Diagnosing anaemia from digital images of the palpebral conjunctiva by machine learning

Diagnosing anaemia from digital images of the palpebral conjunctiva by machine learning - Diagnosing anaemia from digital images of the palpebral conjunctiva by machine learning

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000040494

Enrollment

800

Registered

2020-06-01

Start date

2020-05-02

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

anemia

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: This study's participants will consist of patients who are performed the laboratory test to Outpatient department and general internal medicine department of Dokkyo Medical University, from May 2020 to April 2021.

Exclusion criteria

Exclusion criteria: 1. Those who cannot get digital image of palpebral conjunctiva. 2. Those who do not consent to participate in this study.

Design outcomes

Primary

Measure	Time frame
The primary study outcome is a diagnostic accuracies (sensitivity, specificity, positive predictive value, negative predictive value and ROC curve by cross validation etc) of predictive hemoglobin from machine learning for digital image of palpebral conjunctiva. The reference standard is the patient's serum hemoglobin.	—

Secondary

Measure	Time frame
The difference of the primary study outcome and the doctor's predictive value for the patient's serum hemoglobin	—

Countries

Japan

Contacts

Public ContactTakanobu Hirosawa

Dokkyo Medical University Department of Diagnostic and Generalist Medicine

hirosawa@dokkyomed.ac.jp0282-86-1111

Outcome results