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Multimodal Deep Learning Model for Multi-task Diagnosis and Triage Suggestions of Ophthalmic Diseases
Sponsor: Guangdong Provincial People's Hospital
Summary
Accurate and comprehensive interpretation of anterior segment diseases from slit-lamp and smartphone photographs remains a clinical challenge due to the limited specificity and structure of existing Artificial Intelligence tools. The purpose of this international, multicenter clinical trial is to developed and validated an agent-based framework that integrates vision-language models and large language models to enhance the diagnostic workflow of anterior segment diseases.
Official title: Development and Validation of Multimodal Deep Learning Model for Autonomous Diagnosis, Generative Reporting, and Specialist Referral in Ophthalmic Diseases: An International Multicenter Cohort Study
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
2000
Start Date
2025-07-28
Completion Date
2027-12-31
Last Updated
2026-03-05
Healthy Volunteers
Yes
Conditions
Interventions
Multimodal Vision-language Model Diagnosis
Multimodal Vision-language Model for Multi-task Diagnosis and Triage Suggestions of Ophthalmic Diseases Patients presenting with complaints of anterior segment diseases first complete a slit-lamp examination or take a mobile phone eye photograph. A multimodal vision-language model uses patient-related images (such as selfies and eye exam photos) to make an intelligent diagnosis. The diagnosis is kept private. The patient then seeks medical attention and undergoes a clinical examination by an experienced clinician. A second experienced clinician then reviews the clinical diagnosis. If the diagnosis agrees, it is considered the gold standard. If there is a discrepancy in the diagnosis, the consensus between the two clinicians is used as the gold standard.
Locations (1)
Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
Guangzhou, Guangdong, China