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Multimodal Deep Learning Model for Multi-task Diagnosis and Triage Suggestions of Ophthalmic Diseases

NCT07447973 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 2000

Last updated 2026-03-05

No results posted yet for this study

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.

Conditions

  • 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 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.

Sponsors & Collaborators

  • Guangdong Provincial People's Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-07-28
Primary Completion
2027-11-20
Completion
2027-12-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07447973 on ClinicalTrials.gov