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Prospective validation of a CT-based artificial intelligence-assisted diagnosis system for urolithiasis

Prospective validation of a CT-based artificial intelligence-assisted diagnosis system for urolithiasis

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500095803
Enrollment
Unknown
Registered
2025-01-13
Start date
2025-01-21
Completion date
Unknown
Last updated
2025-01-27

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

Conditions

Urolithiasis

Interventions

Gold Standard:In this study, the developed software was used to segment and label urolithiasis in CT images manually. Then the stone volume was calculated according to the results of manual segmentati
Index test:The ability of the artificial intelligence-assisted diagnosis system for urolithiasis to automatically detect urolithiasis, calculate stone volume, and predict stone composition.

Sponsors

Sun Yat-sen Memorial Hospital of Sun Yat-sen University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
No minimum to 100 Years

Inclusion criteria

Inclusion criteria: 1.Patients diagnosed with urolithiasis. 2.Surgical stone removal was performed, and stone composition analysis was performed. 3.CT examination was performed before surgery.

Exclusion criteria

Exclusion criteria: 1.CT image quality is not good, such as artifacts, image blur, etc. 2.There are too few stone specimens to obtain the results of stone composition analysis.

Design outcomes

Primary

MeasureTime frame
Dice coefficient;Area under the ROC curve, AUC;—

Secondary

MeasureTime frame
Accuracy;—

Countries

China

Contacts

Public ContactTianxin Lin

Sun Yat-sen Memorial Hospital of Sun Yat-sen University

lintx@mail.sysu.edu.cn+86 20 3407 1255

Outcome results

None listed

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