Bone ScanIATOS2NCT06782438
Evaluation of an Artificial Intelligence Algorithm Reducing Noise on Fast Whole-body Bone Tomoscintigraphy Acquisitions Recorded by a 360 Degree Cadmium-Zinc-Tellurid Camera
An observational study of Bone Scan, run by Central Hospital, Nancy, France at 1 site in France.
Trial summary
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 Read more about this study
- Who is it for?
- Ages 18 to 99 years
- Conditions the study is for
- Bone Scan
- What is being tested?
- artificial intelligence algorithm
- How many people
- 20 participants
- Where
- 1 siteFrance
- Study dates
- 27 Feb 2025to 30 Mar 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 99 years
- All sexes
- No healthy volunteers
- Adult, Older adult
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
Who may be able to join
- Patients who had a whole-body thee dimensions bone scan for rheumatological or oncological indications.
Who cannot join
- Patients opposed to the use of their data
Trial locations and contacts
1 site in 1 country.
About this study
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
Read the 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.
- Conditions
- Bone Scan
- Being tested
- artificial intelligence algorithm
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
artificial intelligence algorithmOther
to apply an artificial intelligence algorithm to treat the imaging
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: one day
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Jan 2025
- Study start27 Feb 2025
- Primary completion15 Mar 2025
- Study completion30 Mar 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Bone ScanStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 20 participants (actual)
Sponsor and investigators
- Lead sponsor
- Central Hospital, Nancy, France
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Achraf BAHLOUL, Central Hospital, Nancy, France
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06782438
- Sponsor's protocol ID
- 2024PI241
- Acronym
- IATOS2
- Status last verified
- Jun 2026
- First submitted
- 7 Jan 2025
- Last updated
- 25 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Bahloul A, Rajadhas F, Doyen M, Lamash Y, Roth N, Roch V, Marie PY, Imbert L. A deep-learning noise reduction algorithm outperforms the spatial filters previously required for bone SPECT on a high-speed whole-body 360 degrees CZT-camera. EJNMMI Res. 2025 Dec 31;16(1):19. doi: 10.1186/s13550-025-01344-1.
PubMed 41474536
Study at a glance
From the registry record, last updated by the sponsor on 25 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of an Artificial Intelligence Algorithm Reducing Noise on Fast Whole-body Bone Tomoscintigraphy Acquisitions Recorded by a 360 Degree Cadmium-Zinc-Tellurid Camera
- ClinicalTrials.gov ID
- NCT06782438
- Acronym / sponsor ID
- IATOS2 / 2024PI241
- Conditions
- Bone Scan
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 20 participants (actual)
- Lead sponsor
- Central Hospital, Nancy, FranceUniversity, hospital or other
- Study start
- 27 Feb 2025 (actual)
- Primary completion
- 15 Mar 2025 (actual)
- Study completion
- 30 Mar 2025 (actual)
- First posted
- 17 Jan 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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 design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 99 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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
Key inclusion criteria (1 listed)
- Patients who had a whole-body thee dimensions bone scan for rheumatological or oncological indications.
Key exclusion criteria (1 listed)
- Patients opposed to the use of their data
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| artificial intelligence algorithmto apply an artificial intelligence algorithm to treat the imaging | Other | – |
Primary outcome
To compare imaging treated by the intelligence artificial algorithm with imaging treated with the traditionnal filter artificial algorithm
Time frame: one day
Quantification value on imaging measured with intelligence artificial algorithm compared with quantification value measured with conventional filter
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Jun 2026.
- Results publication: PubMed 41474536Bahloul A, Rajadhas F, Doyen M, Lamash Y, Roth N, Roch V, Marie PY, Imbert L. A deep-learning noise reduction algorithm outperforms the spatial filters previously required for bone SPECT on a high-speed whole-body 360 degrees CZT-camera. EJNMMI Res. 2025 Dec 31;16(1):19. doi: 10.1186/s13550-025-01344-1.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Bone Scan, run by Central Hospital, Nancy, France at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 25 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
To compare imaging treated by the intelligence artificial algorithm with imaging treated with the traditionnal filter artificial algorithm (time frame: one day). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06782438, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Jun 2026.