Ophthalmic Multimodal AI-Assisted Medical Decision-Making

Recruiting

• Conditions: Ocular Diseases

• Registration Number: NCT06755190
• Lead Sponsor: The Eye Hospital of Wenzhou Medical University

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.

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 Timeline

• Study First Submitted: 2024-12-15
• Study Start: 2024-12-20
• Study First Submitted QC: 2024-12-23
• Study First Posted: 2025-01-01
• Status Verified: 2025-04
• Last Update Submitted: 2025-04-16
• Last Update Posted: 2025-04-17
• Primary Completion: 2025-05
• Study Completion: 2025-05

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 5000000

Inclusion Criteria

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.

1. 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.
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.

Exclusion Criteria

1. Incomplete or missing critical EHR components.
2. Cases with ambiguous or unverified diagnoses that cannot be clearly categorized.
3. Duplicated or redundant data from the same patient.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Area Under the Curve (AUC)	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	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	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	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	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	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	1 years	Percentage (%) of patients experiencing postoperative complications.
Recurrence Risk Rate	1 years	Percentage (%) of patients experiencing recurrence during the follow-up period.
Survival Rate	1 years	Percentage (%) of patients alive, calculated using Kaplan-Meier survival curves.
Effectiveness of Decision Support	1 years	Percentage (%) improvement in the accuracy of treatment decisions with AI assistance compared to traditional decisions.
Decision Time Efficiency	1 years	Average time (seconds) required for physicians to make diagnostic and treatment decisions, before and after AI assistance.

Secondary Outcome Measures

Name	Time	Method
System Usability Score	1 years	Evaluated using the System Usability Scale (SUS), with scores ranging from 0-100.
AI System Response Time	1 years	Average time (seconds) taken for the AI to provide recommendations after data input.
System Failure Rate	1 years	Frequency of AI system failures, measured as failures per thousand hours of use (failures/thousand hours).
User Interface Design Satisfaction	1 years	Evaluated using the User Experience Questionnaire (UEQ), with scores ranging from 1-7.
Patient Satisfaction Score	1 years	Measured using the Patient Satisfaction Questionnaire (CSQ-8), with scores ranging from 8-32.
Treatment Adherence	1 years	Percentage (%) of patients adhering to personalized treatment plans and regular follow-up visits.
Physician Acceptance of AI System	1 years	Evaluated using the Technology Acceptance Model (TAM) scale, with scores ranging from 1-7.

Trial Locations

Locations (5)

ZhuHai Hospital, zhuhai, guangdong; 🇨🇳 Zhuhai, Guangdong, China

First Affiliated Hospital of Wenzhou Medical University; 🇨🇳 Wenzhou, Zhejiang, China

Second Affiliated Hospital of Wenzhou Medical Universit; 🇨🇳 Wenzhou, Zhejiang, China

The Eye Hospital of Wenzhou Medical University; 🇨🇳 Wenzhou, Zhejiang, China

Macau University of Science and Technology Hospital; 🇲🇴 Macau, Macau