Development of Diagnostic Artificial intelligence in the Ophthalmology
Not Applicable
- Conditions
- Cataract, Dry Eye Disease, Keratoconus, Glaucoma, Allergic conjunctivitis, etc
- Registration Number
- JPRN-UMIN000040321
- Lead Sponsor
- Department of Ophthalmology, Keio University School of Medicine
- Brief Summary
The accuracy of tear film breakup time estimation was 0.789 (95% confidence interval (CI) 0.769 0.809), and the area under the receiver operating characteristic curve of this AI model was 0.877 (95% CI 0.861 0.893). The sensitivity and specificity of this AI model for the diagnosis of DED were 0.778 (95% CI 0.572 0.912) and 0.857 (95% CI 0.564 0.866), respectively. We successfully developed a novel AI-based diagnostic model for DED.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Complete: follow-up continuing
- Sex
- All
- Target Recruitment
- 300
Inclusion Criteria
Not provided
Exclusion Criteria
A case which does not want to commit to the clinical trial, no opt-in paper, other
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Clinical parameters by the Ophthalmological images
- Secondary Outcome Measures
Name Time Method