AI accelerates Inherited Retinal Disease Analysis in largest study to date 

Researchers and clinicians at the UCL Institute of Ophthalmology and NIHR Moorfields Biomedical Research Centre, have developed a deep learning model, ‘AIRDetect’ to process fundus autofluorescence (FAF) retinal imaging, a critical imaging modality for diagnosing and managing inherited retinal diseases. Interpretation of FAF imaging requires specialist knowledge, is prone to human-to-human variability, and is time-consuming.
 
FAF imaging can detect retinal abnormalities at an early stage, key for timely and accurate diagnosis. This large-scale study successfully analysed a total of 45,749 retinal images from 3,606 Moorfields patients with a diverse range of inherited retinal conditions which can now be compared more quantitatively.  Previous research in this area has been limited to single conditions due to the large number of images to review. The study highlights the potential of AI-driven research to uncover patterns that were previously too time-consuming or too complex to analyse. 
 

Links

• Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of >3500 Patients with Inherited Retinal Disease from the United Kingdom
• Dr William A Woof
• Professor Michel Michaelides
• Professor Konstantinos Balaskas
• Associate Professor Nikolas Pontikos

Image

• Section from Figure 3 on published paper: Examples of manually and automatically segmented masks for the 5 features: vessels, disc, ring, hyper-autofluorescence (hyper-AF), and hypo-autofluorescence (hypo-AF). The vessel data set was separate from the rest of the data; hence, vessel visualization is separate from other features