Ophthalmic Multimodal AI-Assisted Medical Decision-Making

The Eye Hospital of Wenzhou Medical University5,000,000 enrolled

Overview

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.

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

5,000,000

Conditions

Ocular Diseases

Interventions

Diagnostic Test: AI-Based Diagnostic and Prognostic Model for Ocular DiseasesDIAGNOSTIC_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.

Eligibility

Sex: ALL

Medical Language ↔ Plain English

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.

Locations (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

Outcomes

Primary Outcomes

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

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

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

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

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).

Time frame: 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).

Time frame: 1 years

Postoperative Complication Rate

Central Contacts

Lan Wang, MD

CONTACT

+86-0577-85397527 wl2832300533@163.com

Data from ClinicalTrials.gov

This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.

Secondary Outcomes

System Usability Score

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

Time frame: 1 years

AI System Response Time

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

Time frame: 1 years

System Failure Rate

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

Time frame: 1 years

User Interface Design Satisfaction

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

Time frame: 1 years

Patient Satisfaction Score

Measured using the Patient Satisfaction Questionnaire (CSQ-8), with scores ranging from 8-32.

Time frame: 1 years

Treatment Adherence

Percentage (%) of patients adhering to personalized treatment plans and regular follow-up visits.

Time frame: 1 years

Physician Acceptance of AI System

Evaluated using the Technology Acceptance Model (TAM) scale, with scores ranging from 1-7.

Time frame: 1 years