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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

UNKNOWN

Clinical Phase

NA

Total Enrollment

62 participants

Study Classification

INTERVENTIONAL

Study Start Date

2020-07-21

Study Completion Date

2023-07-01

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence

NCT06412900

Urinary Stone Renal Colic Obstruction Ureter +4 more

ACTIVE_NOT_RECRUITING

Apply Machine Learning to the Interpretation of Urinary Crystal Morphology.

NCT06178575

Kidney Calculi

NOT_YET_RECRUITING

Analysis the Relationship Between the Abdomen CT and Outcomes in Acute Urinary Stone Patients

NCT02983409

Urinary Calculi Emergency Service, Hospital

COMPLETED

Kidney Stone Structural Analysis By Helical Computed Tomography (CT)

NCT00169780

Nephrolithiasis

COMPLETED

US-Non Contrast Computed Tomography Real-time Fusion for Urinary Stones Follow up

NCT02539004

Urolithiasis Ureterolithiasis

COMPLETED

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Urolithiasis Urinary Tract Stones Renal Colic Deep Learning Reconstruction

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Abdominopelvic low dose CT

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

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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