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Not yet recruiting NCT06575283

Predicting Cerebral Palsy in Infants With White Matter Injury Using MRI

— Observational Cerebral Palsy Periventricular White Matter Abnormalities

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: No intervention will be performed in this cohort study.
Who it may be relevant to
Registry conditions: Cerebral Palsy, Periventricular White Matter Abnormalities. Basic parameters: 6 months — 2 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Official title

Early Prediction of Cerebral Palsy by MRI in Infants With White Matter Injury: a Multicenter Study

Overview

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.

Interventions

  • 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.

Primary outcome measures

  • Accuracy of the model predicting cerebral palsy [Time frame: From September 2024 to December 2025]

Eligibility criteria

Inclusion criteria

  • 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).

Exclusion criteria

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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model
Cohort

Study locations

China · 1 center
  • The First Affiliated Hospital of Xi'an Jiaotong University — Xi'an

Identifiers

NCT: NCT06575283 · XJTU1AF2024LSYY-154

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