Detection of Urinary Stones on ULDCT With Deep-learning Image Reconstruction Algorithm (URO DLIR)

Centre Hospitalier Universitaire, Amiens

Status: Active, not recruiting

Conditions: Urinary Tract Stones; Urolithiasis; Deep Learning Reconstruction; Renal Colic

Treatments: Diagnostic Test: Abdominopelvic low dose CT

Study type: Interventional

Funder types: Other

Identifiers

NCT04490343

PI2020_843_0053

Details and patient eligibility

About

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.

Enrollment: 62 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Age ≥ 18 years old,
• Patient referred for abdominopelvic CT to confirm urolithiasis or for follow-up,
• Affiliation to a social security program,
• Ability of the subject to understand and express opposition

Exclusion criteria

• Age <18 years old,
• Person under guardianship or curators,
• Pregnant woman,
• Any contraindications to CT

Trial design

Primary purpose: Diagnostic

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)