Image Grading

Software Solution

dr LITE

 Diabetic Retinopathy “Disease/No Disease” Diagnostic Solution

Description

 

We understand that speed is important for the high volumes expected for community level screening and we have developed DR Lite, which offers binary decision output suitable for referral in seconds.

DR Lite is based on DR Grader, which is our world class leading DR grading software, offering the same levels of sensitivity and specificity.

 

The difference is that DR Lite tells the operator in seconds whether the patient has a referral condition (Disease/No Disease). This is very suitable for situations where the primary care provider does not need to know the different severity levels but needs to know whether the patient needs referral to a specialist.

DR Lite

 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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