Health TrialsbyHealthPanel

Ophthalmic AI-Assisted Medical Decision-Making

ConditionOcular Diseases
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
AgeNot listed
SponsorThe Eye Hospital of Wenzhou Medical University

About this trial

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.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

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.

Disqualifiers

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.

Trial design

Design model

Parallel

Treatments tested in this trial

  • AI-associated strategy

    Combination 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.

Treatment groups

100,000 Participants
are divided into 2 treatment groups
Group A: AI-assisted medical decision-makingExperimental treatment 1 intervention
Group B: Traditional medical decision-makingNo intervention 0 interventions

Trial outcomes

Primary outcomes

1

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).

Time frame
2 years
2

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).

Time frame
2 years
3

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).

Time frame
2 years
4

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).

Time frame
2 years

Secondary outcomes

1

System Usability Score

Evaluated using the System Usability Scale (SUS), with scores ranging from 0-100.

Time frame
2 years
2

AI System Response Time

Average time (seconds) taken for the AI to provide recommendations after data input.

Time frame
2 years
3

System Failure Rate

Frequency of AI system failures, measured as failures per thousand hours of use (failures/thousand hours).

Time frame
2 years
4

User Interface Design Satisfaction

Evaluated using the User Experience Questionnaire (UEQ), with scores ranging from 1-7.

Time frame
2 years

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

The Eye Hospital of Wenzhou Medical University

Lead sponsor

First Affiliated Hospital of Wenzhou Medical University

Collaborator

Second Affiliated Hospital of Wenzhou Medical University

Collaborator

ZhuHai Hospital

Collaborator

Macau University of Science and Technology Hospital

Collaborator

Source ClinicalTrials.gov
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