Validation of the Utility of Rare Disease Intelligence Platform: A Multicenter Cluster Clinical Trial
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Cataract
- Sponsor
- Sun Yat-sen University
- Enrollment
- 53
- Locations
- 4
- Primary Endpoint
- The proportion of accurate, mistaken and miss detection of CC-Cruiser.
- Status
- Completed
- Last Updated
- 10 years ago
Overview
Brief Summary
The prevention and treatment of diseases via artificial intelligence represents an ultimate goal in computational medicine. The artificial intelligence for systematic clinical application has not yet been successfully validated. Currently, the main prevention strategy for rare diseases is to build specialized care centers. However, these centers are scattered, and their coverage is insufficient, resulting in inadequate health care among a large proportion of rare disease patients. Here, the investigators use "deep learning" to create CC-Cruiser, an intelligence agent involving three functional networks: "pick-up networks" for diagnostics, "evaluation networks" for risk stratification and "strategist networks" to provide assisted treatment decisions. The investigator also establish a cloud intelligence platform for multi-hospital collaboration and conduct clinical trial and website-based study to validate its versatility.
Investigators
Erping Long
Principal Investigator, Home for Cataract Children, Zhongshan Ophthalmic Center
Sun Yat-sen University
Eligibility Criteria
Inclusion Criteria
- •Patients who underwent ophthalmic examination of the eye and recorded their ocular information in the collaborating hospital.
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
The proportion of accurate, mistaken and miss detection of CC-Cruiser.
Time Frame: Up to 4 years