Scaling Artificial Intelligence for Diabetic Retinopathy Screening: Integrations with the Existing Web-based Platform vs the DICOM System with PACS Server

Status

Active, not recruiting

Phases

Phase 4

Study type

Interventional

Source

TCTR

Registry ID

TCTR20260402005

Enrollment

99999

Registered

2026-04-02

Start date

2026-04-21

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

Diabetic retinopathy screening in adults with diabetes focusing on detection of referable cases comparing AI assisted workflows using web based JPEG platform and DICOM PACS system. Diabetic Retinopathy, Screening, Artificial Intelligence, Deep Learning, DICOM, PACS, Implementation

Interventions

Participants undergo diabetic retinopathy screening using a web based platform where retinal images are uploaded in JPEG format and analyzed by an artificial intelligence system. The results are used

Experimental Other,Active Comparator Other

AI assisted DR screening using web based JPEG platform,AI assisted DR screening using DICOM PACS system

Eligibility

Sex/Gender

All

Age

18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Patients with diabetes mellitus aged 18 years or older 2. Patients with diabetes mellitus who are registered in the civil registration system and are eligible for diabetic retinopathy screening according to the Ministry of Public Health policy 3. Patients who are able to undergo retinal imaging in at least one eye

Exclusion criteria

Exclusion criteria: 1. Patients with a prior diagnosis of the following conditions: macular edema from causes other than diabetic retinopathy, such as age-related macular degeneration (AMD), radiation retinopathy, or retinal vein occlusion 2. History of retinal laser treatment or prior retinal surgery 3. Presence of other ocular diseases (non-diabetic retinopathy) requiring referral to an ophthalmologist 4. Inability to obtain retinal images in both eyes (for any reason) 5. Patients who are unable to provide informed consent or make decisions independently

Design outcomes

Primary

Measure	Time frame
Screening efficiency During study period Composite outcome including screening time, number of patients screened per day, and workflow steps	—

Secondary

Measure	Time frame
Diagnostic performance At time of screening Sensitivity and specificity for detection of referable diabetic retinopathy compared with reference standard	—

Countries

Thailand

Contacts

Public ContactPaisan Ruamviboonsuk

Rajavithi Hospital

paisan.trs@gmail.com022062900

Outcome results

None listed

Source: TCTR (via WHO ICTRP) · Data processed: Aug 10, 2026