Predicting Cerebral Palsy in Infants With White Matter Injury Using MRI
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age6-2
SponsorFirst Affiliated Hospital Xi'an Jiaotong University
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
Infants and children at high risk of periventricular white matter injury (PVWMI) (gestational age <35 weeks, birth weight <2.6 kg, forceps-assisted delivery/fetal head attraction, Apgar score <7, hypoglycaemia, sepsis, electrolyte disturbances, premature rupture of membranes);
Those who underwent MRI at 6 months of age-2 years, including at least T1WI and T2WI sequences;
Upon follow-up, the patient's clinical diagnosis: cerebral palsy, other diagnoses that did not develop into cerebral palsy, or inability to confirm the diagnosis).
Disqualifiers
Incomplete MRI images or unreadable images due to motion artefacts;
Incomplete neurobehavioural assessment data (including: gross motor function).
Trial population
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.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
No intervention will be performed in this cohort study
Other interventionDeep learning classification models will be used for automatic prediction of cerebral palsy. Machines will be used to assist doctors in cerebral palsy risk evaluation.
Treatment groups
1,000 Participants
are divided into 1 treatment groupGroup A: PVWMI Infants aged 6 months to 2 years1 intervention
Trial outcomes
Primary outcomes
1
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 2025Sponsors and contacts
Click on the lead sponsor to view all of their trials.
First Affiliated Hospital Xi'an Jiaotong University
Lead sponsor
The First Affiliated Hospital of Henan University of Traditional Chinese Medicine
Collaborator
Shenzhen Children's Hospital
Collaborator
Zunyi Medical College
Collaborator
Wuxi Women's & Children's Hospital
Collaborator
Shanxi Provincial Maternity and Children's Hospital
Collaborator
Chengdu Medical College
Collaborator
First Affiliated Hospital of Xinjiang Medical University
Collaborator
Baoji Central Hospital
Collaborator
Xian Children's Hospital
Collaborator
Guangzhou Women and Children's Medical Center
Collaborator
Third Affiliated Hospital of Zhengzhou University
Collaborator
Henan Provincial People's Hospital
Collaborator
Source ClinicalTrials.gov
This trial is not recruiting at the moment. You can still explore other options: