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

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.

• Sensitivity for Urgent Referral Findings [Срок оценки: Through study completion, up to 2 months]
• Specificity for Urgent Referral Findings [Срок оценки: Through study completion, up to 2 months]
• False-Negative Rate for Urgent Referral Findings [Срок оценки: Through study completion, up to 2 months]
• False-Positive Rate for Urgent Referral Findings [Срок оценки: Through study completion, up to 2 months]
• Reader Confidence Score [Срок оценки: Immediately after image interpretation.]
• Change in Correct Urgent Referral Decisions After AI Assistance [Срок оценки: Through study completion, up to 2 months]