Multimodal Deep Learning Model for Multi-task Diagnosis and Triage Suggestions of Ophthalmic Diseases
Development and Validation of Multimodal Deep Learning Model for Autonomous Diagnosis, Generative Reporting, and Specialist Referral in Ophthalmic Diseases: An International Multicenter Cohort Study
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Honghua Yu
- Phone Number: +8618688888422
- Email: yuhonghua@gdph.org.cn
Study Locations
-
-
Guangdong
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Guangzhou, Guangdong, China, 510280
- Recruiting
- Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
-
Contact:
- Honghua Yu
- Phone Number: +8618688888422
- Email: yuhonghua@gdph.org.cn
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
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.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Normal participants
Healthy individuals who have no concerns related to their eyes.
|
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.
|
|
Patients with Eye-related Chief Complaints
Individuals who have specific concerns or issues related to their eyes, which they consider as the main reason for seeking medical attention or making a complaint.
|
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.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Diagnostic accuracy of multimodal vision-language model.
Time Frame: from July 2025 to September 2025
|
For each patient, the diagnoses generated by the multimodal vision-language model and the clinical diagnosis provided by skilled clinicians were documented and compared.
Consistency between the two diagnoses indicates the program's precision in clinical practice.
|
from July 2025 to September 2025
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
Other Study ID Numbers
- U24A20707
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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