Ophthalmic Multimodal AI-Assisted Medical Decision-Making

NCT06755190 | Recruiting DMC

• 1 other identifier: Ophthalmic Multimodal AI
• Study Type: observational
• Target: 5,000,000
• Locations: 2 countries
• Sites: 5

Brief 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.

Conditions

Ocular Diseases

Keywords

ocular diseases; Ophthalmic Multimodal AI-Assisted Medical Decision-Making; Artificial Intelligence

Outcome Measures

Primary Outcomes (11)

• 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).

  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).

  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).

  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).

  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).

  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).

  1 years

• Postoperative Complication Rate

  Percentage (%) of patients experiencing postoperative complications.

  1 years

• Recurrence Risk Rate

  Percentage (%) of patients experiencing recurrence during the follow-up period.

  1 years

• Survival Rate

  Percentage (%) of patients alive, calculated using Kaplan-Meier survival curves.

  1 years

• Effectiveness of Decision Support

  Percentage (%) improvement in the accuracy of treatment decisions with AI assistance compared to traditional decisions.

  1 years

• Decision Time Efficiency

  Average time (seconds) required for physicians to make diagnostic and treatment decisions, before and after AI assistance.

  1 years

Secondary Outcomes (7)

• System Usability Score

  1 years

• AI System Response Time

  1 years

• System Failure Rate

  1 years

• User Interface Design Satisfaction

  1 years

• Patient Satisfaction Score

  1 years

Study Arms (2)

normal

patients who do not have the ocular diseases

ocular diseases

patients who have ocular diseases

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

Interventions

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.

ocular diseases

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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.
• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• The Eye Hospital of Wenzhou Medical University: lead

Study Sites (5)

ZhuHai Hospital, zhuhai, guangdong

Zhuhai, Guangdong, China

RECRUITING

First Affiliated Hospital of Wenzhou Medical University

Wenzhou, Zhejiang, China

RECRUITING

Second Affiliated Hospital of Wenzhou Medical Universit

Wenzhou, Zhejiang, China

RECRUITING

The Eye Hospital of Wenzhou Medical University

Wenzhou, Zhejiang, China

RECRUITING

Macau University of Science and Technology Hospital

Macao, Macau, Macau

RECRUITING

Study Officials

• Kang Zhang, PhD.

  The Eye Hospital of Wenzhou Medical University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Lan Wang, MD

CONTACT

+86-0577-85397527; wl2832300533@163.com

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief Scientist

Study Record Dates

• First Submitted: December 15, 2024
• First Posted: January 1, 2025
• Study Start: December 20, 2024
• Primary Completion: May 1, 2025
• Study Completion: May 1, 2025
• Last Updated: April 17, 2025

Record last verified: 2025-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (4); MA (1)