Sankara Eye Foundation and Singapore-based Leben Care are deploying retina risk assessment software-as-a-service platform in India. Netra.AI, the cloud-based artificial intelligence (AI) solution, is powered by Intel technology and is claimed to be using deep learning to identify retinal conditions in a short span of time with the accuracy level of human doctors. The solution can help identify diabetic retinopathy (DR), reducing the screening burden on vitreoretinal surgeons.

“The use of AI to improve disease detection and prevention is a critical step for the healthcare industry and a giant leap for humankind. India has one of the largest diabetic populations in the world and diabetic retinopathy is the major cause for vision loss and blindness in persons of working age. With Netra.AI, Sankara Eye Foundation and Leben Care have leveraged the power of Intel Xeon Scalable processors and built-in Intel Deep Learning (DL) Boost to accurately detect DR and enable timely treatment to effectively combat avoidable vision impairment and blindness in diabetic patients,” said Prakash Mallya, vice president and managing director of sales, marketing and communications group, Intel India.

India has one of the largest diabetic populations of any country in the world, approaching 98 million cases by 2030. Research shows that DR is a leading cause of blindness and vision loss in adults, and early detection and treatment is critical to stopping the damage. However, the lack of trained retinal specialists in India — especially in remote, rural regions — limits effective screening of asymptomatic patients. This results in patients presenting late with advanced diabetic eye disease.

Netra.AI analyzes images from portable, technician-operated fundus camera devices, for immediate results of referable DR grading via a cloud-based web portal. The solution uses AI algorithms, developed in collaboration with retina experts, with a four-step deep convolutional neural network (DCNN). This neural network helps in detecting DR stage and annotating lesions based on pixel density in the fundus images. The solution can be expanded to other retinal conditions and glaucoma, helping to reduce the screening burden on healthcare specialists and focus key resources on patients who need immediate care and intervention.

This content was originally published here.