Predicting Cerebral Palsy in Infants With White Matter Injury Using MRI

Xi'an Jiaotong University

Status: Not yet enrolling

Conditions: Cerebral Palsy; Periventricular White Matter Abnormalities

Treatments: Other: No intervention will be performed in this cohort study

Study type: Observational

Funder types: Other

Identifiers

NCT06575283

XJTU1AF2024LSYY-154

Details and patient eligibility

About

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.

Full 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.

Enrollment: 1,000 estimated patients

Sex: All

Ages: 6 months to 2 years old

Volunteers: Accepts Healthy Volunteers

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).

Trial design

1,000 participants in 1 patient group

PVWMI Infants aged 6 months to 2 years

Description:

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.

Treatment:

Other: No intervention will be performed in this cohort study