AI for the Detection of Retinal Disease and Glaucoma in Patients With Diabetes Mellitus in Primar… (NCT04132401) | Clinical Trial Compass
CompletedNot Applicable
AI for the Detection of Retinal Disease and Glaucoma in Patients With Diabetes Mellitus in Primary Care
Spain902 participantsStarted 2021-05-01
Plain-language summary
Background: Diabetic retinopathy (DR) is one of the most important causes of blindness worldwide, especially in developed countries. In diabetic patients, periodic examination of the back of the eye using a nonmydriatic camera has been widely demonstrated to be an effective system to control and prevent the onset of DR. Convolutional neural networks have been used to detect DR, achieving very high sensitivities and specificities.
Hypothesis: It is possible to develop algorithms based on artificial intelligence that can demonstrate equal or superior performance and that constitute an alternative to the current screening of DR and other ophthalmic pathologies in diabetic patients.
Objectives:
* Development of an artificial intelligence system for the detection of signs of retinal pathology and other ophthalmic pathologies in diabetic patients.
* Scientific validation of the system to be used as a screening system in primary care.
Methods: This project consisted of carrying out two studies simultaneously:
1. Development of an algorithm with artificial intelligence to detect signs of DR and other pathologies of the central retina in patients with diabetes.
2. An observational, cross-sectional study comparing the diagnostic capacity of the algorithms with that of the family medicine specialists who read the fundus images. The reference was double-blind reading by ophthalmologists who specialize in retina.
The cession of the images began at the end of 2018. The images used for the validation were obtained during routine diabetic retinopathy screening between May and August 2021. The results have since been published.
The study allowed the development of an algorithm based on AI able to demonstrate an equal or superior performance, and to constitute a complement or an alternative to the current screening of DR in diabetic patients.
Who can participate
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See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Inclusion Criteria:
* Clinical diagnosis of type II diabetes mellitus
* Fundus photograph taken as part of the screening for diabetic retinopathy
Exclusion Criteria:
* patients with glaucoma under treatment
* patients with advanced dementia who do not collaborate in taking photographs
* patients with significant deafness who cannot follow the instructions for taking photographs
* patients with mobility problems (wheelchairs, important kyphosis) or tremor who cannot take photographs
* patients with pathologies that interfere with the quality of images such as cataracts, nystagmus, corneal leucoma or corneal transplants.
Questions worth asking your doctor
Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.
1Since this trial is already completed and was testing an AI algorithm's ability to detect conditions like diabetic retinopathy and glaucoma in primary care, has the AI tool been validated well enough for my doctor to use it as part of my routine diabetes eye care?
2This study measured how accurately an AI could spot retinal disease and glaucoma in people with diabetes — does my doctor currently have access to AI-assisted eye screening, and would it replace or just supplement a traditional eye exam with an ophthalmologist?
3Because this trial looked at several conditions including diabetic retinopathy, glaucoma, and macular degeneration, should I be getting screened for all of these given my diabetes diagnosis, and how often does my doctor recommend that?
4Since this was a diagnostic accuracy study rather than a treatment trial, what happens next if an AI screening tool flags something concerning in my retina — what would the follow-up process look like for me?
5Given that this trial was conducted in a primary care setting, can my doctor explain whether AI-based retinal screening is something they offer or refer to, and whether it would be appropriate for someone at my stage of diabetes management?
Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
Questions for the trial coordinator
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
1What does taking part actually involve week to week — how many visits, where, and how long does each one take?
2What costs are covered by the study, and what might I have to pay for myself, including travel, parking, or time off work?
3What happens during screening, and what happens if the study team confirms I don't meet the criteria after those tests?
4Who pays for the scans, blood work, and other tests the trial requires — the study, my insurance, or me?
5How will being in the trial affect my regular care, and will my own doctor stay informed and involved?
6Can I leave the trial at any point if I change my mind, and what would happen to my care if I do?
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
What they're measuring
1
Sensitivity of the algorithm
Timeframe: 1 year
2
Specificity of the algorithm
Timeframe: 1 year
3
Accuracy of the algorithm
Timeframe: 1 year
4
Area under the receiver operating characteristic curve of the algorithm
Timeframe: 1 year
Trial details
NCT IDNCT04132401
SponsorFundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina