How AI can Accelerate Diabetic Retinopathy Detection

Major vision loss due to DR is preventable with timely remedial intervention like eye exams (including visual acuity testing, tonometry and pupil dilation) and regular screening at the earlier stages by using a DR diagnostic and grading screening tool. 

Study: 975 Executives about AI

This process is unfortunately handicapped by lack of trained clinicians, as well as challenges posed by data set availability, fundus image analysis and multiple camera imagery. The screening process is also time-consuming. To combat it, we developed an AI-based DR diagnostic tool to help doctors detect and grade the level of DR disease: 

• The automated detection system makes use of machine learning techniques such as deep convolutional neural networks (CNNs) – neural networks that are used to analyze and classify visual imagery.
• The CNN extracts diagnostic features using a deep learning algorithm trained to classify images across labels to determine whether or not the patient has DR.
• The DR tool will enable doctors to view variations from multiple fundus camera images with the help of image preprocessing techniques. 
• It reduces the time taken for the whole process to less than a minute, from a minimum of 15 minutes manually, and this speed will most likely improve over time.

In the future, clinicians will be able to use our DR detection and grading solution as an app with a mobile-attached, hand-held fundus camera to diagnose DR immediately and guide patients toward further treatment. Curious to learn more? Fill in the form to download the report AI to Eye: Deep Learning Improves Detection of Diabetic Retinopathy. 

Further reading: If you'd like to read more about AI, check out the blog posts Applied AI: How to Determine Your Opportunities, 3 lessons learnt about Artificial Intelligence and 4 Ways AI Will Make a Difference in 2019.