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Data Use Application 004

Lead applicant organisation name

Name of the legal entity that signs the contract to access the data.

Orbis International.

Project title

The title of the project/research study request that the applicant is investigating through the use of health data.

Open-Access Application for Artificial Intelligence (AI) assisted Diabetic Retinopathy Screening Based on UK National Screening Committee guidelines.

Lay summary

A concise and clear description of the project.  This should outline the problem, objective and the expected outcomes in language that is understandable to the general public.

This project aims to develop and validate an open-access application for AI-assisted screening of Diabetic Retinopathy (DR) in low- to middle-income countries (LMICs) based on the screening guidelines introduced by the UK National Screening Committee (UK NSC). The vast majority of adults with diabetes live in LMICs. One in three people with diabetes will be affected by DR and one in ten will be impacted by sight loss as a result. DR impacts active, working-age adults, and as thus can be economically devastative for both affected individuals and society as a whole. While the UK is leading the way in terms of DR screening, most LMICs do not have screening programmes in place, due in part a severe shortage of specialists. Methods based on recent advances in AI can reliably detect sight-threatening DR with levels of performance comparable to Human experts. However, most commercially available solutions are based on the International Clinical Diabetic Retinopathy severity scale, which is incompatible with UK NSC guidelines. As a result, those AI solutions cannot be used in LMICs that adopted NSC guidelines for their screening programmes or as standard of care for DR. Moreover, the cost associated with the procurement of commercial AI solutions can be prohibitive; communities that have the most to gain from the AI revolution are the least likely to benefit from it. In this two-year project, we set out to develop an application leveraging recent advances in AI to facilitate DR screening in LMICs in a way that is compatible with UK NHC guidelines. The application will be integrated with our existing Cybersight AI platform, and will be made freely available to users in LMICs.

Public benefit statement

A description in plain English of the anticipated outcomes, or impact of project on the general public.

Cybersight AI is currently accessible free-of-charge to healthcare professionals in over 140 LMICs. While it is being adopted as clinical decision support for DR grading, incompatibility with UK NSC precludes implementation in many countries and regions. This also holds true for the majority of commercially available solutions. If the project is successful, Cybersight AI will support automated DR grading based on NSC guidelines, and will thus be used as part of screening programmes where Orbis operates. As a result, it is expected that the public will benefit from the work carried out as part of this project. In addition, the development process and results will be documented and published, establishing a baseline for automated DR grading based on UK NSC guidelines.

Latest approval date

The last date the data access request for this project was approved by a data custodian.

01 November 2022.

Dataset(s) name

The name of the dataset(s) being accessed.

Diabetic Eye Screening: the Birmingham, Solihull and Black Country.

Access type

Determines whether the data will be accessed with an Trusted Research Environment (TRE) or via data release.

Data Licence Agreement with data released to a secure Trusted Research Environment under contract with the research applicant.

Data sensitivity level

The level of identifiability of the data being accessed, as defined by Understanding Patient Data.


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