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AI-Powered Ultra-Low radiation CT could revolutionize kidney stone diagnosis

NCT ID NCT04490343

Diagnosis Sponsor: Centre Hospitalier Universitaire, Amiens Source: ClinicalTrials.gov ↗

What the study statuses mean

This study's is highlighted.

Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed This study
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Jun 27, 2026 · Last updated Jun 27, 2026

Summary

This study tested whether an ultra-low-dose CT scan, combined with a deep-learning AI reconstruction, can accurately detect urinary tract stones. 62 adults with known or suspected kidney stones received both a standard low-dose CT and an additional ultra-low-dose scan (less than 1 mSv). The goal was to see if the AI-enhanced ultra-low-dose method matches the standard scan's accuracy while exposing patients to much less radiation.

What this could mean

Our plain-language read of the trial. This is informational only, not medical advice or a prediction.

Active substance
ultra-low dose CT with deep-learning image reconstruction
What this could lead to
If successful, this approach could allow doctors to diagnose kidney stones using much less radiation than standard CT scans.
What could go wrong
This is a small, completed study (62 people) testing a new imaging technique. The AI method may not work as well in all patients or settings, and further validation is needed.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Phase

Not a phased trial

Phase numbers describe drug development. The registry uses this when they do not apply, as it does for trials of devices, procedures or behaviour changes, and for observational studies.

Participants

62 people

The number who actually took part.

Started

Jun 2020

Finished

Mar 2022

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Ages

18 years and older

Sex

Anyone

Healthy volunteers

Not accepted

This study is not open to healthy volunteers. The entry requirements below say who it is open to.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

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

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Conditions

The condition(s) this trial relates to.

Renal Colic Urinary Calculi urolithiasis

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

Deep learning reconstruction Renal colic Urinary tract stones Urolithiasis

Contacts and locations

Locations

  • CHU Amiens

    Amiens, 80480, France

More trials for these conditions

Other studies related to the condition(s) this trial covers.