To build an automated detection system with realistic clinical potential, color fundus photography data can be used as input data. Using image classification, pattern recognition, and machine learning algorithms, several predictive models can be developed to identify signs of DR.
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Clinicians can identify DR by the presence of lesions associated with the vascular abnormalities caused by the disease. While this approach is effective, its resource demands are high. The expertise and equipment required are often lacking in areas where the rate of diabetes in local populations is high and DR detection is most needed. As the number of individuals with diabetes continues to grow, the infrastructure needed to prevent blindness due to DR will become even more insufficient.