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Ophthalmic Multimodal AI-Assisted Medical Decision-Making

NCT06755190 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 5000000

Last updated 2025-04-17

No results posted yet for this study

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.

Conditions

  • Ocular Diseases

Interventions

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.

Sponsors & Collaborators

  • The Eye Hospital of Wenzhou Medical University

    lead OTHER

Principal Investigators

  • Kang Zhang, PhD. · The Eye Hospital of Wenzhou Medical University

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-12-20
Primary Completion
2025-05-31
Completion
2025-05-31

Countries

  • China
  • Macau

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06755190 on ClinicalTrials.gov