Ignite Creation Date:
2024-10-26 @ 3:39 PM
Last Modification Date:
2024-10-26 @ 3:39 PM
Study NCT ID:
NCT06575283
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-08-26
Brief Title:
Predicting Cerebral Palsy in Infants With White Matter Injury Using MRI
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Early Prediction of Cerebral Palsy by MRI in Infants With White Matter Injury a Multicenter Study
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The goal of this study is to determin the MRI features associated with cerebral palsy and to develop prediction models of pediatric disorders by combining MRI with artificial intelligence
The main questions it aims to answer are
How to achieve features on conventional MRI associated with cerebral palsy
How to predict the risk of cerebral palsy in infants aged 6 to 2 years based on conventional MRI and deep learning Researchers will compare characteristics of periventricular white matter injury with cerebral palsy to those without cerebral palsy
Participants will be asked to provide MRI data clinical diagnoses information and follow-up outcomes
The main questions it aims to answer are
How to achieve features on conventional MRI associated with cerebral palsy
How to predict the risk of cerebral palsy in infants aged 6 to 2 years based on conventional MRI and deep learning Researchers will compare characteristics of periventricular white matter injury with cerebral palsy to those without cerebral palsy
Participants will be asked to provide MRI data clinical diagnoses information and follow-up outcomes
Detailed Description:
Cerebral palsy CP is a common group of movement disorders that often results in disability in children In the context of CP the importance of early diagnosis is crucial but current diagnostic modalities often identify cases after the age of 2 years After initial screening of infants at high risk for CP by behavioral scoring magnetic resonance imaging MRI forms an integral part of the comprehensive evaluation The training of conventional model of CP risk prediction requires a large investment of time and financial resources The average sensitivity rate drops to 90 Up to now deep learning technology has been widely used in tasks related to image-based disease classification and has shown excellent performance
Periventricular white matter injury PVWMI accounts for the largest proportion of various types of brain injuries in cerebral palsy and the types of brain injuries in cerebral palsy are rich and complex posing difficulties and challenges to deep learning models Therefore this study focuses on PVWMI the most common type of cerebral palsy and uses conventional MRI to develop a deep learning prediction model for CP in infants aged 6 months to 2 years old
Periventricular white matter injury PVWMI accounts for the largest proportion of various types of brain injuries in cerebral palsy and the types of brain injuries in cerebral palsy are rich and complex posing difficulties and challenges to deep learning models Therefore this study focuses on PVWMI the most common type of cerebral palsy and uses conventional MRI to develop a deep learning prediction model for CP in infants aged 6 months to 2 years old
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None