Image Grading

Software Solution

dr grader

Diabetic Retinopathy

Multi-Level Grading Solution


The DR Grader ™️ diabetic retinopathy screening system features an automatic image grading/storage/forward platform and a long-term retinopathy prediction for patients. The deep learning-based DR grading system (DLS) is based on deep learning models, and provides severity grading of DR in line with ETDRS Guidelines .


We are the 1st diabetic retinopathy AI software that was approved in Singapore and Australia, and now also approved for sale in the European Union.


DR Grader consistently achieves between 92%-100% for both sensitivity and specificity, making it world class leading in terms of accuracy.


DR GRADER is a key enabler in making fast and efficient diabetic retinopathy screening possible for primary care providers, diabetes clinics, optical shops and rural clinics, allowing them to track the patient’s health conditions over time and identify referable DR patients.

DR Grader Workflow

Related Publications

Evaluation of Artificial Intelligence–Based Grading of Diabetic Retinopathy in Primary Care

Authors and publisher details:

Yogesan Kanagasingam, Di Xiao, Janardhan Vignaraja, Amita Preetham, Mei-Ling Tay-Kearney and Ateev Mehrotra.

JAMA Network Open, 2018;1(5):e182665. doi:10.1001/jamanetworkopen.2018.2665.  

Major automatic diabetic retinopathy screening systems and related core algorithms: a review

Authors and publisher details:

Di Xiao and Alauddin Bhuiyan and Shaun Frost and Janardhan Vignarajan and Mei-Ling Tay-Kearney and Yogesan Kanagasingam.

Machine Vision and Applications.30:423-446, 2019.

Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine

Authors and publisher details:

Sajib Saha, Basura Fernando, Jorge Cuadros, Di Xiao, Yogesan Kanagasingam.

Journal of Digital Imaging. 31(6): 869–878, 2018. 

Machine Learning Based Automatic Neovascularization Detection on Optic Disc Region

Authors and publisher details:

Shuang Yu, Di Xiao, Yogesan Kanagasingam.

IEEE Journal of Biomedical and Health Informatics

Chapter 2: Screening of the Retina in Diabetes Patients by Morphological Means, Teleophthalmology in Preventive Medicine

Authors and publisher details:

Di Xiao, Yogesan Kanagasingam.

Georg Michelson, Springer Berlin Heidelberg New York Dordrecht London, pp. 15-26, 2015. 

Retinal hemorrhage detection by rule-based and machine learning approach

Authors and publisher details:

Di Xiao, Shuang Yu, Vignarajan J,  Dong An,  Mei-Ling Tay-Kearney, Kanagasingam Y

IEEE Engineering in Medicine and Biology Society. Annual Conference, 2017:660-663.

Exudate detection for diabetic retinopathy with convolutional neural networks

Authors and publisher details:

Shuang Yu, Di Xiao and Yogesan Kanagasingam.

IEEE Engineering in Medicine and Biology Society. Annual Conference 2017:1744-1747.

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