Ocular DiseasesNCT06755060
Ophthalmic AI-Assisted Medical Decision-Making
A study testing AI-associated strategy in people with Ocular Diseases, run by The Eye Hospital of Wenzhou Medical University at 5 sites in 2 countries.
Trial summary
This is a multi-center, prospective 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 ageHealthy volunteers can take part
- Conditions the study is for
- Ocular Diseases
- What is being tested?
- AI-associated strategy
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100,000 participants
- Where
- 5 sitesin 2 countries
- Study dates
- 1 Dec 2024to 30 Dec 2026
- Phase
- Interventional
- 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
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- Age Criteria: No age restrictions apply for inclusion in the study.
- Ophthalmic Disease Diagnosis: Eligible patients must have a diagnosis of one or more ophthalmic conditions, with the diagnosis confirmed by a qualified ophthalmologist.
- Imaging and Clinical Data Requirements: Patients must be able to provide complete ophthalmic imaging data and electronic medical records (EMR) that are comprehensive and accessible for the purposes of the study.
- Informed Consent: All patients, or their legal representatives in the case of minors or individuals unable to provide informed consent, must sign a consent form that clearly outlines the study's objectives, procedures, potential risks and discomforts, data usage, and the rights and responsibilities of participants. In the case of minors or those unable to consent, informed consent must be obtained from the patient's legal guardian.
- Treatment Adherence: Participants must demonstrate the ability to understand and adhere to the study's requirements, including compliance with follow-up visits, examination schedules, and treatment recommendations. Patients must agree to participate in regular assessments and data collection, including imaging exams, laboratory tests, and follow-up evaluations as required by the study protocol.
- Clinical Physician Assessment: The attending physician must determine that the patient meets all inclusion criteria and has the capacity to comply with the necessary treatment, diagnostic tests, and follow-up protocols throughout the study duration.
Who cannot join
- Acute or Severe Ocular Diseases: Patients with acute ocular conditions requiring immediate medical intervention, which necessitate exclusion from interventional studies due to the urgency of their treatment.
- Serious Systemic Diseases: Patients with serious systemic illnesses that may interfere with the treatment of ocular diseases, impact the effectiveness of the intervention, or complicate the interpretation of study outcomes.
- Prior Exposure to Study Interventions: Patients who have previously undergone the intervention being studied or participated in other experimental treatments within ongoing clinical trials, as this may introduce bias or confound the study results.
- Incomplete Imaging or Clinical Data: Patients who are unable to provide complete or adequate ophthalmic imaging data or lack a comprehensive electronic medical record (EMR), which are essential for the integrity of the study data.
- Pregnancy or Lactation: Pregnant or breastfeeding women, for whom there may be potential risks associated with ocular treatment or imaging procedures. Such cases will be evaluated on an individual basis to ensure patient safety.
- Mental Health or Cognitive Impairment: Patients diagnosed with significant mental health disorders or cognitive impairments that prevent them from fully understanding the nature and risks of the study, or from complying with the treatment regimen and follow-up procedures.
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- Drug Allergies or Severe Reactions: Patients with known allergies or severe adverse reactions to any medications or ophthalmic treatments likely to be used during the study, which could pose a health risk to the patient.
- Current Participation in Other Clinical Trials: Patients who are concurrently involved in other interventional clinical trials (especially those related to ophthalmology), as this may lead to conflicting treatments or interfere with the assessment of the study's outcomes.
- Inability to Comply with Follow-up Requirements: Patients who, due to logistical, health-related, or personal factors, are unable to comply with the required follow-up visits, treatment regimens, or data collection, which are essential for the study's longitudinal analysis.
- Other Clinical Exclusions: Patients whose participation, based on the clinical judgment of the treating physician, may not be in their best interest due to their health condition or other factors, or who may experience adverse outcomes from participating in the study.
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, 325000
Cheng Tang, MD, +86-0577-55579999, c249325687@163.com
- Recruiting
Second Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang, 325000
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
Kang Zhang, PhD.
- Recruiting
ZhuHai Hospital
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
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, prospective 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
- AI-associated strategy
- 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.
