Validation of the Utility of Ophthalmology Intelligent Diagnostic System: A Clinical Trial
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Ophthalmopathy
- Sponsor
- Sun Yat-sen University
- Enrollment
- 615
- Locations
- 1
- Primary Endpoint
- The proportion of accurate, mistaken and miss detection of the ophthalmology diagnostic system.
- Status
- Completed
- Last Updated
- 6 years ago
Overview
Brief Summary
The prevention and treatment of diseases via artificial intelligence represents an ultimate goal in computational medicine. Application scenarios of the current medical algorithms are too simple to be generally applied to real-world complex clinical settings. Here, the investigators use "deep learning" and "visionome technique", an novel annotation method for artificial intelligence in medical, to create an automatic detection and classification system for four key clinical scenarios: 1) mass screening, 2) comprehensive clinical triage, 3) hyperfine diagnostic assessment, and 4) multi-path treatment planning. The investigator also establish a telemedicine system and conduct clinical trial and website-based study to validate its versatility.
Investigators
Haotian Lin
Clinical Professor
Sun Yat-sen University
Eligibility Criteria
Inclusion Criteria
- •Patients and residents who underwent ophthalmic examination of the eye and recorded their ocular information in the outpatient clinic and community.
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
The proportion of accurate, mistaken and miss detection of the ophthalmology diagnostic system.
Time Frame: Up to 5 years