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Machine Learning-based Fundus Image Analysis for Assisting Glaucoma Diagnosis

Machine Learning-based Fundus Image Analysis for Assisting Glaucoma Diagnosis

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600118086
Enrollment
Unknown
Registered
2026-02-02
Start date
2026-03-01
Completion date
Unknown
Last updated
2026-02-09

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

Conditions

Glaucoma

Interventions

Moderate glaucoma:None
Severe glaucoma:None
Mild glaucoma group:None

Sponsors

Tianjin Medical University Eye Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1.Patients with primary open-angle glaucoma (POAG) and primary chronic angle-closure glaucoma (PACG) who underwent fundus photography and visual field examination at Tianjin Medical University Eye Hospital.

Exclusion criteria

Exclusion criteria: 1.Samples with blurred fundus images, overexposure, or severe obscuration that cannot be used for analysis; 2.Other ocular diseases affecting fundus image interpretation, such as diabetic retinopathy, macular degeneration, etc;

Design outcomes

Primary

MeasureTime frame
Area under the receiver operating characteristic curve, AUC;

Secondary

MeasureTime frame
Confusion Matrix;

Countries

China

Contacts

Public ContactYu Bo

Tianjin Medical University Eye Hospital

yubo4950@126.com+86 22 86428811

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 15, 2026