Develop predictive models for diabetic retinopathy using risk factors collected from patient clinical records.
Develop predictive models for automated diabetic retinopathy assessment using a combination of patient risk factor data and data from digital retinal images previously evaluated by experts.
Evaluate the predictive accuracy of: a) the models developed for specific-aim 2, and, b) the assessments of optometrist readers against standard of care dilated retinal examinations by board certified ophthalmologists for 300 diabetic patients utilizing a new Los Angeles County reading center.
Create web-based software tools based on the predictive models developed in specific-aim 1 that can be used to initiate outreach to high-risk patients in under-resourced settings, boosting detection rates for those patients who are most at risk for diabetic retinopathy.
Establish targeted outreach methods to promote screening for patients that the predictive models from specific-aim 1 identified as potentially having undetected diabetic retinopathy.