Ocular DiseasesNCT06755190
Ophthalmic Multimodal AI-Assisted Medical Decision-Making
An observational study of Ocular Diseases, run by The Eye Hospital of Wenzhou Medical University at 5 sites in 2 countries.
Trial summary
This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted medical decision support system, leveraging multimodal data fusion, in ophthalmic clinical practice. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Ocular Diseases
- What is being tested?
- Diagnostic Test: AI-Based Diagnostic and Prognostic Model for Ocular Diseases
- How many people
- 5,000,000 participants
- Where
- 5 sitesin 2 countries
- Study dates
- 20 Dec 2024to May 2025
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: All patients who have received treatment at multiple centers, including The Eye Hospital of Wenzhou Medical University, First Affiliated Hospital of Wenzhou Medical University, Second Affiliated Hospital of Wenzhou Medical University, ZhuHai Hospital, and Macau University of Science and Technology Hospital.
Who may be able to join
- 1.All patients who have received treatment at multiple centers, including The Eye Hospital of Wenzhou Medical University, First Affiliated Hospital of Wenzhou Medical University, Second Affiliated Hospital of Wenzhou Medical University, ZhuHai Hospital, and Macau University of Science and Technology Hospital.
- 2.Availability of comprehensive electronic health records (EHR), including: Ophthalmic images (e.g., fundus photography, OCT, or slit-lamp images). Electronic medical records (e.g., diagnosis, treatment, and follow-up notes). Examination results (e.g., visual acuity, intraocular pressure, or laboratory tests). 3.Patients with a clear and confirmed diagnosis of one or more ocular diseases. 4.Patients with sufficient follow-up records to allow assessment of disease progression or prognosis, if applicable.
- All ophthalmology patients who have previously received treatment at the Department of Ophthalmology, the Eye Hospital of Wenzhou Medical University, First Affiliated Hospital of Wenzhou Medical University, Second Affiliated Hospital of Wenzhou Medical University, Zhuhai People's Hospital, and the University Hospital.
- Availability of comprehensive electronic health records (EHR), including: Ophthalmic images (e.g., fundus photography, OCT, or slit-lamp images). Electronic medical records (e.g., diagnosis, treatment, and follow-up notes). Examination results (e.g., visual acuity, intraocular pressure, or laboratory tests).
- Patients with a clear and confirmed diagnosis of one or more ocular diseases.
- Patients with sufficient follow-up records to allow assessment of disease progression or prognosis, if applicable.
Who cannot join
- Incomplete or missing critical EHR components.
- Cases with ambiguous or unverified diagnoses that cannot be clearly categorized.
- Duplicated or redundant data from the same patient.
Trial locations and contacts
5 sites in 2 countries. Contact the study team or a site near you to ask about taking part.
5 sites in 2 countries
China4 sites, 4 recruiting
- Recruiting
First Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Cheng Tang, MD, +86-0577-55579999, c249325687@163.com
- Recruiting
Second Affiliated Hospital of Wenzhou Medical Universit
Wenzhou, Zhejiang
Sian Liu, PhD., +86-0577-88002888, liusan@mail3.sysu.edu.cn
- Recruiting
The Eye Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Lan Wang, MD, +86-0577-85397527, wl2832300533@163.com
- Recruiting
ZhuHai Hospital, zhuhai, guangdong
Zhuhai, Guangdong
Bingzhou Li, +86-0756-2222569, mr_jerry_99@163.com
Macau1 site, 1 recruiting
- Recruiting
Macau University of Science and Technology Hospital
Macao, Macau
Yang Liu, MD, +853-2882-1838, liuyang.macau@gmail.com
About this study
Official title: A Study on Ophthalmic Multimodal AI-Assisted Medical Decision-Making Based on Imaging and Electronic Medical Record Data
This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted medical decision support system, leveraging multimodal data fusion, in ophthalmic clinical practice.
Read the detailed description
Visual impairments significantly affect an individual's quality of life. Early screening, diagnosis, and treatment of ocular diseases are crucial for preventing the onset and progression of vision disorders. In clinical practice, ophthalmologists often need to integrate a wide range of patient data, including demographic information, medical history, biochemical markers such as blood glucose and lipid levels, risk factors, as well as various ophthalmic data, such as fundus images, OCT scans, and visual field tests, to make an accurate diagnosis and develop an appropriate treatment plan. In an era where precision and personalized medicine are at the forefront of healthcare, the early detection and diagnosis of eye diseases, as well as the selection of suitable diagnostic and therapeutic strategies at different stages of the disease, have become significant challenges in clinical settings. Recent advancements in medical imaging and analysis techniques have greatly enhanced the accuracy and effectiveness of ocular disease diagnosis. This study aims to develop an ophthalmic artificial intelligence-assisted decision-making system by integrating multimodal data from imaging and electronic medical records, in combination with deep learning techniques. The objective is to improve diagnostic accuracy, streamline clinical workflows, and provide more personalized treatment options for patients. Ultimately, this system seeks to enhance treatment outcomes and improve the overall quality of life for patients suffering from ocular diseases.
