Retinal Clinical Assessment With AI-derived Quantitative Information

Starting soon

Conditions studied: no Obvious Abnormalities, Diabetic Retinopathy (DR), AMD, Cup-to-disc Ratio Bigger Than 0.5, Pathological Myopia, Macular Hole, Epiretinal Membrane, Retinal Vein Occlusion (RVO)

In brief

This randomized controlled trial evaluates whether providing clinicians with AI-derived quantitative retinal information improves the quality and efficiency of retinal clinical assessment. Participating ophthalmologists and ophthalmology trainees will be randomly assigned to one of two groups. The intervention group will write clinical reports with access to automated quantitative measurements generated from fundus image analysis, including multiple retinal structural and vascular biomarkers. The control group will complete the same reporting tasks using only the original fundus images without AI-generated quantitative information. All reports produced by both groups will be de-identified and independently evaluated by a separate panel of senior ophthalmologists who are blinded to group allocation. The expert evaluators will assess report accuracy, completeness, clarity, and overall clinical quality using predefined scoring criteria. The study aims to determine whether access to quantitative retinal biomarkers enhances clinicians' reporting performance and reduces reporting time during retinal assessment tasks.

Key facts

Study ID

NCT07291960

Run by

Beijing Tongren Hospital

People needed

29

Starts

2026-04-15

Expected to finish

2026-05-15

Last updated by the study team

2026-04-29

Who can join

Age: any. Sex: any. Healthy volunteers: accepted.

You may qualify if…

• Clinician Participants (Report Writers)
• Board-certified ophthalmologists or ophthalmology trainees (registrars or fellows) with clinical experience in interpreting fundus images.
• Capable of independently completing retinal clinical reports based on fundus photography.
• Willing and able to participate in the study tasks (report writing) under assigned study conditions.
• Able to provide informed consent.
• Expert Evaluators (Outcome Assessors)
• Senior ophthalmologists with at least 5 years of post-certification clinical experience.
• Not involved in the report-writing stage of the study.
• Willing to evaluate de-identified reports across predefined quality dimensions.
• Able to provide informed consent.
• Fundus Images (Data Inputs)
• Retinal fundus photographs of sufficient quality for clinical interpretation.
• Images representing a range of common retinal findings (normal or abnormal).
• Previously collected, de-identified images with no patient-identifiable information.

You may not qualify if…

• Clinician Participants
• Lack of experience in interpreting fundus images (e.g., interns, medical students).
• Prior involvement in the development, training, or validation of the AI system being tested.
• Inability to complete reporting tasks due to time constraints or technical limitations.
• Any condition that may interfere with ability to perform study tasks (e.g., prolonged absence).
• Expert Evaluators
• Participation in the intervention or control reporting arms.
• Prior exposure to or involvement in development of the AI system.
• Any conflict of interest affecting impartiality of report quality evaluation.
• Fundus Images
• Poor-quality images with insufficient clarity for interpretation.
• Images containing artifacts or cropping that prevent accurate segmentation or assessment.
• Images with any remaining patient identifiers (excluded to maintain confidentiality).

Full record on ClinicalTrials.gov

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