Artificial intelligence application to aid fundus screening
Diabetic retinopathy is the most common complication of diabetes. Its screening with fundus photography uses a lot of resources. University of Oulu is involved in a project that aims to automate retinopathy screening through machine learning.
Following the growth of diabetes, the number of patients suffering from diabetic retinopathy is also increasing. The diagnosis and monitoring of retinopathy is based on fundus photographs and their analysis, which uses a lot of public health resources.
“The number of fundus photographs has increased by 45% over the past five years. Already now, their screening is a huge project, and the work will only increase,” explained Nina Hautala, Professor of Ophthalmology at the University of Oulu. “It takes a few minutes for one person to analyse one image, but, for instance, in the area of Oulu University Hospital (OYS), over 200 photographs are taken for screening on a weekly basis.”
Automatising the analysis of retinopathy photographs would make the screening more effective, save time and labour, and speed up access to treatment. A collaborative project of the University of Oulu and Ilmenau University of Technology in Germany has taken on the challenge, and is currently developing an application based on machine learning, which will detect changes in the fundus.
Wide range of changes in the fundus challenges machine learning
In practice, the application looks for the same things in the fundus photographs as an ophthalmologist: visible signs of disease. The application has to be taught how to recognise such signs by using photographic material; one of the reasons for the German researchers to contact the University of Oulu was the screening system of Finnish diabetes patients, which has collected fundus photographs systematically throughout the patients’ medical history.
The developed methods are to be licensed for companies
Another ongoing project, called Crystal, aims to extend the automatisation to other eye diseases as well.
“The aim is to create models for an application that can be used to screen retinopathy but also open-angle glaucoma, and age-related macular degeneration,” said Katri Kukkola, Researcher from the Optoelectronics and Measurement Techniques unit at the University of Oulu.
Machine learning is a hot topic in eye diseases. The annual publication rate has risen in the 2010s from a few to thousands.
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Source: University of Oulu