The three most common chronic eye conditions –  age-related macular degeneration (AMD), retinal vein occlusion (RVO) and diabetic retinopathy (DR) – are all treated by ophthalmologists with injections into the eye.  A new study by researchers at the University of Bern and the Inselspital has tapped AI to develop an automated tool that can help determine the ideal frequency of these injections in order to prevent blindness.

According to an announcement of the project, one consequence of the world’s aging population is that cases of AMD, RVO or DME are globally on the rise, making it hard for specialized eye clinics to keep up with the growing demand for regular treatments. 

"As doctors, we want to give each patient the necessary attention and treatment frequency that they need", said Sebastian Wolf, Head of the Ophthalmology Department of the Inselspital that currently sees 6000 patients with AMD, RVO and DR. "But it is also an organizational challenge to meet all patients' needs and be able to study all relevant eye imaging data to assess individual disease progression and take treatment decisions in the short time given."

To monitor progression of the three eye conditions, Optical Coherence Tomography (OCT), an imaging tool that generates 3D images of the eye at extremely high resolution, is usually applied. In collaboration with the ARTORG Center for Biomedical Engineering Research, the Inselspital has developed automated OCT analysis tools based on artificial intelligence, which can assist eye doctors in the assessment of a whole patient OCT-set in just a few seconds. Together with RetinAI, a startup specialized in AI-based eye care technologies, they now have conducted a retrospective study of patients to assess how well AI can predict anti-vascular endothelial growth factor (anti-VEGF) treatment demand from the start.

For the study, researchers examined OCT-data from 340 patients with AMD and 285 patients with RVO or DME who had been treated with anti-VEGF at the Inselspital between 2014 and 2018. Based on morphological features automatically extracted from the OCT volumes at baseline and after two consecutive visits, as well as patient demographic information, two machine learning models were trained to predict the probability of the long-term treatment frequency demand of a new patient (one for AMD and one for RVO and DME).

Based on the first three visits, it was possible to predict if a patient had a low or a high treatment demand for both the AMD and the RVO & DME groups with similar high accuracy.

"We have shown that machine learning classifiers can predict treatment demand when a patient is first diagnosed with a chronic eye disease," explained Mathias Gallardo, postdoctoral researcher the ARTORG AI in Medical Imaging (AIMI) lab and member of the new Center for Artificial Intelligence in Medicine (CAIM). "Hence, artificial intelligence may assist in establishing patient-specific treatment plans for the most common chronic eye conditions in the near future."

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