AI Article Synopsis
- Diabetic retinopathy affects millions of visually impaired and blind people worldwide, with a projected increase from 2.6 million in 2015 to 3.2 million in 2020, particularly impacting low and middle-income countries where early detection is vital.
- Recent advancements in deep learning technologies have enabled automated screening methods that improve efficiency, although most still rely on conventional fundus photography instead of ultra-wide-field imaging, which captures more of the retinal surface.
- This study introduces a detection system using ultra-wide-field fundus photography and deep learning, demonstrating that it significantly outperforms traditional methods in accurately detecting diabetic retinopathy.
Article Abstract
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.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7820327 | PMC |
| http://dx.doi.org/10.1038/s41598-021-81539-3 | DOI Listing |
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