- Conditions
- Ocular Diseases
- Being tested
- Diagnostic Test: AI-Based Diagnostic and Prognostic Model for Ocular Diseases
- Keywords
- ocular diseases, Ophthalmic Multimodal AI-Assisted Medical Decision-Making, Artificial Intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Diagnostic Test: AI-Based Diagnostic and Prognostic Model for Ocular DiseasesDiagnostic test
This intervention involves an AI system that leverages multimodal data fusion to support the clinical decision-making and evaluation of ophthalmic diseases. It integrates multi-modal data, including fundus photography, optical coherence tomography (OCT), and patient clinical records, to provide real-time, precise, and personalized diagnostic support. Unlike other models, this system utilizes a longitudinal patient dataset to predict disease progression and treatment outcomes.Key distinguishing features include: 1. Multi-Modal Data Integration: Combines imaging, clinical, and genetic data for comprehensive analysis. 2. Predictive Capability: Offers advanced prognostic predictions, enabling personalized treatment plans. 3. Deep Learning Framework: Employs state-of-the-art deep learning algorithms for improved diagnostic accuracy and efficiency. 4. Real-World Validation: Validated using a large cohort of diverse patient data, ensuring generalizability and robustness.
| Study group | Role | What they receive |
|---|---|---|
| normal | patients who do not have the ocular diseases | |
| ocular diseases | patients who have ocular diseases Receives: Diagnostic Test: Diagnostic Test: AI-Based Diagnostic and Prognostic Model for Ocular Diseases |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the Curve (AUC)
AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1).
Measured: 1 years
Sensitivity
Sensitivity (also called True Positive Rate) is a measure of how well a model identifies positive instances. It is defined as the proportion of actual positive cases correctly identified by the model. No unit (a ratio or percentage, typically expressed as a percentage).
Measured: 1 years
Accuracy Accuracy Accuracy
Accuracy measures the proportion of all correct predictions (true positives and true negatives) out of the total number of cases evaluated by the model. No unit (a ratio or percentage, typically expressed as a percentage).
Measured: 1 years
Specificity
Specificity (also called True Negative Rate) measures the proportion of actual negative cases correctly identified by the model. No unit (a ratio or percentage, typically expressed as a percentage).
Measured: 1 years
False Positive Rate
False Positive Rate (FPR) measures the proportion of actual negative cases that are incorrectly identified as positive by the model. No unit (a ratio or percentage, typically expressed as a percentage).
Measured: 1 years
False Negative Rate
False Negative Rate (FNR) measures the proportion of actual positive cases that are incorrectly identified as negative by the model. No unit (a ratio or percentage, typically expressed as a percentage).
Measured: 1 years
Postoperative Complication Rate
Percentage (%) of patients experiencing postoperative complications.
Measured: 1 years
Recurrence Risk Rate
Percentage (%) of patients experiencing recurrence during the follow-up period.
Measured: 1 years
Survival Rate
Percentage (%) of patients alive, calculated using Kaplan-Meier survival curves.
Measured: 1 years
Effectiveness of Decision Support
Percentage (%) improvement in the accuracy of treatment decisions with AI assistance compared to traditional decisions.
Measured: 1 years
Decision Time Efficiency
Average time (seconds) required for physicians to make diagnostic and treatment decisions, before and after AI assistance.
Measured: 1 years
Secondary outcomes (7)
System Usability Score
Evaluated using the System Usability Scale (SUS), with scores ranging from 0-100.
Measured: 1 years
AI System Response Time
Average time (seconds) taken for the AI to provide recommendations after data input.
Measured: 1 years
System Failure Rate
Frequency of AI system failures, measured as failures per thousand hours of use (failures/thousand hours).
Measured: 1 years
User Interface Design Satisfaction
Evaluated using the User Experience Questionnaire (UEQ), with scores ranging from 1-7.
Measured: 1 years
Patient Satisfaction Score
Measured using the Patient Satisfaction Questionnaire (CSQ-8), with scores ranging from 8-32.
