Predicting Cerebral Palsy in Infants With White Matter Injury Using MRI

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

1000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-09-01

Study Completion Date

2025-12-31

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

MRI-based Biomarkers for Predicting Punctate White Matter Lesions in Neonates

NCT02637817

Brain Diseases Amblyopia Brain Damage, Chronic +1 more

UNKNOWN

Brain White Matter Injury in Late Preterm Infants

NCT04508517

White Matter Injury

UNKNOWN

Neuroimaging of Babies During Natural Sleep to Assess Typical Development and Cerebral Palsy

NCT06396520

Cerebral Palsy Infant Development Development, Infant

RECRUITING

Risk Factors , Types and Neuroimaging Findings in Patients With Cerebral Palsy at Assiut University Children's Hospital

NCT06928012

Cerebral Palsy

NOT_YET_RECRUITING

A Longitudinal Study of Movement and Participation in Patients With Cerebral Palsy

NCT01903603

Cerebral Palsy

COMPLETED

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Cerebral Palsy Periventricular White Matter Abnormalities

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

PVWMI Infants aged 6 months to 2 years

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.

No intervention will be performed in this cohort study

Intervention Type OTHER

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.

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

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.

Intervention Type OTHER

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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).

Minimum Eligible Age

6 Months

Maximum Eligible Age

2 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes