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Automated Analysis of Retinal Images for Detection of Diabetic Retinopathy using Artificial Intelligence (AI)

Automated Analysis of Retinal Images for Detection of Diabetic Retinopathy using Artificial Intelligence (AI)

Status
Unknown
Phases
Unknown
Study type
Observational
Source
TCTR
Registry ID
TCTR20211122006
Enrollment
10000
Registered
2021-11-22
Start date
2021-11-01
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

Patients with any type of diabetes mellitus Diabetic retinopathy screening Image enhancement Image analysis Color retinal image Artificial intelligence Diabetic retinopathy

Interventions

Diabetic patients (type 1 or 2) who come for a diabetic retinopathy screening
Diagnostic
Diabetic patients

Sponsors

National Higher Education Science Research and Innovation Policy Council (NXPO)
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Type 1 or 2 diabetes mellitus who come for a diabetic retinopathy screening at the eye clinic of Songklanagarind Hospital, 2. At least 18 years old of age, 3. Able to take fundus photography

Exclusion criteria

Exclusion criteria: 1. Poor quality of fundus photograph that could not be evaluated, 2. Retinal pathologies other than diabetic retinopathy

Design outcomes

Primary

MeasureTime frame
Correction of DR severity grading by AI 1 year Sensitivity and Specificity

Secondary

MeasureTime frame
Agreement between AI and specialist grading 6 months Kappa statistic

Countries

Thailand

Contacts

Public ContactPatama Bhurayanontachai

Prince of Songkla University

patama.b@psu.ac.th0897330848

Outcome results

None listed

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