Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images

Visually impaired and blind people due to diabetic retinopathy were 2.6 million in 2015 and estimated to be 3.2 million in 2020 globally. Though the incidence of diabetic retinopathy is expected to decrease for high-income countries, detection and treatment of it in the early stages are crucial for low-income and middle-income countries. Due to the recent advancement of deep learning technologies, researchers showed that automated screening and grading of diabetic retinopathy are efficient in saving time and workforce. However, most automatic systems utilize conventional fundus photography, despite ultra-wide-field fundus photography provides up to 82% of the retinal surface. In this study, we present a diabetic retinopathy detection system based on ultra-wide-field fundus photography and deep learning. In experiments, we show that the use of early treatment diabetic retinopathy study 7-standard field image extracted from ultra-wide-field fundus photography outperforms that of the optic disc and macula centered image in a statistical sense.

Automated summary

The prevalence of visual impairment and blindness caused by diabetic retinopathy is expected to increase globally. Early detection and treatment are crucial, especially in low-income and middle-income countries. Recent advancements in deep learning technologies have shown promising results in automated screening and grading of diabetic retinopathy using ultra-wide-field fundus photography.