Artificial Intelligence for Screening of Multiple Corneal Diseases
- Conditions
- Deep LearningCorneal DiseaseScreening
- Interventions
- Diagnostic Test: Cornea diseases diagnosed by artificial intelligence algorithm
- Registration Number
- NCT06211218
- Lead Sponsor
- Tianjin Eye Hospital
- Brief Summary
This study developed a deep learning algorithm based on anterior segment images and prospectively validated its ability to identify corneal diseases.The effectiveness and accuracy of this algorithm was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and area under curve.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 3000
- The quality of slit-lamp images should clinical acceptable.
- More than 90% of the slit-lamp image area including three main regions (sclera, pupil, and lens) are easy to read and discriminate.
1)Insufficient information for diagnosis.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Cornea diseases diagnosed by artificial intelligence algorithm Cornea diseases diagnosed by artificial intelligence algorithm -
- Primary Outcome Measures
Name Time Method Area under curve 1 week We used the receiver operating characteristic (ROC) curve and area under curve to examine the ability of this artificial intelligence algorism recognition and classification of corneal diseases.
Sensitivity and specificity 1 week We used sensitivity and specificity to examine the ability of this artificial intelligence algorism recognition and classification of corneal diseases.
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Tiajin Eye Hospital
🇨🇳Tianjin, Tianjin, China