AI-associated strategyCombination product
The intervention in this study involves an AI system that leverages multimodal data fusion to support the clinical decision-making and evaluation of ophthalmic diseases. Patients in the intervention group will undergo standard ophthalmic examinations, with clinical decisions guided by the recommendations generated by the AI system. In contrast, patients in the control group will receive only standard ophthalmic examinations and treatment, without the support of AI-assisted decision-making tools.
| Study group | Role | What they receive |
|---|---|---|
| AI-assisted medical decision-making | Experimental | Patients in the intervention group will receive AI-assisted medical decision-making based on multimodal data. Receives: Combination Product: AI-associated strategy |
| Traditional medical decision-making | No intervention | Patients in the control group will receive traditional medical decision-making, where treatment and diagnostic decisions are made solely by the attending physician based on clinical judgment, without the support of AI-assisted system. |
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: 2 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: 2 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: 2 years
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: 2 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: 2 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: 2 years
Postoperative Complication Rate
Percentage (%) of patients experiencing postoperative complications.
Measured: 2 years
Recurrence Risk Rate
Percentage (%) of patients experiencing recurrence during the follow-up period.
Measured: 2 years
Survival Rate
Percentage (%) of patients alive, calculated using Kaplan-Meier survival curves.
Measured: 2 years
Secondary outcomes (9)
System Usability Score
Evaluated using the System Usability Scale (SUS), with scores ranging from 0-100.
Measured: 2 years
AI System Response Time
Average time (seconds) taken for the AI to provide recommendations after data input.
Measured: 2 years
System Failure Rate
Frequency of AI system failures, measured as failures per thousand hours of use (failures/thousand hours).
Measured: 2 years
User Interface Design Satisfaction
Evaluated using the User Experience Questionnaire (UEQ), with scores ranging from 1-7.
Measured: 2 years
Patient Satisfaction Score
Measured using the Patient Satisfaction Questionnaire (CSQ-8), with scores ranging from 8-32.
Measured: 2 years
Treatment Adherence
Percentage (%) of patients adhering to personalized treatment plans and regular follow-up visits.
Measured: 2 years
Physician Acceptance of AI System
Evaluated using the Technology Acceptance Model (TAM) scale, with scores ranging from 1-7.
Measured: 2 years
Effectiveness of Decision Support
Percentage (%) improvement in the accuracy of treatment decisions with AI assistance compared to traditional decisions.
Measured: 2 years
Decision Time Efficiency
Average time (seconds) required for physicians to make diagnostic and treatment decisions, before and after AI assistance.
Measured: 2 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 start1 Dec 2024
- Primary completion30 Dec 2026 (estimated)
- Study completion30 Dec 2026 (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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 100,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The Eye Hospital of Wenzhou Medical University
- Sponsor type
- University, hospital or other
- Collaborators
- First Affiliated Hospital of Wenzhou Medical University, Second Affiliated Hospital of Wenzhou Medical University, ZhuHai Hospital, Macau University of Science and Technology Hospital
- Responsible party
- Principal investigator, Kang Zhang, The Eye Hospital of Wenzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06755060
- Sponsor's protocol ID
- Ophthalmic AI
- Status last verified
- Aug 2025
- First submitted
- 9 Dec 2024
- Last updated
- 20 Aug 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 20 Aug 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
- NCT06755060
- Acronym / sponsor ID
- Ophthalmic AI
- Conditions
- Ocular Diseases
- Keywords
- ocular diseases, Ophthalmic Multimodal AI-Assisted Medical Decision-Making, Artificial Intelligence
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 100,000 participants (estimated)
- Lead sponsor
- The Eye Hospital of Wenzhou Medical UniversityUniversity, hospital or other
- Collaborators
- First Affiliated Hospital of Wenzhou Medical University, Second Affiliated Hospital of Wenzhou Medical University, ZhuHai Hospital, Macau University of Science and Technology Hospital
- Study start
- 1 Dec 2024 (actual)
- Primary completion
- 30 Dec 2026 (estimated)
- Study completion
- 30 Dec 2026 (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, prospective 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
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-assisted medical decision-making | Experimental | Combination Product: AI-associated strategyPatients in the intervention group will receive AI-assisted medical decision-making based on multimodal data. |
| Traditional medical decision-making | No intervention | –Patients in the control group will receive traditional medical decision-making, where treatment and diagnostic decisions are made solely by the attending physician based on clinical judgment, without the support of AI-assisted system. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (6 listed)
- Age Criteria: No age restrictions apply for inclusion in the study.
