Validation of the Utility of Ophthalmology Intelligent Diagnostic System
- Conditions
- OphthalmopathyArtificial Intelligence
- Interventions
- Device: Ophthalmology diagnostic system.
- Registration Number
- NCT03499145
- Lead Sponsor
- Sun Yat-sen University
- 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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 615
- Patients and residents who underwent ophthalmic examination of the eye and recorded their ocular information in the outpatient clinic and community.
Not provided
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Eligible patients for AI test. Ophthalmology diagnostic system. Device: ophthalmology diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases.
- Primary Outcome Measures
Name Time Method The proportion of accurate, mistaken and miss detection of the ophthalmology diagnostic system. Up to 5 years
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Zhongshan Ophthalmic Center, Sun Yat-sen University
🇨🇳Guangzhou, Guangdong, China