Multimodal Deep Learning Model for Multi-task Diagnosis and Triage Suggestions of Ophthalmic Diseases
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Multimodal Vision-language Model Diagnosis.
- Who it may be relevant to
- Registry conditions: Anterior Segment Diseases. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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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
Overview
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.
Interventions
- Diagnostic test 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 exp
Primary outcome measures
- Diagnostic accuracy of multimodal vision-language model. [Time frame: from July 2025 to September 2025]
Eligibility criteria
Inclusion criteria
- Informed consent obtained;
- Participants should be sufficiently able to read, write, and understand Chinese or English;
- For normal participants: individuals should have no concerns related to their eyes.
- For participants with eye-related chief complaints: individuals should have specific concerns or issues related to their eyes.
Exclusion criteria
- Incomplete clinical data to support final diagnosis;
- Patients who, in the opinion of the attending physician or clinical study staff, are too medically unstable to participate in the study safely.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Other
Study locations
China · 1 center
- Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern M — Guangzhou
Identifiers
NCT: NCT07447973 · U24A20707