Artificial Intelligence for Diagnosing Diabetic Retinopathy in Primary Care
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Mobile retinography interpreted by artificial intelligence, Mobile retinography interpreted by ophthalmologists.
- Who it may be relevant to
- Registry conditions: Diabetic Retinopathy. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Brazil
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Effects of Artificial Intelligence-based Diabetic Retinopathy Screening on Timely Access to Treatment in Individuals With Diabetes
Overview
This is a clinical trial to evaluate the effects of universal screening for diabetic retinopathy (DR) and diabetic macular edema (DME) using artificial intelligence (AI) in the interpretation of fundus photographs obtained by trained nursing assistant using a portable fundus camera in a primary care setting, compared with images obtained by the same method, but interpreted by ophthalmologists.
Interventions
- Diagnostic test Mobile retinography interpreted by artificial intelligence
All participants will undergo mobile retinal photography in primary care by a trained nursing assistant. If randomized to RDIA group, their photos will be analyzed by artificial intelligence. - Diagnostic test Mobile retinography interpreted by ophthalmologists
All participants will undergo mobile retinal photography in primary care by a trained nursing assistant. If randomized to RDOF group, their photos will be interpreted remotely by ophthalmologists.
Primary outcome measures
- Number of appropriate referrals to ophthalmologist [Time frame: Through study completion, an average of 1 year]
Secondary outcome measures (3)
- Number of patients referred for laser sessions [Time frame: Through study completion, an average of 1 year]
- Number of pharmacological intraocular treatment [Time frame: Through study completion, an average of 1 year]
- Referral failure [Time frame: After study completion, an average of 15 months]
Eligibility criteria
Inclusion criteria
- Adults (> 18 years old) diagnosed with diabetes mellitus who agree to participate in the study.
Exclusion criteria
- Any contraindication for pharmacological mydriasis (such as knowledge of having closed-angle glaucoma, pregnancy).
- Life expectancy less than 6 months
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Quadruple blind
- Primary purpose
- Screening
Study locations
Brazil · 1 center
- Hospital de Clínicas de Porto Alegre — Porto Alegre
Publications
- Chagas TA, Dos Reis MA, Leivas G, Santos LP, Gossenheimer AN, Melo GB, Malerbi FK, Schaan BD. Prevalence of diabetic retinopathy in Brazil: a systematic review with meta-analysis. Diabetol Metab Syndr. 2023 Mar 2;15(1):34. doi: 10.1186/s13098-023-01003-2. PMID 36864478
- Schneiders J, Telo GH, Lavinsky D, Dos Reis MA, Correa BG, Schaan BD. Organizational intervention to improve access to retinopathy screening for patients with diabetes mellitus: health care service improvement project in a tertiary public hospital. Prim Care Diabetes. 2023 Aug;17(4):354-358. doi: 10.1016/j.pcd.2023.05.007. Epub 2023 Jun 14. PMID 37328386
- Dos Reis MA, Kunas CA, da Silva Araujo T, Schneiders J, de Azevedo PB, Nakayama LF, Rados DRV, Umpierre RN, Berwanger O, Lavinsky D, Malerbi FK, Navaux POA, Schaan BD. Advancing healthcare with artificial intelligence: diagnostic accuracy of machine learning algorithm in diagnosis of diabetic retinopathy in the Brazilian population. Diabetol Metab Syndr. 2024 Aug 29;16(1):209. doi: 10.1186/s13098- PMID 39210394
Identifiers
NCT: NCT07236879 · 2024-0238