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Accuracy of AI versus Opthalmologist in detecting Diabetic Retinopathy from fundus images

Comparison of the DRISTi -Artificial Intelligence System with Ophthalmologist grading for Detection of Diabetic Retinopathy from fundus images. - NIL

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2025/10/095888
Enrollment
760
Registered
2025-10-10
Start date
Unknown
Completion date
Unknown
Last updated
2025-10-13

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

Conditions

Health Condition 1: H350- Background retinopathy and retinalvascular changes

Interventions

Intervention1: Nil: Nil

Sponsors

none
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: patients presenting for DR screening collected from multiple secondary and tertiary eye care hospitals and community screening site

Exclusion criteria

Exclusion criteria: patients attending hospital for reasons other than DR screening

Design outcomes

Primary

MeasureTime frame
The primary outcome is the sensitivity and specificity of the AI system in detecting eyes with mtmDR or vtDR, compared to the reference standardTimepoint: NA—

Secondary

MeasureTime frame
The primary outcome is the sensitivity and specificity of the AI system in detecting eyes with mtmDR or vtDR, compared to the reference standard. The secondary outcomes include the sensitivity and specificity of the AI system in identifying eyes with DME relative to the reference standard. Timepoint: NA—

Countries

India

Contacts

Public Contactjyothsna rajagopal

ARTELUS-Artificial Learning Systems India Private Limited,

j.rajagopal@artelus.ai9980949289

Outcome results

None listed

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