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Artificial Intelligence for Screening of Multiple Corneal Diseases

Recruiting
Conditions
Deep Learning
Corneal Disease
Screening
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
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.

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Arm && Interventions
GroupInterventionDescription
Cornea diseases diagnosed by artificial intelligence algorithmCornea diseases diagnosed by artificial intelligence algorithm-
Primary Outcome Measures
NameTimeMethod
Area under curve1 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 specificity1 week

We used sensitivity and specificity to examine the ability of this artificial intelligence algorism recognition and classification of corneal diseases.

Secondary Outcome Measures
NameTimeMethod

Trial Locations

Locations (1)

Tiajin Eye Hospital

🇨🇳

Tianjin, Tianjin, China

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