EyeMap.ai Labs is a computational ophthalmology research lab developing AI systems for early retinal disease detection. This study aims to evaluate and improve AI models that analyse fundus photographs (photographs of the back of the eye) to detect conditions including age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma.
This study involves analysis of retinal images. It does not involve any new medical procedures. All imaging is performed using standard optometric equipment already in use at participating sites.
Retinal diseases are a leading cause of preventable vision loss worldwide. Many individuals are not screened until symptoms are already apparent, at which point irreversible damage may have occurred. AI-based screening tools have the potential to dramatically increase the reach and frequency of retinal health assessment — particularly in non-specialist settings like optometry practices and pharmacies.
To develop safe, accurate, and fair AI screening tools, we need to validate our models across diverse populations.
If you agree to take part, the following will happen:
Risks: The risks of participation are very low. Fundus photography is a routine, non-invasive procedure. The brief flash of light may cause momentary visual disturbance lasting a few seconds. There are no known risks from the imaging itself.
Data risk: As with all health data research, there is a theoretical risk of re-identification, despite the robust anonymisation measures we apply. This risk is very small and is addressed in detail in the Data Rights document.
Benefits to you: You will receive an information summary about your fundus image. If the AI system flags any finding that warrants clinical attention, you will be advised to discuss this with your optometrist or GP. This may result in earlier detection of an eye condition for some participants.
Benefits to society: Your contribution will help develop AI tools that could one day enable earlier detection of blinding eye diseases across large populations.
Your fundus images and questionnaire responses will be stored securely and used exclusively for the purposes described in this information sheet. Specifically:
All information collected about you during the course of this research will be kept strictly confidential. Your data will be identified by a participant code only. Any publication of results will use anonymised, aggregated data — you will not be identifiable in any publication or presentation arising from this research.
Confidentiality would only need to be broken in the highly unlikely event that information emerged indicating a serious risk of harm to you or to another identifiable person. In such circumstances, we would discuss this with you wherever possible before taking any action.
Participation is entirely voluntary. You are free to withdraw at any time and without providing a reason. Withdrawal will have no effect on your care or on your relationship with any service or institution.
If you withdraw after your data has been collected but before it has been anonymised and incorporated into the anonymised dataset, we will delete your data upon request. Once your data has been fully anonymised and merged into the research dataset, it may not be possible to identify and remove your specific contribution — this is because the anonymisation process is irreversible by design, which protects your identity.
To withdraw, contact the principal investigator at alex [at] eyemap [dot] ai or use the contact details below.
The results of this study will be used to validate and improve EyeMap.ai's diagnostic AI models. We aim to publish findings in peer-reviewed scientific journals and present results at academic conferences. All publications will use anonymised, aggregated data.
If you would like to be informed of the study's published results, please indicate this on your consent form and provide an email address for this purpose. We will send you a plain-English summary of key findings when they are available.
This study is being conducted in accordance with applicable research ethics standards. We are committed to meeting the requirements of the UK Policy Framework for Health and Social Care Research and, where applicable, the Declaration of Helsinki. Ethical oversight details are provided in the study-specific appendix to this information sheet.
Alexandru Dancu, PhD
EyeMap.ai Labs, Ophthalmic AI Research
Email: alex [at] eyemap [dot] ai
If you have a concern or complaint about any aspect of this study that you do not wish to raise with the research team, please contact the relevant ethics committee identified in the study-specific appendix. Your right to complain will not be affected in any way by taking part in this research.