Measured: 1 years
Treatment Adherence
Percentage (%) of patients adhering to personalized treatment plans and regular follow-up visits.
Measured: 1 years
Physician Acceptance of AI System
Evaluated using the Technology Acceptance Model (TAM) scale, with scores ranging from 1-7.
Measured: 1 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Jan 2025
- Study start20 Dec 2024
- Primary completionMay 2025 (estimated)
- Study completionMay 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Ocular 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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 5,000,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The Eye Hospital of Wenzhou Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Kang Zhang, The Eye Hospital of Wenzhou Medical University
- Investigators
- Kang Zhang, PhD., Principal investigator, The Eye Hospital of Wenzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06755190
- Sponsor's protocol ID
- Ophthalmic Multimodal AI
- Status last verified
- Apr 2025
- First submitted
- 15 Dec 2024
- Last updated
- 17 Apr 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Study on Ophthalmic Multimodal AI-Assisted Medical Decision-Making Based on Imaging and Electronic Medical Record Data
- ClinicalTrials.gov ID
- NCT06755190
- Acronym / sponsor ID
- Ophthalmic Multimodal AI
- Conditions
- Ocular Diseases
- Keywords
- ocular diseases, Ophthalmic Multimodal AI-Assisted Medical Decision-Making, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 5,000,000 participants (estimated)
- Lead sponsor
- The Eye Hospital of Wenzhou Medical UniversityUniversity, hospital or other
- Study start
- 20 Dec 2024 (actual)
- Primary completion
- May 2025 (estimated)
- Study completion
- May 2025 (estimated)
- First posted
- 1 Jan 2025 (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.
This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted medical decision support system, leveraging multimodal data fusion, in ophthalmic clinical practice.
Detailed description
Visual impairments significantly affect an individual's quality of life. Early screening, diagnosis, and treatment of ocular diseases are crucial for preventing the onset and progression of vision disorders. In clinical practice, ophthalmologists often need to integrate a wide range of patient data, including demographic information, medical history, biochemical markers such as blood glucose and lipid levels, risk factors, as well as various ophthalmic data, such as fundus images, OCT scans, and visual field tests, to make an accurate diagnosis and develop an appropriate treatment plan. In an era where precision and personalized medicine are at the forefront of healthcare, the early detection and diagnosis of eye diseases, as well as the selection of suitable diagnostic and therapeutic strategies at different stages of the disease, have become significant challenges in clinical settings. Recent advancements in medical imaging and analysis techniques have greatly enhanced the accuracy and effectiveness of ocular disease diagnosis. This study aims to develop an ophthalmic artificial intelligence-assisted decision-making system by integrating multimodal data from imaging and electronic medical records, in combination with deep learning techniques. The objective is to improve diagnostic accuracy, streamline clinical workflows, and provide more personalized treatment options for patients. Ultimately, this system seeks to enhance treatment outcomes and improve the overall quality of life for patients suffering from ocular diseases.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| normal | – | –patients who do not have the ocular diseases |
| ocular diseases | – | Diagnostic Test: Diagnostic Test: AI-Based Diagnostic and Prognostic Model for Ocular Diseasespatients who have ocular diseases |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- All patients who have received treatment at multiple centers, including The Eye Hospital of Wenzhou Medical University, First Affiliated Hospital of Wenzhou Medical University, Second Affiliated Hospital of Wenzhou Medical University, ZhuHai Hospital, and Macau University of Science and Technology Hospital.
Key inclusion criteria (6 listed)
- 1.All patients who have received treatment at multiple centers, including The Eye Hospital of Wenzhou Medical University, First Affiliated Hospital of Wenzhou Medical University, Second Affiliated Hospital of Wenzhou Medical University, ZhuHai Hospital, and Macau University of Science and Technology Hospital.
- 2.Availability of comprehensive electronic health records (EHR), including: Ophthalmic images (e.g., fundus photography, OCT, or slit-lamp images). Electronic medical records (e.g., diagnosis, treatment, and follow-up notes). Examination results (e.g., visual acuity, intraocular pressure, or laboratory tests). 3.Patients with a clear and confirmed diagnosis of one or more ocular diseases. 4.Patients with sufficient follow-up records to allow assessment of disease progression or prognosis, if applicable.
- All ophthalmology patients who have previously received treatment at the Department of Ophthalmology, the Eye Hospital of Wenzhou Medical University, First Affiliated Hospital of Wenzhou Medical University, Second Affiliated Hospital of Wenzhou Medical University, Zhuhai People's Hospital, and the University Hospital.
