Cerebral PalsyNCT06575283
Predicting Cerebral Palsy in Infants With White Matter Injury Using MRI
An observational study of Cerebral Palsy and Periventricular White Matter Abnormalities, run by First Affiliated Hospital Xi'an Jiaotong University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 6 months to 2 yearsHealthy volunteers can take part
- Conditions the study is for
- Cerebral Palsy, Periventricular White Matter Abnormalities
- How many people
- 1,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Sep 2024to 31 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 6 months to 2 years
- All sexes
- Healthy volunteers accepted
- Child
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.
Who may be able to join
- 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).
Who cannot join
- Incomplete MRI images or unreadable images due to motion artefacts;
- Incomplete neurobehavioural assessment data (including: gross motor function).
Trial locations and contacts
1 site in 1 country.
China1 site
The First Affiliated Hospital of Xi'an Jiaotong University
Xi'an, Shaanxi
Jian Yang, Ph.D.,M.D., 0086-18991232396, cjr.yangjian@vip.163.com
About this study
Official title: Early Prediction of Cerebral Palsy by MRI in Infants With White Matter Injury: a Multicenter Study
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.
Read the 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.
- Being tested
- No intervention will be performed in this cohort study
- Keywords
- Cerebral palsy, Periventricular white matter injury, MRI, Deep learning
- Medical subject headings
- Cerebral Palsy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No intervention will be performed in this cohort studyOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: No intervention will be performed in this cohort study |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: From September 2024 to December 2025
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Aug 2024
- Study start1 Sep 2024 (estimated)
- Primary completion31 Dec 2025 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cerebral PalsyStudies of the same condition, open and under way.
- More trials run by First Affiliated Hospital Xi'an Jiaotong UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Shenzhen Children's Hospital, Zunyi Medical College, Wuxi Women's & Children's Hospital, Shanxi Provincial Maternity and Children's Hospital, Chengdu Medical College, First Affiliated Hospital of Xinjiang Medical University, Baoji Central Hospital, Xian Children's Hospital, Guangzhou Women and Children's Medical Center, Third Affiliated Hospital of Zhengzhou University, Henan Provincial People's Hospital
- Responsible party
- Sponsor
- Investigators
- Jian Yang, Ph.D.,M.D, Study director, First Affiliated Hospital Xi'an Jiaotong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06575283
- Sponsor's protocol ID
- XJTU1AF2024LSYY-154
- Status last verified
- Aug 2024
- First submitted
- 26 Aug 2024
- Last updated
- 19 Sep 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The data will be available from the corresponding author upon reasonable request.
Study at a glance
From the registry record, last updated by the sponsor on 19 Sep 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Early Prediction of Cerebral Palsy by MRI in Infants With White Matter Injury: a Multicenter Study
- ClinicalTrials.gov ID
- NCT06575283
- Acronym / sponsor ID
- XJTU1AF2024LSYY-154
- Keywords
- Cerebral palsy, Periventricular white matter injury, MRI, Deep learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- First Affiliated Hospital Xi'an Jiaotong UniversityUniversity, hospital or other
- Collaborators
- The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Shenzhen Children's Hospital, Zunyi Medical College, Wuxi Women's & Children's Hospital, Shanxi Provincial Maternity and Children's Hospital, Chengdu Medical College, First Affiliated Hospital of Xinjiang Medical University, Baoji Central Hospital, Xian Children's Hospital, Guangzhou Women and Children's Medical Center, Third Affiliated Hospital of Zhengzhou University, Henan Provincial People's Hospital
- Study start
- 1 Sep 2024 (estimated)
- Primary completion
- 31 Dec 2025 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 28 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| PVWMI Infants aged 6 months to 2 years | – | Other: No intervention will be performed in this cohort studyInfants 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. |
Who may qualify
- Ages
- 6 months to 2 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
- 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.
Key inclusion criteria (3 listed)
- 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).
Key exclusion criteria (2 listed)
- Incomplete MRI images or unreadable images due to motion artefacts;
- Incomplete neurobehavioural assessment data (including: gross motor function).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No intervention will be performed in this cohort studyDeep learning classification models will be used for automatic prediction of cerebral palsy. Machines will be used to assist doctors in cerebral palsy risk evaluation. | Other | PVWMI Infants aged 6 months to 2 years |
Primary outcome
Accuracy of the model predicting cerebral palsy
Time frame: From September 2024 to December 2025
Determine the accuracy of PVWMI classification and cerebral palsy prediction. The higher the value, the better the model performance.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Sep 2024.
- Individual participant dataSharing planned: Yes. The data will be available from the corresponding author upon reasonable request.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jian Yang, Ph.D.,M.D study director
First Affiliated Hospital Xi'an Jiaotong University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cerebral Palsy and Periventricular White Matter Abnormalities, run by First Affiliated Hospital Xi'an Jiaotong University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 19 Sep 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Yitong Bian, MD (15209220323, bianyt0323@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of the model predicting cerebral palsy (time frame: From September 2024 to December 2025). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Sep 2024.