Predicting Diabetic Retinopathy from Risk Factor Data and Digital Retinal Images

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Abstract Diabetic retinopathy is the leading cause of blindness among US adults between the ages of 20 and 74 years. Laser photocoagulation surgery has been established as an effective way of treating retinopathy if it is detected early. Yearly retinal screening examinations are a potent tool in the battle to reduce the incidence of blindness from diabetic retinopathy because they provide diabetic patients with timely diagnoses and consequently, the potential for timely treatment. Primary care safety net clinics provide monitoring and other services for diabetic patients but they are often not equipped to provide specialty care services such as retinal screenings. Access to specialists who can provide retinal screenings can be increased through the use of telemedicine, which has shown great promise as a means of screening for diabetic retinopathy in the US and internationally. A pilot study by Charles Drew University investigators had a total of 2,876 teleretinal screenings performed for diabetic retinopathy, with 2,732 unique diabetic patients from six South Los Angeles safety net clinics screened. The present study aims to build on this prior work by: (a) developing novel software that utilizes information from clinical records to detect latent diabetic retinopathy in diabetic patients who have not yet received an annual eye examination, and (b) devising methods to speed up the diabetic retinopathy detection process for diabetic patients who have had digital retinal images taken by partially automating the process using image processing and machine learning techniques. Specifically, we propose to: 1. Develop predictive models for diabetic retinopathy using risk factors collected from patient clinical records. 2. 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. 3. 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. 4. 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. 5. Establish targeted outreach methods to promote screening for patients that the predictive models from specific aim 1 identify as potentially having undetected diabetic retinopathy.