Evaluation of an Artificial Intelligence Algorithm Reducing Noise on Fast Whole-body Bone Tomoscintigraphy Acquisitions Recorded by a 360 Degree Cadmium-Zinc-Tellurid Camera (IATOS2)

Recently, artificial intelligence algorithms reducing noise by deep learning have been developed with application to SPECT and PET images.

Many studies have reported the possibility of reducing the recording time in bone scintigraphy by applying artificial intelligence algorithms reducing noise

Study Overview

• Status: Recruiting
• Conditions: Bone Scan
• Intervention / Treatment: Other: artificial intelligence algorithm

Detailed Description

Only two studies compared images denoised by a Deep Learning algorithm to those denoised by conventional filters (Gaussian and median filters). The first study was conducted only on patients, without phantom analysis and without taking into account the size of the lesions. The second study included an analysis on phantom and patients, but with application to planar images rather than to SPECT images that are increasingly used today

The hypothesis of our study conducted on phantom and patients is that an artificial intelligence algorithm reducing noise could replace the conventional filters usually used in bone SPECT for the denoising of scintigraphic images.

• Study Type: Observational
• Enrollment (Estimated): 20

Contacts and Locations

• Study Contact: Name: Véronique ROCH, MSc; Phone Number: +33 0383154276; Email: v.roch@chru-nancy.fr
• Study Contact Backup: Name: Achraf BAHLOUL, MD; Phone Number: +33 0383153911; Email: a.bahloul@chru-nancy.fr

Study Locations

• France
  • Vandoeuvre les Nancy cedex, France, 54511
    • Recruiting
    • Nuclear Medicine Department
    • Contact: Veronique Roch, MSc; Phone Number: 0383154276; Email: v.roch@chru-nancy.fr

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Patients who had a whole-body thee dimensions bone scan for rheumatological or oncological indications and non opposed to th use of their data

Description

Inclusion Criteria:

Patients who had a whole-body thee dimensions bone scan for rheumatological or oncological indications.

Exclusion Criteria:

Patients opposed to the use of their data

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
To compare imaging treated by the intelligence artificial algorithm with imaging treated with the traditionnal filter artificial algorithm	Quantification value on imaging measured with intelligence artificial algorithm compared with quantification value measured with conventional filter	one day

Collaborators and Investigators

• Sponsor: Central Hospital, Nancy, France

Study record dates

Study Major Dates

• Study Start (Actual): February 27, 2025
• Primary Completion (Estimated): March 15, 2025
• Study Completion (Estimated): March 30, 2025

Study Registration Dates

• First Submitted: January 7, 2025
• First Submitted That Met QC Criteria: January 14, 2025
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: February 28, 2025
• Last Verified: February 1, 2025

More Information

Terms related to this study

• Other Study ID Numbers: 2024PI241

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No