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Development and Validation of a Deep Learning Model for Grading of Vesicoureteral Reflux on Voiding Cystourethrogram

Development and Validation of a Deep Learning Model for Grading of Vesicoureteral Reflux on Voiding Cystourethrogram

Status
Active, not recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400088035
Enrollment
Unknown
Registered
2024-08-09
Start date
2024-08-15
Completion date
Unknown
Last updated
2024-08-12

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

Conditions

Congenital malformations of the urinary system

Interventions

Gold Standard:Three clinicians with 10 years of clinical experience grading the VUR according to the ternational Reflux Society classification criteria
Index test:The deep learning model for VUR Grading

Sponsors

Children's Hospital of Fudan University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
1 Years to 12 Years

Inclusion criteria

Inclusion criteria: Patients aged from 1 to 13 years olds whose VCUG images were clear without severe metal artifacts and diagnosed as Vesicoureteral Reflux , are included in this study

Exclusion criteria

Exclusion criteria: The full ureter could not be visualized, and for poor image quality, excessive malformation (e.g., cloacal malformation, ectopic ureter, and hypospadias), lack of anteroposterior view were excluded.

Design outcomes

Primary

MeasureTime frame
The AUC of grading VUR;The accuracy of grading VUR; The sensitivity of grading VUR;The specificity of grading VUR;

Countries

CHINA

Contacts

Public ContactZhuang Likai

Children's Hospital of Fudan University

lszx04336@163.com+86 136 3641 2046

Outcome results

None listed

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