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Deep learning approach to predict visual field in the Korean patients with glaucoma

Deep learning approach to predict central 10-2 and/or 24-2 visual field from swept-source optical coherence tomography or funduscopy images in the Korean patients with glaucoma

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0007192
Enrollment
3000
Registered
2022-04-14
Start date
2022-03-30
Completion date
Unknown
Last updated
2022-05-02

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

None listed

Interventions

None listed

Sponsors

Pusan National University Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Glaucoma patients who visited Pusan National University Hospital from 2011 to 2021. These patients must no history of traumatic eye damage, no other ophthalmologic diseases, and no eye surgery without cataract surgery.

Exclusion criteria

Exclusion criteria: The patients who had history of traumatic eye damage, other ophthalmologic diseases, and history of eye surgery without cataract surgery.

Design outcomes

Primary

MeasureTime frame
Visual field test results—

Secondary

MeasureTime frame
Reliability of prediction—

Countries

Korea, Republic of

Contacts

Public ContactChung Woohyun

Pusan National University Hospital

ot2018212@naver.com+82-51-240-7326

Outcome results

None listed

Source: CRIS (via WHO ICTRP) · Data processed: Feb 4, 2026