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

Centre Hospitalier Universitaire, Amiens62 enrolled

Overview

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.

Study Type

INTERVENTIONAL

Allocation

Purpose

DIAGNOSTIC

Masking

NONE

Enrollment

Conditions

Urolithiasis Urinary Tract Stones Renal Colic Deep Learning Reconstruction

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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

Outcomes

Primary Outcomes

Accuracy between low dose CT using DLIR reconstruction and low dose CT without DLIR reconstruction for the detection of urinary tract stones

Accuracy between low dose CT using DLIR reconstruction and low dose CT without DLIR reconstruction for the detection of urinary tract stones. Patients who were referred to the department for abdominopelvic CT exam for urolithiasis diagnostic or follow-up, and had consented to participate in the study, will undergo an additional ultra-low dose acquisition (ULD, \<1 mSv) with deep learning-based reconstruction (DLIR).

Time frame: day 1