- Ophthalmic Disease Diagnosis: Eligible patients must have a diagnosis of one or more ophthalmic conditions, with the diagnosis confirmed by a qualified ophthalmologist.
- Imaging and Clinical Data Requirements: Patients must be able to provide complete ophthalmic imaging data and electronic medical records (EMR) that are comprehensive and accessible for the purposes of the study.
- Informed Consent: All patients, or their legal representatives in the case of minors or individuals unable to provide informed consent, must sign a consent form that clearly outlines the study's objectives, procedures, potential risks and discomforts, data usage, and the rights and responsibilities of participants. In the case of minors or those unable to consent, informed consent must be obtained from the patient's legal guardian.
- Treatment Adherence: Participants must demonstrate the ability to understand and adhere to the study's requirements, including compliance with follow-up visits, examination schedules, and treatment recommendations. Patients must agree to participate in regular assessments and data collection, including imaging exams, laboratory tests, and follow-up evaluations as required by the study protocol.
Key exclusion criteria (10 listed)
- Acute or Severe Ocular Diseases: Patients with acute ocular conditions requiring immediate medical intervention, which necessitate exclusion from interventional studies due to the urgency of their treatment.
- Serious Systemic Diseases: Patients with serious systemic illnesses that may interfere with the treatment of ocular diseases, impact the effectiveness of the intervention, or complicate the interpretation of study outcomes.
- Prior Exposure to Study Interventions: Patients who have previously undergone the intervention being studied or participated in other experimental treatments within ongoing clinical trials, as this may introduce bias or confound the study results.
- Incomplete Imaging or Clinical Data: Patients who are unable to provide complete or adequate ophthalmic imaging data or lack a comprehensive electronic medical record (EMR), which are essential for the integrity of the study data.
- Pregnancy or Lactation: Pregnant or breastfeeding women, for whom there may be potential risks associated with ocular treatment or imaging procedures. Such cases will be evaluated on an individual basis to ensure patient safety.
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 |
|---|---|---|
| AI-associated strategyThe intervention in this study involves an AI system that leverages multimodal data fusion to support the clinical decision-making and evaluation of ophthalmic diseases. Patients in the intervention group will undergo standard ophthalmic examinations, with clinical decisions guided by the recommendations generated by the AI system. In contrast, patients in the control group will receive only standard ophthalmic examinations and treatment, without the support of AI-assisted decision-making tools. | Combination product | AI-assisted medical decision-making |
Primary outcome
Area Under the Curve (AUC)
Time frame: 2 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: 2 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).
Specificity
Time frame: 2 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).
Accuracy
Time frame: 2 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).
False Positive Rate
Time frame: 2 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: 2 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: 2 years
Percentage (%) of patients experiencing postoperative complications.
Recurrence Risk Rate
Time frame: 2 years
Percentage (%) of patients experiencing recurrence during the follow-up period.
Survival Rate
Time frame: 2 years
Percentage (%) of patients alive, calculated using Kaplan-Meier survival curves.
9 secondary outcomes
System Usability Score
Time frame: 2 years
AI System Response Time
Time frame: 2 years
System Failure Rate
Time frame: 2 years
User Interface Design Satisfaction
Time frame: 2 years
Patient Satisfaction Score
Time frame: 2 years
Treatment Adherence
Time frame: 2 years
Physician Acceptance of AI System
Time frame: 2 years
Effectiveness of Decision Support
Time frame: 2 years
Decision Time Efficiency
Time frame: 2 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 20 Aug 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.
5 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-associated strategy in people with 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 10 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 20 Aug 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: 2 years). The study lists 9 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 20 Aug 2025.