How AI can Accelerate Diabetic Retinopathy Detection

How AI can Accelerate Diabetic Retinopathy Detection

Snapshot Insights

Snapshot Insights,

WHO believes that 80% of all visual impairment caused by DR (Diabetic Retinopathy) can be cured with regular screening and early detection. Follow Cognizant’s journey in developing an AI-based diagnostic tool to speed up identification of DR symptoms.

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. 

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.