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

NCT ID: NCT06782438

Last Updated: 2025-03-04

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

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Recruitment Status

RECRUITING

Total Enrollment

20 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-02-27

Study Completion Date

2025-03-30

Brief Summary

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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

Detailed Description

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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.

Conditions

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Bone Scan

Study Design

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Observational Model Type

OTHER

Study Time Perspective

RETROSPECTIVE

Interventions

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artificial intelligence algorithm

to apply an artificial intelligence algorithm to treat the imaging

Intervention Type OTHER

Eligibility Criteria

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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
Minimum Eligible Age

18 Years

Maximum Eligible Age

99 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Central Hospital, Nancy, France

OTHER

Sponsor Role lead

Responsible Party

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Achraf BAHLOUL

Principal investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Nuclear Medicine Department

Vandœuvre-lès-Nancy, , France

Site Status RECRUITING

Countries

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France

Central Contacts

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Achraf BAHLOUL, MD

Role: CONTACT

0383153911 ext. +33

Véronique ROCH, MSc

Role: CONTACT

0383154276 ext. +33

Facility Contacts

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VERONIQUE ROCH, MSc

Role: primary

0383154276

Other Identifiers

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2024PI241

Identifier Type: -

Identifier Source: org_study_id

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