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An Eye-Tracking Attention-Aware Reinforcement Learning Algorithm for Quantitative Lesion Evaluation in Diabetic Retinopathy

An Eye-Tracking Attention-Aware Reinforcement Learning Algorithm for Quantitative Lesion Evaluation in Diabetic Retinopathy

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600123723
Enrollment
Unknown
Registered
2026-04-29
Start date
2025-12-01
Completion date
Unknown
Last updated
2026-05-04

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

Conditions

diabetic retinopathy

Interventions

Observation group:N/A

Sponsors

Department of Ophthalmology and Visual Sciences (DOVS), Chinese University of Hong Kong
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Type 1 or Type 2 diabetes; 2. Adult patients of ages above 18 years and above; 3. Chinese; 4. Treatment naïve at baseline; 5. Data collection period ranged from July 2015 to June 2025.

Exclusion criteria

Exclusion criteria: 1. Pregnant; 2. Dementia or major mental diseases; 3. Eye pathology that interferes with retinal imaging (e.g. dense cataract, corneal ulcer) or sufficient image quality; 4. Glaucoma; 5. Presence of any other maculopathy not related to diabetes (e.g. wet age-related macular degeneration and vitreomacular traction)

Design outcomes

Primary

MeasureTime frame
Accuracy;—

Countries

China

Contacts

Public ContactDr JIANG Hongyang

Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong

hongyangjiang@cuhk.edu.hk+852 3943 5856

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: May 7, 2026