
Fiscal year
2021
Award amount
$45,000
Award type
3
Project period
2021-05-01 → 2023-05-31