Data Use Application 005
Lead applicant organisation name
Name of the legal entity that signs the contract to access the data.
Seon Diagnostics Ltd.
Project title
The title of the project/research study request that the applicant is investigating through the use of health data.
Explainable Artificial Intelligence (AI) as the future of Automated Diabetic Retinopathy Image Assessment Software and the development of a retinal abnormality detection medical device.
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.
Diabetic Retinopathy (DR) is a common potentially serious complication of diabetes which can lead to irreversible damage to sight and even blindness if not detected early and treated. The only proven way to ensure that DR is detected early and treated is through routine, annual eye examinations. The NHS has established a world-leading diabetic eye screening programme that offers annual eye examinations to all people aged 12 and over. Comprehensive diabetic eye screening programmes are very effective in minimising the risk of irreversible sight loss. However, whilst cost-effective, these programmes are expensive to run and, crucially, there is a persistent shortage of qualified staff to undertake the examinations and assess the pictures taken in the examination.
Over the last decade several Automated Diabetic Retinopathy Image Assessment Software's (ARIASs) have been developed as tools to aid, or replace, the current approach to DR screening that is based on grading by trained professionals (human diabetic retinopathy graders). However, while all the currently available tools claim to detect, with variable levels of confidence, moderate or serious disease that needs to be seen by a senior doctor, none of them can provide the patient and their doctors with an explanation of how the ARIAS arrived at its result. These technologies are often referred to as ‘black boxes’ as the results they produce cannot be interrogated.
This project aims to address the black box issue by developing a system based on sophisticated computing technology that closely follows the approach a human diabetic retinopathy grader. Our proposed solution will mimic the steps a human grader takes in assessing the disease present in a patient’s eye - firstly by identifying the kinds of disease present, the amount and its location and then, secondly, by translating this to a disease grade based on local grading procedures. In this way, the patient’s doctor will be presented with a grading result and the basis on how that result was determined by the ARIAS. Underpinned by extensive testing, we believe this approach will give patients and their doctors the confidence to integrate automated DR detection into the national diabetic eye screening programme. Additionally, we believe that millions of people around the world living with diabetes and at risk of sight loss can be helped by the ARIAS as a low-cost way to make routine diabetic eye screening available to them.
Public benefit statement
A description in plain English of the anticipated outcomes, or impact of project on the general public.
In a UK aging population with a rapidly growing incidence of type II diabetes, earlier detection and intervention will result in better outcomes for patients and positively impact on the cost to people and society of the burden of diabetes and other nondiabetics related abnormalities. The benefits of the ARIAS for patients living with diabetes in the UK would be expected to translate across all health systems around the world, especially those (the vast majority) where national diabetic eye screening programmes do not exist. Seon Diagnostics believes that its core technology has the potential to be deployed in the detection and early diagnosis of other pathologies in the eye based on image data acquired using other imaging modalities (for example, Optical Coherence Tomography). It is also feasible that this core technology may be able to detect signs in the eye that are predictive of other diseases and the metadata associated with the INSIGHT dataset holds the potential to expand these possibilities markedly.
Latest approval date
The last date the data access request for this project was approved by a data custodian.
18 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 released under Data Licensing Agreement to a secure Trusted Research Environment provided by the University Hospitals Birmingham as the NHS Data Controller.
Data sensitivity level
The level of identifiability of the data being accessed, as defined by Understanding Patient Data.
Anonymised.