A NEW research project to train artificial technology to detect signs of eye disease is expected to increase early detection and prevention of eye diseases.
Moorfields Eye Hospital NHS Trust is teaming up with Google’s Artificial Intelligence division DeepMind to investigate how machine learning technology can help to better analyse digital scans of the eye, giving eye care professionals a better and faster understanding of eye disease.
The research project will see one million anonymous digital eye scans from Moorfields Eye Hospital – that can’t be traced to a specific person – analysed for subtle early warning signs of diseases such as macular degeneration and diabetic retinopathy that physicians might miss during their diagnosis.
Professor Sir Peng Tee Khaw, Director of the National Institute for Health Research Biomedical Research Centre in Ophthalmology at Moorfields Eye Hospital, said with sight loss predicted to double by the year 2050 it was vital the use of cutting edge technology to prevent eye disease was explored.
“Our research with DeepMind has the potential to revolutionise the way professionals carry out eye tests and could lead to earlier detection and treatment of common eye diseases such as age-related macular degeneration,” he said.
RNIB Eye Health Campaign Manager Clara Eaglan said AI technology that could check retinal scans and detect eye disease at a much earlier stage could play a big role in tackling avoidable sight loss.
“In many cases once sight is lost it cannot be restored, so earlier detection that leads to rapid treatment will be hugely beneficial,” Clara said.
Macular Society Chief Executive Cathy Yelf said it was an exciting development towards early detection of eye disease and finding a cure for conditions including age-related macular degeneration (AMD).
“AMD is a devastating condition and delays due to pressure on eye clinics have resulted in some people suffering unnecessary sight loss,” Cathy said.
“This technology could ease that pressure if it can accurately diagnose conditions such as wet AMD resulting in urgent referrals for only those that need them.”