Clinical trial NCT06575283: Predicting Cerebral Palsy in Infants With White Matter Injury Using MRI
Official title: Early Prediction of Cerebral Palsy by MRI in Infants With White Matter Injury: a Multicenter Study
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Cerebral Palsy
Periventricular White Matter Abnormalities
Keywords: Cerebral palsy
Periventricular white matter injury
MRI
Deep learning
Lead sponsor: First Affiliated Hospital Xi'an Jiaotong University
Sponsor class: OTHER
Collaborators: name: The First Affiliated Hospital of Henan University of Traditional Chinese Medicine
name: Shenzhen Children's Hospital
name: Zunyi Medical College
name: Wuxi Women's & Children's Hospital
name: Shanxi Provincial Maternity and Children's Hospital
name: Chengdu Medical College
name: First Affiliated Hospital of Xinjiang Medical University
name: Baoji Central Hospital
name: Xian Children's Hospital
name: Guangzhou Women and Children's Medical Center
name: Third Affiliated Hospital of Zhengzhou University
name: Henan Provincial People's Hospital
First posted: 2024-08-28
Enrollment: 1000
Sex: ALL
Minimum age (years): 6
Maximum age (years): 2
Standard ages: CHILD
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study 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.

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.

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.

Eligibility criteria: Inclusion Criteria:

1. 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);
2. Those who underwent MRI at 6 months of age-2 years, including at least T1WI and T2WI sequences;
3. 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).

Exclusion Criteria:

1. Incomplete MRI images or unreadable images due to motion artefacts;
2. Incomplete neurobehavioural assessment data (including: gross motor function).

Primary outcomes:
measure: Accuracy of the model predicting cerebral palsy
timeFrame: From September 2024 to December 2025
Secondary outcomes:
Other outcomes:
Locations:
city: Xi'an
country: China
facility: The First Affiliated Hospital of Xi'an Jiaotong University
state: Shaanxi
Study officials:
affiliation: First Affiliated Hospital Xi'an Jiaotong University
name: Jian Yang, Ph.D.,M.D
role: STUDY_DIRECTOR
References: