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
The Eye Hospital of Wenzhou Medical University
5,000,000 participants
Dec 20, 2024
OBSERVATIONAL
Conditions
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.
Eligibility
Inclusion Criteria6
- 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.
Exclusion Criteria3
- 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.
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Interventions
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.
Locations(5)
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NCT06755190