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
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
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.
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:
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.
Inclusion Criteria:
Exclusion Criteria:
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