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Kidney disease identification using artificial intelligence and retinal photographs in people with diabetes

A deep-learning based tool for prediction of chronic kidney disease from retinal images in people with type 2 diabetes - NIL

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2024/04/065141
Enrollment
2400
Registered
2024-04-03
Start date
Unknown
Completion date
Unknown
Last updated
2024-04-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Health Condition 1: E112- Type 2 diabetes mellitus with kidney complications

Interventions

Intervention1: NIL: NIL

Sponsors

European Foundation for the Study of Diabetes
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: 1. Data of individuals with type 2 diabetes aged = 18 years who have provided written informed consent for use of their anonymised data. 2. Data of individuals with and without diabetic kidney disease 3. Individuals who have clear retinal images 4. Retinal images with and without diabetic retinopathy changes

Exclusion criteria

Exclusion criteria: 1.Unclear retinal images due to media opacities 2.Clinical and image data of those who have not provided consent for use of anonymized data

Design outcomes

Primary

MeasureTime frame
Development of deep learning algorithm for prediction of kidney disease using retinal images among type 2 diabetesTimepoint: 1 year—

Secondary

MeasureTime frame
Real time validation deep learning algorithm developed for prediction of kidney disease using retinal imagesTimepoint: 6 months—

Countries

India

Contacts

Public ContactDr Viswanathan Mohan

Madras Diabetes Research Foundation

drmohans@diabetes.ind.in04443968888

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Feb 4, 2026