Deep learning models to predict the risk of diabetic kidney disease progression using fundus imaging in DM patients: a prospective study protocol

Development and validation of predictive models for the progression of diabetic kidney disease based on MMC clinical data and TCM four diagnostic information

Status

Recruiting

Phases

Early Phase 1

Study type

Observational

Source

ChiCTR

Registry ID

ChiCTR2300072281

Enrollment

Unknown

Registered

2023-06-08

Start date

2023-07-01

Completion date

Unknown

Last updated

2023-06-18

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

Conditions

diabetic kidney disease

Interventions

Case series:N/A

Eligibility

Sex/Gender

All

Age

18 Years to 75 Years

Inclusion criteria

Inclusion criteria: (1) aged 18-75 years; (2) the fasting blood glucose level of greater than or equal to 7.0 mmol/L, and/or two-hour postprandial blood glucose level of greater than or equal to 11.1 mmol/L, and/or glycosylated hemoglobin greater than or equal to 6.5%.

Exclusion criteria

Exclusion criteria: (1) those diagnosed with other severe diseases such as tumor and diseases of immune and hematological systems; (2) those who cannot make a clear description or cooperate with the imaging collection due to mental disorders; (3) women during pregnancy or breastfeeding; (4) those who refuse to sign informed consent.

Design outcomes

Primary

Measure	Time frame
fundus image;TCM syndrome;	—

Countries

China

Contacts

Public ContactSun Luying

Beijing University of Chinese Medicine Fangshan Hospital

18600173188@163.com+86 186 0017 3188

Outcome results

None listed

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