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Optimization and Validation of an Artificial Intelligence Model for Predicting Chronic Kidney Disease Using Retinal?Fundus?Images

Building a healthier future: Artificial Intelligence-based early diagnosis of chronic kidney disease through retinal images - NIL

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2025/08/093433
Enrollment
3384
Registered
2025-08-21
Start date
Unknown
Completion date
Unknown
Last updated
2025-09-15

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

Conditions

Health Condition 1: N189- Chronic kidney disease, unspecified

Interventions

Intervention1: Participants with confirmed diagnosis of Chronic Kidney Disease (CKD): In the intervention arm, individuals with a confirmed diagnosis of Chronic Kidney Disease (CKD) will undergo urine

Sponsors

LifeBytes India Private Limited
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Patients with CKD (all stages), diabetes, or hypertension presenting at PGIMER, Chandigarh, will be recruited to identify retinal markers for early CKD diagnosis.

Exclusion criteria

Exclusion criteria: Patients with a history of eye injury or prior eye surgery will be excluded.

Design outcomes

Primary

MeasureTime frame
serum creatinine and proteinuriaTimepoint: baseline and three monthly till 2 years—

Secondary

MeasureTime frame
NILTimepoint: NIL—

Countries

India

Contacts

Public ContactSmita Divyaveer

PGIMER Chandigarh

divyaveer.ss@gmail.com8208839726

Outcome results

None listed

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