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.
Licensed ophthalmologists
Willing to participate as readers
Completion of study training
Retinal specialists involved in establishing gold-standard labels
Prior access to gold-standard labels
Incomplete study participation
Factorial
AI-Assisted UWF Lesion-Based Triage System
Diagnostic testReaders interpret UWF retinal images with lesion-level AI findings and urgent referral recommendations.
Unassisted Interpretation
Diagnostic testReaders interpret UWF retinal images without AI assistance.
Correct Lesion-Based Urgent Referral Triage Rate
Proportion of reader referral decisions consistent with expert-adjudicated lesion-based urgent referral classifications.
Sensitivity for Urgent Referral Findings
Sensitivity for correctly classifying non-urgent referral images according to expert-adjudicated lesion-based triage labels.
Specificity for Urgent Referral Findings
Specificity for correctly classifying non-urgent referral images according to expert-adjudicated lesion-based triage labels.
False-Negative Rate for Urgent Referral Findings
Proportion of urgent referral images incorrectly classified as non-urgent referral by readers.
False-Positive Rate for Urgent Referral Findings
Proportion of non-urgent referral images incorrectly classified as urgent referral by readers.
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