The evaluation of retinal images using an algorithm based on deep machine learning can improve early detection and treatment of diabetic retinopathy, claims research.

The advent of deep learning in ophthalmology has revolutionised the detection and diagnosis of diabetic retinopathy (DR). By utilising convolutional neural networks (CNNs) and advanced image ...

MELBOURNE, Australia, April 20, 2017 /PRNewswire/ -- IBM (NYSE: IBM) this week released the results of new research using deep learning and visual analytics technology to advance early detection ...

Automated machine-learning models accurately identified risk for diabetic retinopathy progression using ultra-widefield retinal images, according to a study published in JAMA Ophthalmology.&ldquo ...

High numbers of false positives and poor image quality were found when an artificial intelligence system for diabetic retinopathy detection was tested in a primary care setting.

Diabetes affects more than 101 million people in India today. The number is estimated to reach 125 million by the year 2045. There are several systemic health risks associated with diabetes, such ...

For the past three years, Google and Verily—Alphabet’s life sciences and healthcare arm—have been researching the use of machine learning to screen for diabetic retinopathy, a leading cause ...

Early detection of diabetic retinopathy is vital for successful treatment and improved patient outcomes. ... By training AI on a machine learning algorithm, the images can be assessed for risk.

It makes use of the same machine-learning technique that Google uses to label millions of Web images. Diabetic retinopathy is caused by damage to blood vessels in the eye and results in a gradual ...