- Availability of comprehensive electronic health records (EHR), including: Ophthalmic images (e.g., fundus photography, OCT, or slit-lamp images). Electronic medical records (e.g., diagnosis, treatment, and follow-up notes). Examination results (e.g., visual acuity, intraocular pressure, or laboratory tests).
- Patients with a clear and confirmed diagnosis of one or more ocular diseases.
Key exclusion criteria (3 listed)
- Incomplete or missing critical EHR components.
- Cases with ambiguous or unverified diagnoses that cannot be clearly categorized.
- Duplicated or redundant data from the same patient.
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 |
|---|---|---|
| Diagnostic Test: AI-Based Diagnostic and Prognostic Model for Ocular DiseasesThis intervention involves an AI system that leverages multimodal data fusion to support the clinical decision-making and evaluation of ophthalmic diseases. It integrates multi-modal data, including fundus photography, optical coherence tomography (OCT), and patient clinical records, to provide real-time, precise, and personalized diagnostic support. Unlike other models, this system utilizes a longitudinal patient dataset to predict disease progression and treatment outcomes.Key distinguishing features include: 1. Multi-Modal Data Integration: Combines imaging, clinical, and genetic data for comprehensive analysis. 2. Predictive Capability: Offers advanced prognostic predictions, enabling personalized treatment plans. 3. Deep Learning Framework: Employs state-of-the-art deep learning algorithms for improved diagnostic accuracy and efficiency. 4. Real-World Validation: Validated using a large cohort of diverse patient data, ensuring generalizability and robustness. | Diagnostic test | ocular diseases |
Primary outcome
Area Under the Curve (AUC)
Time frame: 1 years
AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1).
Sensitivity
Time frame: 1 years
Sensitivity (also called True Positive Rate) is a measure of how well a model identifies positive instances. It is defined as the proportion of actual positive cases correctly identified by the model. No unit (a ratio or percentage, typically expressed as a percentage).
Accuracy Accuracy Accuracy
Time frame: 1 years
Accuracy measures the proportion of all correct predictions (true positives and true negatives) out of the total number of cases evaluated by the model. No unit (a ratio or percentage, typically expressed as a percentage).
Specificity
Time frame: 1 years
Specificity (also called True Negative Rate) measures the proportion of actual negative cases correctly identified by the model. No unit (a ratio or percentage, typically expressed as a percentage).
False Positive Rate
Time frame: 1 years
False Positive Rate (FPR) measures the proportion of actual negative cases that are incorrectly identified as positive by the model. No unit (a ratio or percentage, typically expressed as a percentage).
False Negative Rate
Time frame: 1 years
False Negative Rate (FNR) measures the proportion of actual positive cases that are incorrectly identified as negative by the model. No unit (a ratio or percentage, typically expressed as a percentage).
Postoperative Complication Rate
Time frame: 1 years
Percentage (%) of patients experiencing postoperative complications.
Recurrence Risk Rate
Time frame: 1 years
Percentage (%) of patients experiencing recurrence during the follow-up period.
Survival Rate
Time frame: 1 years
Percentage (%) of patients alive, calculated using Kaplan-Meier survival curves.
Effectiveness of Decision Support
Time frame: 1 years
Percentage (%) improvement in the accuracy of treatment decisions with AI assistance compared to traditional decisions.
Decision Time Efficiency
Time frame: 1 years
Average time (seconds) required for physicians to make diagnostic and treatment decisions, before and after AI assistance.
7 secondary outcomes
System Usability Score
Time frame: 1 years
AI System Response Time
Time frame: 1 years
System Failure Rate
Time frame: 1 years
User Interface Design Satisfaction
Time frame: 1 years
Patient Satisfaction Score
Time frame: 1 years
Treatment Adherence
Time frame: 1 years
Physician Acceptance of AI System
Time frame: 1 years
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 17 Apr 2025.
- Individual participant dataSharing planned: No
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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.
Kang Zhang, PhD. principal investigator
The Eye Hospital of Wenzhou Medical University
5 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Ocular Diseases, run by The Eye Hospital of Wenzhou Medical University at 5 sites in 2 countries.
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 6 inclusion and 3 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 17 Apr 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in China, Macau. 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 Lan Wang, MD (+86-0577-85397527, wl2832300533@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Curve (AUC) (time frame: 1 years). The study lists 11 primary outcomes in all. 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 17 Apr 2025.