Development and Validation of an Automated Diabetic Retinopathy Screening Tool for Primary Care Setting.

We report the construction of a fully automated artificial intelligence deep learning (DL) software on a secure Health Insurance Portability and Accountability Act–compliant cloud-based platform for effective and efficient screening of referable and nonreferable diabetic retinopathy (DR) in the primary care setting. DR is one of the leading causes of blindness in the U.S. and other developed countries. Early detection is the key for prevention. Currently, screening for DR is done by ophthalmologists or optometrists with limited catchment areas and also requires time-consuming referral. Many patients (some studies suggest almost one out of three) do not get these exams (1). The critical goal is the screening of “referable” DR for referral to an ophthalmologist. A 5-point scale (2) (no DR and mild, moderate, severe, and proliferative DR) may be used for grading DR based on the presence and extent of microaneurysms, exudates, hemorrhages, and other abnormalities. By definition, no DR and mild DR are considered nonreferable, and the other categories are considered referable. Previous automated DR screening techniques are of varying accuracy and performance, but recently DL approaches have had greater success. DL refers to a class of machine learning techniques that learns features directly from images without feature labels and usually requires very large training data sets …