AGE: Artificial Intelligence-assissted Glaucoma Evaluation

Sponsor

Sun Yat-sen University (Other)

Overall Status

Completed

CT.gov ID

NCT03268031

Collaborator

Chinese Academy of Sciences (Other)

10,800

Enrollment

Location

29.6

Actual Duration (Months)

365.3

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

Glaucoma is currently the second leading cause of irreversible blindness in the world. Our study intends to combine clinical data of glaucoma patients in Zhongshan Ophthalmic Center with Artificial Intelligence techniques to create programs that can screen and diagnose glaucoma.

Condition or Disease	Intervention/Treatment	Phase
Artificial Intelligence Glaucoma	Diagnostic Test: Visual field and OCT tests

Detailed Description

Glaucoma is currently the second leading cause of irreversible blindness in the world, which brings heavy burden to human society. Compared to other ocular diseases, diagnostic process of glaucoma is complicated depends on multiple test results, including visual field test, OCT, etc. How to diagnose glaucoma correctly and fast has always been a hot topic in glaucoma researches. Artificial intelligence is used to study and develop theories and methods that can help simulate and extend human intelligence, which has been utilized in a lot of research fields such as automatic drive and medicine. The study intends to combine clinical data of glaucoma patients in Zhongshan Ophthalmic Center with Artificial Intelligence techniques to create programs that can screen and diagnose glaucoma.

Study Design

Study Type:

Observational

Actual Enrollment :

10800 participants

Observational Model:

Case-Only

Time Perspective:

Retrospective

Official Title:

Development of Artificial Intelligence-assissted Diagnostic Program of Glaucoma

Actual Study Start Date :

Aug 15, 2017

Actual Primary Completion Date :

Dec 1, 2019

Actual Study Completion Date :

Feb 1, 2020

Arms and Interventions

Arm	Intervention/Treatment
Glaucoma patients Glaucoma patients will take visual field test and OCT imaging of optic nerve area. All of these data will be collected as source of machine learning.	Diagnostic Test: Visual field and OCT tests Visual field test and OCT are commonly used essential tests to make accurate diagnosis of glaucoma. Algorithms to classify Visual field and OCT tests would both be developed and verified.
Non-glaucoma participants Non-glaucoma participants will take visual field test and OCT imaging of optic nerve area. All of these data will be collected as source of machine learning.	Diagnostic Test: Visual field and OCT tests Visual field test and OCT are commonly used essential tests to make accurate diagnosis of glaucoma. Algorithms to classify Visual field and OCT tests would both be developed and verified.

Arm

Intervention/Treatment

Glaucoma patients

Glaucoma patients will take visual field test and OCT imaging of optic nerve area. All of these data will be collected as source of machine learning.

Diagnostic Test: Visual field and OCT tests

Visual field test and OCT are commonly used essential tests to make accurate diagnosis of glaucoma. Algorithms to classify Visual field and OCT tests would both be developed and verified.

Non-glaucoma participants

Non-glaucoma participants will take visual field test and OCT imaging of optic nerve area. All of these data will be collected as source of machine learning.

Diagnostic Test: Visual field and OCT tests

Visual field test and OCT are commonly used essential tests to make accurate diagnosis of glaucoma. Algorithms to classify Visual field and OCT tests would both be developed and verified.

Outcome Measures

Primary Outcome Measures

Accuracy of diagnosis by artificial intelligence algorithm [from August 2017 to February 2021]
Accuracy of diagnosis by artificial intelligence algorithm and compare this result with glaucoma specialists

Secondary Outcome Measures

Sensitivity of diagnosis by artificial intelligence algorithm [from August 2017 to February 2021]
Sensitivity of diagnosis by artificial intelligence algorithm
Specificity of diagnosis by artificial intelligence algorithm [from August 2017 to February 2021]
Specificity of diagnosis by artificial intelligence algorithm

Eligibility Criteria

Criteria

Ages Eligible for Study:

N/A and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Yes

Inclusion Criteria:

BCVA>0.1
able to complete reliable visual field test
no history of intraocular surgery or fundus laser

Exclusion Criteria:

unable to complete visual field test

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Zhongshan Ophthalmic Center	Guangzhou	Guangdong	China	510000

Sponsors and Collaborators

Sun Yat-sen University
Chinese Academy of Sciences

Investigators

Principal Investigator: Xiulan Zhang, Doctor, Sun Yat-sen University

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

Responsible Party:

Xiulan Zhang, Director of Clinical Research Center, Sun Yat-sen University

ClinicalTrials.gov Identifier:

NCT03268031

Other Study ID Numbers:

ProjectAGE

First Posted:

Aug 31, 2017

Last Update Posted:

Oct 22, 2020

Last Verified:

Oct 1, 2020

Individual Participant Data (IPD) Sharing Statement:

Plan to Share IPD:

Studies a U.S. FDA-regulated Drug Product:

Studies a U.S. FDA-regulated Device Product:

Keywords provided by Xiulan Zhang, Director of Clinical Research Center, Sun Yat-sen University

artificial intelligence

glaucoma

Additional relevant MeSH terms:

Glaucoma

Study Results

No Results Posted as of Oct 22, 2020