NCT06211218

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.

Trial Health

57
Monitor

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
3,000

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Dec 2020

Longer than P75 for all trials

Geographic Reach
1 country

1 active site

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

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Study Timeline

Key milestones and dates

Study Start

First participant enrolled

December 6, 2020

Completed
1 year until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 6, 2021

Completed
2.1 years until next milestone

First Submitted

Initial submission to the registry

January 8, 2024

Completed
10 days until next milestone

First Posted

Study publicly available on registry

January 18, 2024

Completed
11 months until next milestone

Study Completion

Last participant's last visit for all outcomes

December 6, 2024

Completed
Last Updated

November 4, 2024

Status Verified

October 1, 2024

Enrollment Period

1 year

First QC Date

January 8, 2024

Last Update Submit

October 31, 2024

Conditions

Deep LearningCorneal DiseaseScreening

Outcome Measures

Primary Outcomes (2)

  • 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.

    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.

    1 week

Study Arms (1)

Cornea diseases diagnosed by artificial intelligence algorithm

Diagnostic Test: Cornea diseases diagnosed by artificial intelligence algorithm

Interventions

Cornea diseases diagnosed by artificial intelligence algorithmDIAGNOSTIC_TEST

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

Cornea diseases diagnosed by artificial intelligence algorithm

Eligibility Criteria

Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

The study population is derived from an anonymous database that contains health examination results of the general population.

You may qualify if:

  • 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.

You may not qualify if:

  • ）Insufficient information for diagnosis.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Tiajin Eye Hospital

Tianjin, Tianjin Municipality, China

RECRUITING

MeSH Terms

Conditions

Corneal Diseases

Condition Hierarchy (Ancestors)

Eye Diseases

Study Officials

  • Yan Wang, Prof

    Tianjin Eye Hospital

    STUDY CHAIR

Central Study Contacts

Yan Huo, Master

CONTACT

13102118953hy13102118953@163.com

Study Design

Study Type
observational
Observational Model
CASE CONTROL
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

January 8, 2024

First Posted

January 18, 2024

Study Start

December 6, 2020

Primary Completion

December 6, 2021

Study Completion

December 6, 2024

Last Updated

November 4, 2024

Record last verified: 2024-10

Locations

CN(1)