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Machine-learning approach to determine retinal characteristics for hemoglobin screening

Machine-learning approach to determine retinal characteristics for hemoglobin screening

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000035149
Enrollment
Unknown
Registered
2020-08-02
Start date
2020-06-22
Completion date
Unknown
Last updated
2020-08-03

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

Conditions

Hemoglobin level

Interventions

Case series:Nil

Sponsors

The Centre for Clinical Research and Biostatistics, The Chinese University of Hong Kong
Lead Sponsor

Eligibility

Sex/Gender
Male
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Age>18 years; 2. Willing to sign the inform consent; 3. Willing to comply with procedures required in the protocol; 4. Completed a fingerstick hemoglobin test.

Exclusion criteria

Exclusion criteria: 1. Poor retinal images quality that cannot be used in the analysis; 2. Subjects with other eye diseases which are not suitable for retinal imaging, such as severe cataract, glaucoma, atretopsia, corneal plague; 3. Subjects are distress with flashlight or have experience with photosensitive seizure; 4. Unwillingness or inability to comply with procedures required in the protocol.

Design outcomes

Primary

MeasureTime frame
retinal vessels parameters;—

Countries

China

Contacts

Public ContactProf. Benny Zee

The Chinese University of Hong Kong

bzee@cuhk.edu.hk+852 22528865

Outcome results

None listed

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