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Verification and application of convolutional neural networks for automated screening of diabetic retinopathy in Siriraj hospital

Verification and application of convolutional neural networks for automated screening of diabetic retinopathy in Siriraj hospital

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
TCTR
Registry ID
TCTR20200513003
Enrollment
1520
Registered
2020-05-13
Start date
2020-01-20
Completion date
Unknown
Last updated
2026-08-03

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

Conditions

Diabetes patient with no DR mild NPDR moderate NPDR severe NPDR PDR other retinopathy Diabetes patient with no DR mild NPDR moderate NPDR severe NPDR PDR other retinopathy

Interventions

Diabetes patient with no DR to mild NPDR,Diabetes patient with moderate NPDR&#44
severe NPDR&#44
PDR&#44
other retinopathy
Diagnostic,Diagnostic
Non referrable DR ,Referrable DR or other retinopathy

Sponsors

Routine To Research Unit (R2R)
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: Diabetes patient who visit outpatient department in Siriraj hospital

Exclusion criteria

Exclusion criteria: Poor quality fundus photo from diabetes patient Inconclusive clinical diagnosis

Design outcomes

Primary

MeasureTime frame
accuracy of automated screening tool for detection of diabetic retinopathy at the time of intervention sensitivity, specificity, PPV, NPV , ROC curve—

Secondary

MeasureTime frame
Program validation at the end of intervention Accuracy—

Countries

Thailand

Contacts

Public ContactNida Wongchaisuwat

Department of Ophthalmology, Faculty of Medicine Siriraj Hospital, Mahidol University

nida.oph@gmail.com0875952336

Outcome results

None listed

Source: TCTR (via WHO ICTRP) · Data processed: Aug 9, 2026