Pre-retinal Hemorrhage, Retinal Detachment, Retinal Neovascularization, Subretinal Hemorrhage, Urgent Referral Retinal Findings, Vision-Threatening Retinal Lesions
Conditions
Keywords
Artificial Intelligence, Urgent Referral, Lesion-Based Triage, Ultra-Widefield Imaging, Clinical Decision Support, Multi-Reader Multi-Case Study
Brief 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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTAI-Assisted UWF Lesion-Based Triage System
Readers interpret UWF retinal images with lesion-level AI findings and urgent referral recommendations.
DIAGNOSTIC_TESTUnassisted Interpretation
Readers interpret UWF retinal images without AI assistance.
Sponsors
Xiamen Ophthalmology Center Affiliated to Xiamen University
Study design
Allocation
RANDOMIZED
Intervention model
FACTORIAL
Primary purpose
DIAGNOSTIC
Masking
NONE
Intervention model description
Each participating ophthalmologist will independently review a library of 600 UWF retinal images. For each reader, cases will be randomly assigned to either: * AI-assisted interpretation * Unassisted interpretation Readers will initially provide an interpretation without AI support. For AI-assigned cases, lesion-level AI findings and urgent referral recommendations will subsequently be displayed before final decision making.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Correct Lesion-Based Urgent Referral Triage Rate | Through study completion, up to 2 months | Proportion of reader referral decisions consistent with expert-adjudicated lesion-based urgent referral classifications. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity for Urgent Referral Findings | Through study completion, up to 2 months | Sensitivity for correctly classifying non-urgent referral images according to expert-adjudicated lesion-based triage labels. |
| Specificity for Urgent Referral Findings | Through study completion, up to 2 months | Specificity for correctly classifying non-urgent referral images according to expert-adjudicated lesion-based triage labels. |
| False-Negative Rate for Urgent Referral Findings | Through study completion, up to 2 months | Proportion of urgent referral images incorrectly classified as non-urgent referral by readers. |
| False-Positive Rate for Urgent Referral Findings | Through study completion, up to 2 months | Proportion of non-urgent referral images incorrectly classified as urgent referral by readers. |
| Reader Confidence Score | 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). |
| Change in Correct Urgent Referral Decisions After AI Assistance | 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. |
Contacts
CONTACTXiuju Chen, md
Outcome results
None listed