Ophthalmic Multimodal AI-Assisted Medical Decision-Making
NCT ID: NCT06755190
Last Updated: 2025-04-17
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
5000000 participants
OBSERVATIONAL
2024-12-20
2025-05-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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CASE_ONLY
RETROSPECTIVE
Study Groups
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normal
patients who do not have the ocular diseases
No interventions assigned to this group
ocular diseases
patients who have ocular diseases
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.
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
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
2. Cases with ambiguous or unverified diagnoses that cannot be clearly categorized.
3. Duplicated or redundant data from the same patient.
ALL
No
Sponsors
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The Eye Hospital of Wenzhou Medical University
OTHER
Responsible Party
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Kang Zhang
Chief Scientist
Principal Investigators
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Kang Zhang, PhD.
Role: PRINCIPAL_INVESTIGATOR
The Eye Hospital of Wenzhou Medical University
Locations
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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
Macao, Macau, Macau
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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Ophthalmic Multimodal AI
Identifier Type: -
Identifier Source: org_study_id
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