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Retinal Clinical Assessment With AI-derived Quantitative Information

NCT07291960 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 29

Last updated 2026-04-29

No results posted yet for this study

Summary

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.

Conditions

  • 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)

Interventions

DIAGNOSTIC_TEST

AI-derived retinal quantitative information-assisted reporting

Clinicians assigned to the intervention arm will complete retinal clinical reports with access to an AI system that provides automated retinal feature quantification. The system generates multiple quantitative retinal biomarkers-including vessel characteristics, optic nerve head metrics, macular indices, and other region-specific structural measurements-derived from automated segmentation of each fundus image. During report writing, clinicians can view these AI-generated quantitative values alongside the image. The system does not provide diagnostic labels, impressions, or textual interpretations; it only supplies numerical measurements intended to support clinicians' assessment. All clinical judgments, narrative descriptions, and final conclusions in the report are made solely by the clinician.

Sponsors & Collaborators

  • Beijing Tongren Hospital

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-04-15
Primary Completion
2026-05-15
Completion
2026-05-15

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07291960 on ClinicalTrials.gov