AI system for better eye screening
Diabetic retinopathy — an eye condition affecting one in three diabetic patients — is currently diagnosed using manual assessments of retinal photographs involving large teams of trained professionals.
This process can now be made more efficient with a novel artificial intelligence (AI) screening technology capable of identifying retinal images showing signs of diabetic retinopathy with high accuracy. This technology is the result of a collaboration between researchers from NUS Computing, the Singapore National Eye Centre (SNEC) and the Singapore Eye Research Institute (SERI).
The screening technology employs a deep learning system that uses representation-learning methods to process large amount of data, and recognise intricate structures and meaningful patterns that may not be visible to the human eye. The researchers developed and trained the system to recognise and classify retinal images, and tested its performance against close to 500,000 images from multi-ethnic populations across the US, Australia, China, Hong Kong, Mexico and Singapore. This is by far the world’s largest dataset for evaluating the use of a deep learning system to screen for an eye condition.
The same technology can also be employed to diagnose two other eye conditions – glaucoma suspect and age-related macular degeneration.
“Our technology has sensitivity greater than 90 per cent and specificity greater than 85 per cent for the three eye conditions,” said NUS Computing Professor Lee Mong Li Janice, who is part of the cross-institutional research team.
With the growing prevalence of diabetes in Singapore, the researchers believe that their screening technology will provide a more efficient and sustainable way of screening patients for diabetic retinopathy.
Professor Wong Tien Yin, Medical Director of SNEC and Chairman of SERI, said that the technology will help increase efficiency and reduce cost. In countries with existing screening programmes, such as Singapore and the UK, it could replace a large proportion of the manual assessment required. “In communities and countries without existing programmes and without sufficient ophthalmologists — such as developing countries, parts of China, India, South America — it can be used as a first line screening tool to accurately screen for cases that require referral to an ophthalmologist for treatment,” added Prof Wong, who is also Vice Dean (Clinical Sciences) at Duke-NUS Medical School.
The research team is now beta testing their AI screening technology in the Singapore Diabetic Retinopathy Screening Programme. They aim to increase their datasets to five million images from around the world over the next five years.
“We are also developing more complex algorithms for different severity levels of diabetic retinopathy, predictive algorithms for the incidence and progression of the eye condition and diabetes-related systematic complications such as stroke, coronary diseases and chronic kidney diseases,” added NUS Computing Professor Wynne Hsu.
