Artificial Intelligence and Bone Tomoscintigraphies Achieved With CZT Camera (IATOS)

February 20, 2024 updated by: Achraf BAHLOUL, Central Hospital, Nancy, France

Ultra-Fast Whole-Body Bone Tomoscintigraphies Achieved With a High-Sensitivity 360° CZT Camera and a Dedicated Deep Learning Noise Reduction Algorithm

This study aimed to determine whether the whole-body bone Single Photon Emission Computed Tomography (SPECT) recording times of around 10 minutes, routinely provided by a high-sensitivity 360 degrees cadmium and zinc telluride (CZT) camera, can be further reduced by a deep learning noise reduction (DLNR) algorithm.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

This study aimed to determine the extent to which fast whole-body bone-SPECT recording times, routinely obtained with a high-sensitivity 360 degrees CZT-camera and rather low injected activities, can be further reduced using the DLNR algorithm.

Study Type

Observational

Enrollment (Actual)

19

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • France
      • Vandoeuvre les Nancy cedex, France, 54511
        • CHRU Nancy

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

Study Population

Nineteen cancer patients (8 women, 11 men) referred to fast whole-body bone single photon emission tomography for detection or follow-up of bone metastasis were retrospectively included in this study.

Description

Inclusion Criteria:

  • patients referred to fast whole-body bone single photon emission tomography for detection or follow-up of bone metastasis

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Assess a dedicated deep learning noise reduction algorithm
Time Frame: one day
A deep learning noise reduction algorithm was applied on whole-body images recorded
one day

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Central Hospital, Nancy, France

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 30, 2023

Primary Completion (Actual)

September 10, 2023

Study Completion (Actual)

October 30, 2023

Study Registration Dates

First Submitted

February 20, 2024

First Submitted That Met QC Criteria

February 20, 2024

First Posted (Actual)

February 28, 2024

Study Record Updates

Last Update Posted (Actual)

February 28, 2024

Last Update Submitted That Met QC Criteria

February 20, 2024

Last Verified

February 1, 2024

More Information

Terms related to this study

Other Study ID Numbers

  • 2023PI095-407

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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