Anterior Segment DiseasesNCT07447973
Multimodal Deep Learning Model for Multi-task Diagnosis and Triage Suggestions of Ophthalmic Diseases
An observational study of Anterior Segment Diseases, run by Guangdong Provincial People's Hospital at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Anterior Segment Diseases
- What is being tested?
- Multimodal Vision-language Model Diagnosis
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 10 Mar 2026to 31 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Individuals who have or do not have concerns related to their eyes.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
Guangzhou, Guangdong, 510280
Honghua Yu, +8618688888422, yuhonghua@gdph.org.cn
About this study
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
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
- Being tested
- Multimodal Vision-language Model Diagnosis
- Keywords
- Anterior Segment Diseases, Artificial Intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Multimodal Vision-language Model DiagnosisDiagnostic test
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.
| Study group | Role | What they receive |
|---|---|---|
| Normal participants | Healthy individuals who have no concerns related to their eyes. Receives: Diagnostic Test: Multimodal Vision-language Model Diagnosis | |
| 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. Receives: Diagnostic Test: Multimodal Vision-language Model Diagnosis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy of multimodal vision-language model.
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.
Measured: from July 2025 to September 2025
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Mar 2026
- Study start10 Mar 2026
- Primary completion20 Nov 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Anterior Segment DiseasesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Guangdong Provincial People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07447973
- Sponsor's protocol ID
- U24A20707
- Status last verified
- Jul 2026
- First submitted
- 25 Feb 2026
- Last updated
- 7 Jul 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 7 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT07447973
- Acronym / sponsor ID
- U24A20707
- Conditions
- Anterior Segment Diseases
- Keywords
- Anterior Segment Diseases, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Guangdong Provincial People's HospitalUniversity, hospital or other
- Study start
- 10 Mar 2026 (actual)
- Primary completion
- 20 Nov 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 4 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Normal participants | – | Diagnostic Test: Multimodal Vision-language Model DiagnosisHealthy individuals who have no concerns related to their eyes. |
| Patients with Eye-related Chief Complaints | – | Diagnostic Test: Multimodal Vision-language Model DiagnosisIndividuals 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Individuals who have or do not have concerns related to their eyes.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Multimodal Vision-language Model DiagnosisMultimodal 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. | Diagnostic test | Normal participants, Patients with Eye-related Chief Complaints |
Primary outcome
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Jul 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Anterior Segment Diseases, run by Guangdong Provincial People's Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 7 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Honghua Yu (+8618688888422, yuhonghua@gdph.org.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic accuracy of multimodal vision-language model. (time frame: from July 2025 to September 2025). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Jul 2026; AzTrials last refreshed its copy on 27 Oct 2026.