Analysis of fundus image using artificial intelligence

Analysis of fundus image using artificial intelligence - Analysis of fundus image using artificial intelligence

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000032943

Enrollment

2000

Registered

2018-06-15

Start date

2018-06-15

Completion date

Unknown

Last updated

2026-06-29

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

Conditions

Ischemic retinal disease

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Keio University Hospital Ophthalmologist Outpatients, patients who undergo or have undergone fluorescence fundus angiography examination

Exclusion criteria

Exclusion criteria: Patients who failed to acquire the fluorescence fundus contrast image due to some reasons, such as side effects during imaging, patients who could not acquire the fundus image for some reason, such as poor fixation and poor posture

Design outcomes

Primary

Measure	Time frame
Probability prediction of nonperfusion region presence by program	—

Secondary

Measure	Time frame
Probability prediction of neovascular existence, retinal blood vessel, bleeding, exudate, edema, blood vessel abnormality detection accuracy	—

Countries

Japan

Contacts

Public ContactYusaku Katada

Keio University School of Medicine Department of Ophthalmology

yusakukatada@keio.jp03-5363-3204

Outcome results