NCT04490343

Detection of Urinary Stones on ULDCT With Deep-learning Image Reconstruction Algorithm

Detection of Urinary Tract Stones on Ultra-low Dose Abdominopelvic CT Imaging With Deep-learning Image Reconstruction Algorithm

• Status: Unknown
• Phase: N/A
• Study type: Interventional
• Enrollment: 62 (actual)
• Sponsor: Centre Hospitalier Universitaire, Amiens · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

Urolithiasis has an increasing incidence and prevalence worldwide, and some patients may have multiple recurrences. Because these stone-related episodes may lead to multiple diagnostic examinations requiring ionizing radiation, urolithiasis is a natural target for dose reduction efforts. Abdominopelvic low dose CT, which has the highest sensitivity and specificity among available imaging modalities, is the most appropriate diagnostic exam for this pathology. The main objective of this study is to evaluate the diagnostic performance of ultra-low dose CT using deep learning-based reconstruction in urolithiasis patients.

Conditions

• Urolithiasis
• Urinary Tract Stones
• Renal Colic
• Deep Learning Reconstruction

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Abdominopelvic low dose CT	Patients with urinary stones will undergo multiple computed tomography (CT) examinations

Timeline

• Start date: 2020-07-21
• Primary completion: 2022-07-01
• Completion: 2023-07-01
• First posted: 2020-07-29
• Last updated: 2023-03-08

Locations

1 site across 1 country: France