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

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.

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Key information

Conditions

Urolithiasis Calculi Deep Learning Reconstruction Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Male Urogenital Diseases Neurologic Manifestations Pain Pathological Conditions, Anatomical Pathological Conditions, Signs and Symptoms Renal Colic Signs and Symptoms Urinary Calculi Urinary Tract Stones Urogenital Diseases Urologic Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

CHU Amiens

Amiens, 80480, France

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Abdominopelvic low dose CT

Diagnostic Test

Patients with urinary stones will undergo multiple computed tomography (CT) examinations

Primary outcomes

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

Time frame: day 1

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).

Sponsors and collaborators

Lead sponsor

Centre Hospitalier Universitaire, Amiens

Other

Registry information

Official study title

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

Acronym: URO DLIR

Important dates

Study start: 2020
Primary completion: 2022
Study completion: 2022
First posted: Jul 29, 2020
Registry last updated: Jun 15, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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