Validation of the Utility of Myopia Intelligent Diagnostic System
Not Applicable
- Conditions
- Myopia
- Registration Number
- NCT04014725
- Lead Sponsor
- Sun Yat-sen University
- Brief Summary
The screening of myopia via artificial intelligence represents an challenge in computational medicine. Here, the investigators use "deep learning" to create an automatic diagnostic system for myopia screening using ocular appearance images. The investigator also use this system and conduct clinical trial to validate its performance.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- UNKNOWN
- Sex
- All
- Target Recruitment
- 50
Inclusion Criteria
- Student aged 6-18
Exclusion Criteria
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- SINGLE_GROUP
- Primary Outcome Measures
Name Time Method The AUC of the Myopia Intelligent Diagnostic System up to 3 month
- Secondary Outcome Measures
Name Time Method The sensitivity of the Myopia Intelligent Diagnostic System up to 3 month The specificity of the Myopia Intelligent Diagnostic System up to 3 month
Related Research Topics
Explore scientific publications, clinical data analysis, treatment approaches, and expert-compiled information related to the mechanisms and outcomes of this trial. Click any topic for comprehensive research insights.
What molecular mechanisms underlie myopia progression detectable by AI diagnostic systems like the Myopia Intelligent Diagnostic System?
How does the Myopia Intelligent Diagnostic System compare to standard-of-care myopia screening methods in terms of accuracy and efficiency?
Are there specific ocular biomarkers that enhance the predictive accuracy of AI-based myopia diagnostic systems?
What adverse events are associated with AI-driven myopia screening technologies and how are they managed in clinical practice?
What are the key features distinguishing the Myopia Intelligent Diagnostic System from other AI-based myopia detection devices in development?