Deep Learning on Amyloid Positons Emission Tomography

NCT07309107 | Not Yet Recruiting

• 1 other identifier: 2025PI190
• Study Type: observational
• Target: 40
• Locations: 0 countries
• Sites: N/A

Brief Summary

Reducing injected dose and/or acquisition time in amyloid PET imaging would improve comfort, radiation safety and cost-effectiveness in diagnosis and follow-up of patients. This study evaluates the impact of a deep learning-based noise reduction algorithm on visual analysis and Centiloid quantification when simulating reduced injected doses of \[18F\]flutemetamol.

Conditions

Alzheimer Disease

Outcome Measures

Primary Outcomes (1)

• Evaluate the impact of a deep-learning noise reduction algorithm on visual analysis and centiloid quantification when simulating reduced injected doses of 18F-flutemetamol.

  Visual analysis of the cerebral \[¹⁸F\]flutemetamol PET images will be performed by two nuclear medicine specialists in a blinded manner, with a third reader acting as an arbitrator in case of disagreement, according to routine diagnostic criteria.

  Day one

Eligibility Criteria

• Age: 18 Years - 99 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Patients with objective cognitive impairment, referred to our department for a cerebral \[¹⁸F\]flutemetamol positron emission tomography scan between January 1, 2023 and July 1, 2025, will be included

You may qualify if:

• Patients with objective cognitive impairment,
• Referred to our department for a cerebral \[¹⁸F\]flutemetamol positron emission tomography scan between January 1, 2023 and July 1, 2025,

You may not qualify if:

• Patient have objected to the use of their data.

Sponsors & Collaborators

• Central Hospital, Nancy, France: lead

MeSH Terms

Conditions

Alzheimer Disease

Condition Hierarchy (Ancestors)

Dementia; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Tauopathies; Neurodegenerative Diseases; Neurocognitive Disorders; Mental Disorders

Study Officials

• Antoine VERGER, MD, PhD

  CHRU of NANCY

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Ayaz TAHMAROV, MD

CONTACT

0383153911; a.tahmarov@chru-nancy.fr

Véronique ROCH, MSc

CONTACT

0383154276; v.roch@chru-nancy.fr

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: November 24, 2025
• First Posted: December 30, 2025
• Study Start: January 6, 2026
• Primary Completion: January 20, 2026
• Study Completion: January 30, 2026
• Last Updated: December 30, 2025

Record last verified: 2025-12