Health TrialsbyHealthPanel

Ophthalmic Multimodal AI-Assisted Medical Decision-Making

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

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

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.

Disqualifiers

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 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.

Trial design

Design model

Case-only

Time perspective

Retrospective

Treatments tested in this trial

  • Diagnostic Test: AI-Based Diagnostic and Prognostic Model for Ocular Diseases

    Diagnostic 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.

Treatment groups

5,000,000 Participants
are divided into 2 treatment groups
Group A: normal0 interventions
Group B: ocular diseases1 intervention

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
1 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
1 years
3

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).

Time frame
1 years
4

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
1 years

Secondary outcomes

1

System Usability Score

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

Time frame
1 years
2

AI System Response Time

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

Time frame
1 years
3

System Failure Rate

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

Time frame
1 years
4

User Interface Design Satisfaction

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

Time frame
1 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

Source ClinicalTrials.gov
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