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Artificial Intelligence-Assisted Lesion-Based Urgent Referral Triage of Ultra-Widefield Retinal Images

NCT07643129 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 8

Last updated 2026-06-15

No results posted yet for this study

Summary

his study evaluates the clinical utility of an artificial intelligence (AI)-assisted lesion-based urgent referral triage system for ultra-widefield (UWF) retinal images.

Unlike disease-classification systems, the AI system identifies predefined vision-threatening retinal findings and generates lesion-level urgent referral recommendations. Participating ophthalmologists will evaluate UWF retinal images under randomized AI-assisted and unassisted conditions.

The primary objective is to determine whether lesion-based AI assistance improves urgent referral triage performance compared with unaided image interpretation.

Conditions

  • Vision-Threatening Retinal Lesions
  • Urgent Referral Retinal Findings
  • Retinal Detachment
  • Pre-retinal Hemorrhage
  • Subretinal Hemorrhage
  • Retinal Neovascularization

Interventions

DIAGNOSTIC_TEST

AI-Assisted UWF Lesion-Based Triage System

Readers interpret UWF retinal images with lesion-level AI findings and urgent referral recommendations.

DIAGNOSTIC_TEST

Unassisted Interpretation

Readers interpret UWF retinal images without AI assistance.

Sponsors & Collaborators

  • Xiamen Ophthalmology Center Affiliated to Xiamen University

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Model
FACTORIAL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-06-15
Primary Completion
2026-06-25
Completion
2026-06-30

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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 NCT07643129 on ClinicalTrials.gov