Ophthalmic Multimodal AI-Assisted Medical Decision-Making

A Study on Ophthalmic Multimodal AI-Assisted Medical Decision-Making Based on Imaging and Electronic Medical Record Data

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.

Study Overview

• Status: Recruiting
• Conditions: Ocular Diseases
• Intervention / Treatment: Diagnostic test: Diagnostic Test: AI-Based Diagnostic and Prognostic Model for Ocular Diseases

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 Type: Observational
• Enrollment (Estimated): 5000000

Contacts and Locations

• Study Contact: Name: Lan Wang, MD; Phone Number: +86-0577-85397527; Email: wl2832300533@163.com

Study Locations

• China
  • Guangdong
    • Zhuhai, Guangdong, China
      • Recruiting
      • ZhuHai Hospital, zhuhai, guangdong
      • Contact: Bingzhou Li; Phone Number: +86-0756-2222569; Email: mr_jerry_99@163.com
  • Zhejiang
    • Wenzhou, Zhejiang, China
      • Recruiting
      • First Affiliated Hospital of Wenzhou Medical University
      • Contact: Cheng Tang, MD; Phone Number: +86-0577-55579999; Email: c249325687@163.com
    • Wenzhou, Zhejiang, China
      • Recruiting
      • Second Affiliated Hospital of Wenzhou Medical Universit
      • Contact: Sian Liu, PhD.; Phone Number: +86-0577-88002888; Email: liusan@mail3.sysu.edu.cn
    • Wenzhou, Zhejiang, China
      • Recruiting
      • The Eye Hospital of Wenzhou Medical University
      • Contact: Lan Wang, MD; Phone Number: +86-0577-85397527; Email: wl2832300533@163.com
• Macau
  • Macau, Macau
    • Recruiting
    • Macau University of Science and Technology Hospital
    • Contact: Yang Liu, MD; Phone Number: +853-2882-1838; Email: liuyang.macau@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

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 Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

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

Collaborators and Investigators

• Sponsor: The Eye Hospital of Wenzhou Medical University
• Investigators: Principal Investigator: Kang Zhang, PhD., The Eye Hospital of Wenzhou Medical University

Study record dates

Study Major Dates

• Study Start (Actual): December 20, 2024
• Primary Completion (Estimated): May 1, 2025
• Study Completion (Estimated): May 1, 2025

Study Registration Dates

• First Submitted: December 15, 2024
• First Submitted That Met QC Criteria: December 23, 2024
• First Posted (Actual): January 1, 2025

Study Record Updates

• Last Update Posted (Actual): April 17, 2025
• Last Update Submitted That Met QC Criteria: April 16, 2025
• Last Verified: April 1, 2025

More Information

Terms related to this study

• Keywords: Artificial Intelligence; ocular diseases; Ophthalmic Multimodal AI-Assisted Medical Decision-Making
• Other Study ID Numbers: Ophthalmic Multimodal AI

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No