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Diabetic Retinopathy

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A deep learning system for detecting diabetic retinopathy across the disease spectrum.

Nature communications
Retinal screening contributes to early detection of diabetic retinopathy and timely treatment. To facilitate the screening process, we develop a deep learning system, named DeepDR, that can detect early-to-late stages of diabetic retinopathy. DeepDR ...

Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning.

Sensors (Basel, Switzerland)
Diabetic retinopathy (DR) is a disease resulting from diabetes complications, causing non-reversible damage to retina blood vessels. DR is a leading cause of blindness if not detected early. The currently available DR treatments are limited to stoppi...

Understanding inherent image features in CNN-based assessment of diabetic retinopathy.

Scientific reports
Diabetic retinopathy (DR) is a leading cause of blindness and affects millions of people throughout the world. Early detection and timely checkups are key to reduce the risk of blindness. Automated grading of DR is a cost-effective way to ensure earl...

Artificial Intelligence in Hypertension: Seeing Through a Glass Darkly.

Circulation research
Hypertension remains the largest modifiable cause of mortality worldwide despite the availability of effective medications and sustained research efforts over the past 100 years. Hypertension requires transformative solutions that can help reduce the...

Subfoveal choroidal thickness changes after intravitreal ranibizumab injections in different patterns of diabetic macular edema using a deep learning-based auto-segmentation.

International ophthalmology
PURPOSE: To evaluate the effect of intravitreal injection of ranibizumab (IVR) on subfoveal choroidal thickness (SFCT) and its relationship with central macular thickness (CMT) and best-corrected visual acuity (BCVA) changes in eyes with center-invol...

Development and evaluation of a deep learning model for the detection of multiple fundus diseases based on colour fundus photography.

The British journal of ophthalmology
AIM: To explore and evaluate an appropriate deep learning system (DLS) for the detection of 12 major fundus diseases using colour fundus photography.

Deep learning-based detection and stage grading for optimising diagnosis of diabetic retinopathy.

Diabetes/metabolism research and reviews
AIMS: To establish an automated method for identifying referable diabetic retinopathy (DR), defined as moderate nonproliferative DR and above, using deep learning-based lesion detection and stage grading.

Deep Learning-Based Diabetic Retinopathy Severity Grading System Employing Quadrant Ensemble Model.

Journal of digital imaging
The diabetic retinopathy accounts in the deterioration of retinal blood vessels leading to a serious compilation affecting the eyes. The automated DR diagnosis frameworks are critically important for the early identification and detection of these ey...

Artificial Intelligence in the assessment of diabetic retinopathy from fundus photographs.

Seminars in ophthalmology
: Over the next 25 years, the global prevalence of diabetes is expected to grow to affect 700 million individuals. Consequently, an unprecedented number of patients will be at risk for vision loss from diabetic eye disease. This demand will almost ce...