Artificial Intelligence-assissted Glaucoma Evaluation (AGE)

Sun Yat-sen University

Status: Completed

Conditions: Artificial Intelligence; Glaucoma

Treatments: Diagnostic Test: Visual field and OCT tests

Study type: Observational

Funder types: Other

Identifiers

NCT03268031

ProjectAGE

Details and patient eligibility

About

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.

Full 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.

Enrollment: 10,800 patients

Sex: All

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

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

Exclusion criteria

1. unable to complete visual field test

Trial design

10,800 participants in 2 patient groups

Glaucoma patients

Description:

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.

Treatment:

Diagnostic Test: Visual field and OCT tests

Non-glaucoma participants

Description:

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.

Treatment:

Diagnostic Test: Visual field and OCT tests