Modelling Tau Distribution From DTI With Generative Adversarial Network for Alzheimer's Disease Diagnosis
NCT ID: NCT05020626
Last Updated: 2024-08-22
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
250 participants
Study Classification
OBSERVATIONAL
Study Start Date
2021-06-30
Study Completion Date
2025-12-31
Brief Summary
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The most significant impact of this project is to propose for the first time a novel generative adversarial network (GAN), as one kind of deep learning architecture, to automatically generate synthetic PET images reflecting tau deposition, from brain DTI images. If successful, this framework will become the most state-of-the-art approach to simulate the stereotypical pattern of intracerebral tau accumulation and distribution in vivo.
Synthetic tau-PET images via DTI, possessing overwhelming superiority in radiation-free, non-invasiveness and cost-effectiveness, will potentially serve as one of alternative modalities of PET in detecting tau-load and probably outperform PET on accessibility, generalizability, and availability in future, making it much more attractive in clinical application. A big conceptual shift may occur preferring a fire-new tau-PET simulated via DTI.
The DTI data-driven deep learning framework to be created in this project will constitute an accurate, robust, clinically applicable and explainable tool to efficiently categorize the subjects into tau-burden positive and tau-burden negative cases, which will undoubtedly contribute to both clinical and research activities.
Synthetic tau-PET images via DTI, possessing overwhelming superiority in radiation-free, non-invasiveness and cost-effectiveness, will potentially serve as one of alternative modalities of PET in detecting tau-load and probably outperform PET on accessibility, generalizability, and availability in future, making it much more attractive in clinical application. A big conceptual shift may occur preferring a fire-new tau-PET simulated via DTI.
The DTI data-driven deep learning framework to be created in this project will constitute an accurate, robust, clinically applicable and explainable tool to efficiently categorize the subjects into tau-burden positive and tau-burden negative cases, which will undoubtedly contribute to both clinical and research activities.
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Detailed Description
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Conditions
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Alzheimer's Disease Diagnosis
Study Design
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Observational Model Type
COHORT
Study Time Perspective
RETROSPECTIVE
Study Groups
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Controls
No interventions assigned to this group
Mild cognitive impairment
No interventions assigned to this group
AD dementia
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* With the age of 55 years and above
* With brain MRI taken within ±6 months from the date of clinically confirmed diagnosis of AD, MCI or normal cognition.
* With brain MRI taken within ±6 months from the date of clinically confirmed diagnosis of AD, MCI or normal cognition.
Exclusion Criteria
* AD with mixed dementia
* Non-AD dementia
* History of severe traumatic brain injury, severe depression, stroke, brain tumors, and incident major systemic illness
* Non-AD dementia
* History of severe traumatic brain injury, severe depression, stroke, brain tumors, and incident major systemic illness
Minimum Eligible Age
55 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
Yes
Sponsors
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Chinese University of Hong Kong
OTHER
Sponsor Role
lead
Responsible Party
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Professor Winnie W.C. Chu
Professor
Responsibility Role
PRINCIPAL_INVESTIGATOR
Locations
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The Chinese University of Hong Kong, Prince of Wale Hospital
Hong Kong, Shatin, Hong Kong
Site Status
RECRUITING
Countries
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Hong Kong
Facility Contacts
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References
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Other Identifiers
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2021.243
Identifier Type: -
Identifier Source: org_study_id
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