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Clinical research on non-invasive diagnosis and evaluation of diabetic nephropathy based on radiomics combined with deep learning

Clinical research on non-invasive diagnosis and evaluation of diabetic nephropathy based on radiomics combined with deep learning

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600125652
Enrollment
Unknown
Registered
2026-05-29
Start date
2026-06-01
Completion date
Unknown
Last updated
2026-06-01

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

Conditions

Diabetic nephropathy

Interventions

The diabetic nephropathy group:None

Sponsors

Fifth Affiliated Hospital, Sun Yat-Sen University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.From January 2019 to December 2025, patients with diabetic nephropathy were hospitalized and diagnosed in the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai People's Hospital and Zhuhai Shangchong Hospital; 2.Age >=18 years with no restriction on sex; 3.From January 2019 to December 2025, patients underwent renal ultrasound imaging in the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai People's Hospital and Zhuhai Shangchong Hospital.

Exclusion criteria

Exclusion criteria: 1.The image blur can not be further analyzed; 2.Poor respiratory coordination during examination, the image can not be obtained.

Design outcomes

Primary

MeasureTime frame
Diabetic nephropathy, DN;—

Countries

China

Contacts

Public ContactGao Ziqing

Fifth Affiliated Hospital, Sun Yat-Sen University

gaozq3@mail.sysu.edu.cn+86 756 2528232

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Jun 11, 2026