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Develop and validate a deep learning model for automatic diagnosis and outcome prediction of urinary calculi based on CT images

Develop and validate a deep learning model for automatic diagnosis and outcome prediction of urinary calculi based on CT images

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400086807
Enrollment
Unknown
Registered
2024-07-11
Start date
2024-07-31
Completion date
Unknown
Last updated
2024-07-15

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

Conditions

Urinary calculus

Interventions

Case series (Ureteral stone passing group and ureteral stone not passing group):None

Sponsors

Ningde Hospital Affiliated to Ningde Normal University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: Clinical data of patients diagnosed with urinary calculi were collected in a multi-center hospital from August 2018 to December 2023. All patients underwent plain scan CT examination of urinary tract in imaging department. CT images of urinary tract stones could be obtained. CT images were clear and there were no motion artifacts and other factors affecting the clarity of stone images

Exclusion criteria

Exclusion criteria: (1) Patients who did not perform urinary CT examination or only performed enhanced CT examination in our hospital before treatment and could not obtain plain CT images of urinary calculi; (2) Patients who were not re-examined by CT or ultrasound of urinary system in our hospital after treatment and could not obtain whether ureteral calculi were discharged; (3) The CT image is not clear, and the disturbance of the CT image such as motion artifact causes the image to be blurred; (4) In CT images, there are foreign bodies such as DJ tube and fistula tube that are difficult to distinguish from stones. Renal ureter malformation (5) Incomplete or lost clinical data

Design outcomes

Primary

MeasureTime frame
Accuracy in predicting ureteral calculi passing;Sensitivity in predicting ureteral calculi passing;

Countries

China

Contacts

Public ContactLin Yunqiao

Department of Urology, Ningde Hospital Affiliated to Ningde Normal University

12306184@qq.com+86 150 5938 6868

Outcome results

None listed

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