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

C

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)

Trial contacts and locations

0

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Data sourced from clinicaltrials.gov

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