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NCT Number: NCT07643129

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

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.

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Key information

Conditions

Vision-Threatening Retinal Lesions Eye Diseases Metaplasia Neovascularization, Pathologic Pathologic Processes Pathological Conditions, Signs and Symptoms Pre-retinal Hemorrhage Retinal Detachment Retinal Diseases Retinal Neovascularization Subretinal Hemorrhage Urgent Referral Retinal Findings

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

About this study

Ultra-widefield retinal imaging is increasingly used for retinal disease screening and referral triage. Many vision-threatening retinal abnormalities require timely identification and referral to retinal specialists.

The AI system evaluated in this study is designed as a lesion-based triage tool rather than a disease-diagnosis system. The model identifies predefined urgent referral retinal findings and generates referral recommendations based on lesion-level evidence.

Urgent referral findings include:

  • Retinal detachment
  • Untreated retinal tear or retinal hole
  • Vitreous hemorrhage
  • Pre-retinal hemorrhage
  • Subretinal hemorrhage
  • Retinal neovascularization
  • Optic disc neovascularization
  • Tractional fibrovascular membrane Treated retinal tears associated with laser barricade scars are classified as non-urgent referral findings.

A total of 600 UWF retinal images acquired using Zeiss and Optos imaging systems will be included.

Participating ophthalmologists will independently evaluate images in randomized AI-assisted and unassisted settings.

The primary objective is to determine whether AI assistance improves lesion-based urgent referral triage accuracy.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Licensed ophthalmologists
  • Willing to participate as readers
  • Completion of study training

Exclusion criteria

  • Retinal specialists involved in establishing gold-standard labels
  • Prior access to gold-standard labels
  • Incomplete study participation

Treatment and study plan

AI-Assisted UWF Lesion-Based Triage System

Diagnostic Test

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

Unassisted Interpretation

Diagnostic Test

Readers interpret UWF retinal images without AI assistance.

Primary outcomes

  1. Correct Lesion-Based Urgent Referral Triage Rate

    Time frame: Through study completion, up to 2 months

    Proportion of reader referral decisions consistent with expert-adjudicated lesion-based urgent referral classifications.

Secondary outcomes

  1. Sensitivity for Urgent Referral Findings

    Time frame: Through study completion, up to 2 months

    Sensitivity for correctly classifying non-urgent referral images according to expert-adjudicated lesion-based triage labels.

  2. Specificity for Urgent Referral Findings

    Time frame: Through study completion, up to 2 months

    Specificity for correctly classifying non-urgent referral images according to expert-adjudicated lesion-based triage labels.

  3. False-Negative Rate for Urgent Referral Findings

    Time frame: Through study completion, up to 2 months

    Proportion of urgent referral images incorrectly classified as non-urgent referral by readers.

  4. False-Positive Rate for Urgent Referral Findings

    Time frame: Through study completion, up to 2 months

    Proportion of non-urgent referral images incorrectly classified as urgent referral by readers.

  5. Reader Confidence Score

    Time frame: Immediately after image interpretation.

    Reader-reported confidence level for referral decisions measured using a 5-point Likert scale, ranging from 1 (very uncertain) to 5 (very confident).

  6. Change in Correct Urgent Referral Decisions After AI Assistance

    Time frame: Through study completion, up to 2 months

    Number and proportion of cases in which AI assistance changed an incorrect referral decision to a correct referral decision.

Study contacts

Contact information is provided by the study sponsor or research team.

Xiuju Chen, md

CONTACT

[email protected]

+8618060955810

Sponsors and collaborators

Lead sponsor

Xiamen Ophthalmology Center Affiliated to Xiamen University

Other

Registry information

Official study title

Clinical Utility of an Artificial Intelligence-Assisted Lesion-Based Urgent Referral Triage System for Ultra-Widefield Retinal Images: A Prospective Multi-Reader Multi-Case Randomized Reader Study

Acronym: ALERT-UWF

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Jun 11, 2026
Registry last updated
Jun 15, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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