Artificial Intelligence for Screening of Multiple Corneal Diseases

Tianjin Eye Hospital3,000 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

3,000

Conditions

Deep Learning Corneal Disease Screening

Interventions

Cornea diseases diagnosed by artificial intelligence algorithmDIAGNOSTIC_TEST

An artificial intelligence algorithm was applied to diagnose cornea diseases from slit-lamp images.

Eligibility

Sex: ALL

Medical Language ↔ Plain English

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.

Locations (1)

Tiajin Eye Hospital

Tianjin, Tianjin Municipality, China

RECRUITING

Outcomes

Primary Outcomes

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 week

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 week

Central Contacts

Yan Huo, Master

CONTACT

13102118953 hy13102118953@163.com

Data from ClinicalTrials.gov

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