An observational study in Cerebral Palsy and Periventricular White Matter Abnormalities, sponsored by First Affiliated Hospital Xi'an Jiaotong University. Not yet recruiting at 1 site in China. Open to participants aged 6 Months to 2 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2024-09-19.
Sponsored by First Affiliated Hospital Xi'an Jiaotong University · Observational
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:
Participants will be asked to provide MRI data, clinical diagnoses information, and follow-up outcomes.
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.
750 studies on the registry are indexed under Paralysis; 133 are open to participants now.
This study's planned enrollment of 1,000 is above the median of 60 across 199 observational studies indexed under Paralysis.
Browse Paralysis studies →First Affiliated Hospital Xi'an Jiaotong University is the lead sponsor of 306 studies on the registry; 86 are open to participants now.
Counted across the registry records on this site, refreshed daily.
This study will follow up infants from multiple regions and different hospitals who underwent MRI examinations between 6 months and 2 years old. Each infant and young child included T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI). According to the follow-up outcomes, these infants will be divided into the following groups: cerebral palsy, other diagnoses that did not develop into cerebral palsy, or inability to confirm the diagnosis.
Exclusion Criteria:
Infants will be scanned by MRI at the age of 6 months to 2 years. The infants of periventricular white matter injury (PVWMI) will be enrolled.
Other: No intervention will be performed in this cohort study
Deep learning classification models will be used for automatic prediction of cerebral palsy. Machines will be used to assist doctors in cerebral palsy risk evaluation.
Accuracy of the model predicting cerebral palsy
Determine the accuracy of PVWMI classification and cerebral palsy prediction. The higher the value, the better the model performance.
Time frame: From September 2024 to December 2025
Plan to share: Yes — The data will be available from the corresponding author upon reasonable request.
Supporting information: Study protocol, Sap, Icf, Csr, Analytic code
No publications or documents are linked to this record.
This study is not yet recruiting, as verified in Aug 2024. You cannot join it, but the record below documents what was studied.
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First Affiliated Hospital Xi'an Jiaotong University