Artificial Intelligence for Screening of Multiple Corneal Diseases
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorTianjin Eye Hospital
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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.
Disqualifiers
None
Trial population
The study population is derived from an anonymous database that contains health examination results of the general population.
Trial design
Design model
Case-control
Time perspective
Prospective
Treatments tested in this trial
Cornea diseases diagnosed by artificial intelligence algorithm
Diagnostic testAn artificial intelligence algorithm was applied to diagnose cornea diseases from slit-lamp images.
Treatment groups
3,000 Participants
are divided into 1 treatment groupGroup A: Cornea diseases diagnosed by artificial intelligence algorithm1 intervention
Trial outcomes
Primary outcomes
1
Area under curve
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.
Time frame
1 week2
Sensitivity and specificity
We used sensitivity and specificity to examine the ability of this artificial intelligence algorism recognition and classification of corneal diseases.
Time frame
1 weekSponsors and contacts
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Source ClinicalTrials.gov