In this small business innovations research (SBIR) project we present EyeArt, a retinal image analysis tool for automated diabetic retinopathy (DR) screenings with high diag- nostic efficacy. With its interface to EyePACS, a license-free, scalable telemedicine plat- form, EyeArt will aid the expansion of DR screening and help bridge the exponentially growing disparity between the number of diabetic patients and the number of eye-care providers. Research suggests that the Latino population in general are genetically predisposed to develop diabetes. Their vulnerability to vision loss due to diabetic retinopathy is further compounded by factors such as lack of access to ophthalmology clinicians, lack of in- surance, and lack of education. According to the Department of Health Services (DHS) in Los Angeles County (LAC) the situation for diabetics is particularly grim, with current wait times upwards of 6-9 months for retinal examinations for retinopathy screening. This can lead to treatment delays and progression towards irreversible vision loss. To help reduce risk of vision loss in this diabetic population, we propose to use advanced image analysis algorithms in conjunction with existing telemedicine initiatives to enable faster screening, allow reprioritization of ophthalmologist appointments, and aid in triage of high-risk patients. Our phase I prototype automatic DR screening tool has already shown great potential by beating current academic and commercial DR screening ap- proaches on large public retinal datasets. Going forward, we will build on our approach and further develop innovative, customized algorithms for critical low-level image pro- cessing steps, while leveraging on recent advances in computer vision, and machine learning areas for high-level, inference steps to produce a clinical grade DR screening tool. Our lesion localization and screening engine will be functionally integrated with EyePACS to further drive the expansion of screening, particularly benefiting under- resourced screening programs like the LAC-DHS safety net and its large Hispanic dia- betic population.

Public Health Relevance

EyeArt - an automated retinal image analysis tool will help in triaging patients in need of expert care and thus reduce the cost of diabetic retinopathy (DR) screening, while leading to an expansion of screening in primary care centers through its easily accessible telemedicine interface. This increased access to DR care will help prevent vision loss due to diabetes complications in vulnerable disparity populations such as Latinos who do not get screened due to socio-economic factors. To make an immediate impact we are collaborating with Los Angeles County Department of Health Services (LAC-DHS) to deploy our system, following clinical validation, in their under-resourced safety net teleretinal screening setup whic caters to large disparity populations of LA County.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
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Special Emphasis Panel (ZEB1)
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Pai, Vinay Manjunath
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Eyenuk, Inc.
Woodland Hills
United States
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Bhaskaranand, Malavika; Ramachandra, Chaithanya; Bhat, Sandeep et al. (2016) Automated Diabetic Retinopathy Screening and Monitoring Using Retinal Fundus Image Analysis. J Diabetes Sci Technol 10:254-61