Corneal Disease, Deep Learning, Screening
Conditions
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.
Interventions
DIAGNOSTIC_TESTCornea diseases diagnosed by artificial intelligence algorithm
An artificial intelligence algorithm was applied to diagnose cornea diseases from slit-lamp images.
Sponsors
Tianjin Eye Hospital
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
1. The quality of slit-lamp images should clinical acceptable. 2. More than 90% of the slit-lamp image area including three main regions (sclera, pupil, and lens) are easy to read and discriminate.
Exclusion criteria
1）Insufficient information for diagnosis.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
China
Contacts
Primary ContactYan Huo, Master
Outcome results
None listed