Artificial Intelligence for Diagnosing Diabetic Retinopathy in Primary Care (AID-DR)
Effects of Artificial Intelligence-based Diabetic Retinopathy Screening on Timely Access to Treatment in Individuals With Diabetes
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Beatriz D Schaan, MD, PhD
- Phone Number: +55 51 3359-8127
- Email: bschaan@hcpa.edu.br
Study Locations
-
-
Rio Grande do Sul
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Porto Alegre, Rio Grande do Sul, Brazil, 90035-903
- Recruiting
- Hospital de Clinicas de Porto Alegre
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Contact:
- Beatriz D Schaan, MD, PhD
- Phone Number: +55 51 3359-8127
- Email: bschaan@hcpa.edu.br
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Principal Investigator:
- Fernando K Malerbi, MD, PhD
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Principal Investigator:
- Caio Vinicius Regatieri, MD, PhD
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Principal Investigator:
- Dimitris RV Rados, MD, PhD
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Sub-Investigator:
- Greice Caletti, PhD
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Principal Investigator:
- Jeruza L Neyeloff, MD, PhD
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Sub-Investigator:
- Luis Filipe Nakayama, MD
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Principal Investigator:
- Natan Katz, MD, PhD
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Sub-Investigator:
- Gabriel Leivas, MD
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Sub-Investigator:
- Josiane Schneiders
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-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
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
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Screening
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: Quadruple
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Experimental: RDIA group
The proposed intervention is the provision of universal screening for DR with retinography performed on a mobile fundus camera under mydriasis supported by a computer program in adults with diabetes mellitus; based on the diagnosis made by the computer program, patients will be classified as having referable DR or not, and referral to a specialist will be based on this classification.
|
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.
|
|
Active Comparator: RDOF group
The proposed control is the provision of universal screening for DR in adults with diabetes mellitus with retinography performed on a mobile fundus camera under mydriasis and with obtained images being interpreted remotely by ophthalmologists; based on the diagnosis made by the ophthalmologist, patients will be classified as having referable DR or not, and referral to a specialist will be based on this classification.
|
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.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Number of appropriate referrals to ophthalmologist
Time Frame: Through study completion, an average of 1 year
|
Appropriate referrals: asymptomatic patients with moderate to severe nonproliferative DR, proliferative DR, significant macular edema, and uninterpretable exams.
This data will be obtained by reviewing the medical records of ophthalmological in-person consults of included patients.
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Through study completion, an average of 1 year
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Number of patients referred for laser sessions
Time Frame: Through study completion, an average of 1 year
|
Considering only patients referred for argon laser sessions aiming retinal photocoagulation for the treatment of diabetic retinopathy.
This data will be obtained through access to the medical records of patients referred for specialized care.
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Through study completion, an average of 1 year
|
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Number of pharmacological intraocular treatment
Time Frame: Through study completion, an average of 1 year
|
Considering treatment with anti-VEGF drugs or intravitreal corticosteroids for the treatment of diabetic retinopathy and diabetic macular edema.
This data will be obtained through access to the medical records of patients referred for specialized care.
|
Through study completion, an average of 1 year
|
|
Referral failure
Time Frame: After study completion, an average of 15 months
|
Screening with false negative result.
This data will be obtained by an independent ophthalmologist review of all images of the not referred patients.
|
After study completion, an average of 15 months
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Beatriz D Schaan, MD, PhD, Hospital de Clinicas de Porto Alegre
Publications and helpful links
General 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.
- 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.
- 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/s13098-024-01447-0.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 2024-0238
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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