Eye DiseasesNCT05930444
Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases
An observational study of Eye Diseases, run by Eye & ENT Hospital of Fudan University at 3 sites in China.
Trial summary
With rapid advancements in natural language processing and image processing, there is a growing potential for intelligent diagnosis utilizing chatGPT trained through high-quality ophthalmic consultation. Read more about this study
- Who is it for?
- Ages 2 months and olderHealthy volunteers can take part
- Conditions the study is for
- Eye Diseases
- What is being tested?
- Multimodal Machine Learning Program for Auxiliary Diagnosis of Eye Diseases
- How many people
- 9,825 participants
- Where
- 3 sitesChina
- Study dates
- 21 Jul 2023to 31 Mar 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
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 2 months and older
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: The "normal participants" refers to individuals with no concerns or issues related to their eyes. The "participants with eye-related chief complaints" refers to patients from various eye clinics across China. Each participant must undergo comprehensive medical tests and have their medical records reviewed for diagnosis.
Who may be able to join
- Informed consent obtained;
- Participants should be able to have Chinese as their mother tongue, and be sufficiently able to read, write and understand Chinese;
- 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
3 sites in 1 country.
China3 sites
The Affiliated Eye Hospital of Nanjing Medical University
Nanjing
Fudan Eye & ENT Hospital
Shanghai
Suqian First People's Hospital
Suqian
About this study
Official title: Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases Using ChatGPT-based Natural Language Processing and Image Processing Techniques
With rapid advancements in natural language processing and image processing, there is a growing potential for intelligent diagnosis utilizing chatGPT trained through high-quality ophthalmic consultation. Furthermore, by incorporating patient selfies, eye examination photos, and other image analysis techniques, the diagnostic capabilities can be further enhanced. The multi-center study aims to develop an auxiliary diagnostic program for eye diseases using multimodal machine learning techniques and evaluate its diagnostic efficacy in real-world outpatient clinics.
- Conditions
- Eye Diseases
- Being tested
- Multimodal Machine Learning Program for Auxiliary Diagnosis of Eye Diseases
- Medical subject headings
- Eye Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Multimodal Machine Learning Program for Auxiliary Diagnosis of Eye DiseasesDiagnostic test
Patients presenting with eye-related chief complaints initially complete a mobile phone application. This application utilizes patient medical history and relevant images (such as selfies and photos from eye examinations) to provide intelligent diagnosis. The diagnosis remains undisclosed to the patients. Subsequently, patients seek medical attention and undergo clinical examination by a skilled clinician. The clinical diagnosis is subsequently reviewed by a second experienced clinician. If the diagnoses align, it is considered the gold standard. In cases of discrepancy, the consensus reached by the two clinicians becomes the gold standard.
| Study group | Role | What they receive |
|---|---|---|
| Normal participants | Healthy individuals who have no concerns related to their eyes. | |
| 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 Machine Learning Program for Auxiliary Diagnosis of Eye Diseases |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy of multimodal machine learning program
For each patient, the diagnoses generated by the multimodal machine learning program 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 2023 to March 2024
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Jul 2023
- Study start21 Jul 2023
- Primary completion10 Mar 2024
- Study completion31 Mar 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Eye DiseasesStudies of the same condition, open and under way.
- More trials run by Eye & ENT Hospital of Fudan UniversityThe sponsor's other registered studies.
- 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
- 9,825 participants (actual)
Sponsor and investigators
- Lead sponsor
- Eye & ENT Hospital of Fudan University
- Sponsor type
- University, hospital or other
- Collaborators
- The Affiliated Eye Hospital of Nanjing Medical University, Suqian First Hospital
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05930444
- Sponsor's protocol ID
- FD-EENT-20230625
- Status last verified
- Nov 2024
- First submitted
- 25 Jun 2023
- Last updated
- 15 Nov 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes
Publications
Ma R, Cheng Q, Yao J, Peng Z, Yan M, Lu J, Liao J, Tian L, Shu W, Zhang Y, Wang J, Jiang P, Xia W, Li X, Gan L, Zhao Y, Zhu J, Qin B, Jiang Q, Wang X, Lin X, Chen H, Zhu W, Xiang D, Nie B, Wang J, Guo J, Xue K, Cui H, Cheng J, Zhu X, Hong J, Shi F, Zhang R, Chen X, Zhao C. Multimodal machine learning enables AI chatbot to diagnose ophthalmic diseases and provide high-quality medical responses. NPJ Digit Med. 2025 Jan 27;8(1):64. doi: 10.1038/s41746-025-01461-0.
PubMed 39870855Peng Z, Ma R, Zhang Y, Yan M, Lu J, Cheng Q, Liao J, Zhang Y, Wang J, Zhao Y, Zhu J, Qin B, Jiang Q, Shi F, Qian J, Chen X, Zhao C. Development and evaluation of multimodal AI for diagnosis and triage of ophthalmic diseases using ChatGPT and anterior segment images: protocol for a two-stage cross-sectional study. Front Artif Intell. 2023 Dec 8;6:1323924. doi: 10.3389/frai.2023.1323924. eCollection 2023.
