A Study on Ophthalmic Multimodal AI-Assisted Medical Decision-Making Based on Imaging and Electronic Medical Record Data
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Ocular Diseases
- Sponsor
- The Eye Hospital of Wenzhou Medical University
- Enrollment
- 5000000
- Locations
- 5
- Primary Endpoint
- Area Under the Curve (AUC)
- Status
- Recruiting
- Last Updated
- last year
Overview
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.
Investigators
Kang Zhang
Chief Scientist
The Eye Hospital of Wenzhou Medical University
Eligibility Criteria
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.
- •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.
Exclusion Criteria
- •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.
Outcomes
Primary Outcomes
Area Under the Curve (AUC)
Time Frame: 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
Time Frame: 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
Time Frame: 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
Time Frame: 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
Time Frame: 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
Time Frame: 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
Time Frame: 1 years
Percentage (%) of patients experiencing postoperative complications.
Recurrence Risk Rate
Time Frame: 1 years
Percentage (%) of patients experiencing recurrence during the follow-up period.
Survival Rate
Time Frame: 1 years
Percentage (%) of patients alive, calculated using Kaplan-Meier survival curves.
Effectiveness of Decision Support
Time Frame: 1 years
Percentage (%) improvement in the accuracy of treatment decisions with AI assistance compared to traditional decisions.
Decision Time Efficiency
Time Frame: 1 years
Average time (seconds) required for physicians to make diagnostic and treatment decisions, before and after AI assistance.
Secondary Outcomes
- 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)
- Treatment Adherence(1 years)
- Physician Acceptance of AI System(1 years)