PubMed 38145231
Study at a glance
From the registry record, last updated by the sponsor on 15 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases Using ChatGPT-based Natural Language Processing and Image Processing Techniques
- ClinicalTrials.gov ID
- NCT05930444
- Acronym / sponsor ID
- FD-EENT-20230625
- Conditions
- Eye Diseases
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 9,825 participants (actual)
- Lead sponsor
- Eye & ENT Hospital of Fudan UniversityUniversity, hospital or other
- Collaborators
- The Affiliated Eye Hospital of Nanjing Medical University, Suqian First Hospital
- Study start
- 21 Jul 2023 (actual)
- Primary completion
- 10 Mar 2024 (actual)
- Study completion
- 31 Mar 2024 (actual)
- First posted
- 5 Jul 2023 (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.
With rapid advancements in natural language processing and image processing, there is a growing potential for intelligent diagnosis utilizing chatGPT trained through high-quality ophthalmic consultation. Furthermore, by incorporating patient selfies, eye examination photos, and other image analysis techniques, the diagnostic capabilities can be further enhanced. The multi-center study aims to develop an auxiliary diagnostic program for eye diseases using multimodal machine learning techniques and evaluate its diagnostic efficacy in real-world outpatient clinics.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Normal participants | – | –Healthy individuals who have no concerns related to their eyes. |
| Patients with Eye-related Chief Complaints | – | Diagnostic Test: Multimodal Machine Learning Program for Auxiliary Diagnosis of Eye DiseasesIndividuals 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
- 2 months and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The "normal participants" refers to individuals with no concerns or issues related to their eyes. The "participants with eye-related chief complaints" refers to patients from various eye clinics across China. Each participant must undergo comprehensive medical tests and have their medical records reviewed for diagnosis.
Key inclusion criteria (4 listed)
- Informed consent obtained;
- Participants should be able to have Chinese as their mother tongue, and be sufficiently able to read, write and understand Chinese;
- 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 Machine Learning Program for Auxiliary Diagnosis of Eye DiseasesPatients presenting with eye-related chief complaints initially complete a mobile phone application. This application utilizes patient medical history and relevant images (such as selfies and photos from eye examinations) to provide intelligent diagnosis. The diagnosis remains undisclosed to the patients. Subsequently, patients seek medical attention and undergo clinical examination by a skilled clinician. The clinical diagnosis is subsequently reviewed by a second experienced clinician. If the diagnoses align, it is considered the gold standard. In cases of discrepancy, the consensus reached by the two clinicians becomes the gold standard. | Diagnostic test | Patients with Eye-related Chief Complaints |
Primary outcome
Diagnostic accuracy of multimodal machine learning program
Time frame: from July 2023 to March 2024
For each patient, the diagnoses generated by the multimodal machine learning program 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
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Nov 2024.
- Derived from this study: PubMed 39870855Ma R, Cheng Q, Yao J, Peng Z, Yan M, Lu J, Liao J, Tian L, Shu W, Zhang Y, Wang J, Jiang P, Xia W, Li X, Gan L, Zhao Y, Zhu J, Qin B, Jiang Q, Wang X, Lin X, Chen H, Zhu W, Xiang D, Nie B, Wang J, Guo J, Xue K, Cui H, Cheng J, Zhu X, Hong J, Shi F, Zhang R, Chen X, Zhao C. Multimodal machine learning enables AI chatbot to diagnose ophthalmic diseases and provide high-quality medical responses. NPJ Digit Med. 2025 Jan 27;8(1):64. doi: 10.1038/s41746-025-01461-0.
- Derived from this study: PubMed 38145231Peng Z, Ma R, Zhang Y, Yan M, Lu J, Cheng Q, Liao J, Zhang Y, Wang J, Zhao Y, Zhu J, Qin B, Jiang Q, Shi F, Qian J, Chen X, Zhao C. Development and evaluation of multimodal AI for diagnosis and triage of ophthalmic diseases using ChatGPT and anterior segment images: protocol for a two-stage cross-sectional study. Front Artif Intell. 2023 Dec 8;6:1323924. doi: 10.3389/frai.2023.1323924. eCollection 2023.
- Development and evaluation of multimodal AI for diagnosis and triage of ophthalmic diseases using ChatGPT and anterior segment images: protocol for a two-stage cross-sectional studyLink provided by the sponsor.
- Individual participant dataSharing planned: Yes
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Eye Diseases, run by Eye & ENT Hospital of Fudan University at 3 sites 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: finished, as of the sponsor's last update on 15 Nov 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic accuracy of multimodal machine learning program (time frame: from July 2023 to March 2024). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05930444, which also lists 2 linked publications.
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 15 Nov 2024.