Quantitative 3D UltraSound Brain Imaging: Development of New Bedside Biomarkers to Better Predict Neurodevelopmental Outcomes in Preterm Infants (QUSBI)

Prematurity is a leading cause of neurodevelopmental disorders (NDDs) tightly associated with white matter damage, including punctate white matter lesions (PWMLs). Hence, an improved detection of brain injury early in life in infants born very preterm is a top priority to predict NDDs and therefore to assess potential neuroprotective strategies and implement early interventions. 3D and quantitative tools at the bedside using ultrasound are expected to better detect and quantify not only PWMLs but also other brain structures with promising prognostic value to predict NDDs at 2 years of age.

Study Overview

• Status: Not yet recruiting
• Conditions: Very Preterm Infants Born < 30 Weeks of Gestation
• Intervention / Treatment: Radiation: Cranial ultrasound; Radiation: Brain MRI; Other: PARCA-R questionnaire

Detailed Description

Rationale :

Prematurity is a leading cause of neurodevelopmental disorders (NDDs). In Europe, 10% of the 50 000 children born very preterm will develop cerebral palsy and 35% will experience persistent cognitive and neuropsychiatric disorders, including autism, requiring long-term health care support. NDDs following preterm birth are tightly associated with white matter damage, including punctate white matter lesions (PWMLs), a promising marker of subsequent NDDs affecting up to 24% of very preterm infants. Hence, an improved detection of brain injury early in life in infants born very preterm is a top priority to predict NDDs and therefore to assess potential neuroprotective strategies and implement early interventions. Magnetic Resonance Imaging (MRI) is the gold standard to assess white matter integrity and has revealed an association between thalamus structure, ventricular dilatation, white matter damage, and NDDs at preschool age in infants born very preterm. However, it suffers from limited accessibility, low portability, and high sensitivity to motion artifacts mitigating its value for screening in all preterm infants at risk of NDDs. Conversely, conventional 2D cranial ultrasonography (cUS) is a tool widely used in neonatal intensive care units to prospectively screen brain lesions through the anterior fontanel. However, while suitable to detect severe lesions observed in 4-5% of very premature infants only, it remains less reliable for the comprehensive detection of PWMLs despite recent improvements. Hence, 3D and quantitative tools at the bedside using ultrasound must be developed to automatically detect and quantify not only PWMLs but also other brain structures with promising prognostic value.

Main objective:

Identifying new early imaging biomarkers to predict NDDs at 2 years of age.

Primary endpoint:

Correlation between 3D quantification of PWMLs burden collected between day 3±1 and day 21±3, and results of Bayley Scale assessment, 4th edition, French edition (2022) at 2 years of age and the Parent Report of Children's Abilities-Revised (PARCA-R) questionnaire, which identifies preterm children at risk for developmental delays at 24 months of age.

Secondary objectives:

• Developing a processing pipeline allowing the reconstruction of 3D cUS volume & automatic detection and segmentation algorithms based on deep learning methods.
• Validating these models to detect PWMLs, & segment thalami and cerebral ventricular system (CVS).
• Correlating thalami and CVS 3D longitudinal volumes in very preterm infants with & without PWMLs

Secondary endpoints:

• Longitudinal cUS data will allow the generation of standard growth curves for thalami and CVS volumes, stratified by sex and dependent on the concurrent detection of PWMLs.

• Correlation between 3D quantification of PWMLs burden collected between day 3±1 and day 21±3, and results of the Parent Report of Children's Abilities-Revised (PARCA-R) questionnaire.

  3D quantification of PWMLs and other 3D cUS tissue segmentations generated using new algorithms developed in WP3 as predictors of (i) MRI findings at term and (ii) neurodevelopmental outcomes at 2 years of age.

Impact :

This project will improve the quality of care by extending advanced quantitative brain imaging to all very preterm infants and improving the early diagnosis of brain injury. It is also expected to promote personalized medicine approaches and support translational research in neuroprotection through the development of new biomarkers for brain injury. Socio-economic impacts include cost reduction through early identification of at-risk children and timely interventions to prevent severe NDD. QUSBI will also have a significant impact on family support.

• Study Type: Interventional
• Enrollment (Estimated): 360
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Olivier BAUD, MD, PhD; Phone Number: +33 01 58 41 24 23; Email: olivier.baud@aphp.fr
• Study Contact Backup: Name: Adèle BELLINO; Phone Number: +33 01 71 76 07 57; Email: adele.bellino@aphp.fr

Study Locations

• France
  • Île-de-France Region
    • Paris, Île-de-France Region, France, 75014
      • Hôpital Cochin Port-Royal, APHP Centre
      • Contact: Olivier BAUD, MD, PhD; Phone Number: +33 01 58 41 24 23; Email: olivier.baud@aphp.fr

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Very preterm infants delivered (either inborn or outborn) between 23+0 and 29+6 weeks of gestation
• Informed written consent of the holders of parental authority.

Exclusion Criteria:

• Admission for palliative care
• Chromosomal aberrations and major malformations evidenced after birth Major malformations
• Chromosomal aberrations
• No social security coverage

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Identifying new early imaging biomarkers to predict NDDs at 2 years of age.	Correlation between 3D quantification of PWMLs burden, and results of Bayley Scale assessment, at 2 years of age and the Parent Report of Children's Abilities-Revised (PARCA-R) questionnaire, which identifies preterm children at risk for developmental delays at 24 months of age.	2 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Developing a processing pipeline allowing the reconstruction of 3D cUS volume & automatic detection and segmentation algorithms based on deep learning methods.	Longitudinal cUS data will allow the generation of standard growth curves for thalami and CVS volumes, stratified by sex and dependent on the concurrent detection of PWMLs	Between day 3±1 and day 21±3
Validating these models to detect PWMLs, & segment thalami and cerebral ventricular system (CVS).	Correlation between 3D quantification of PWMLs burden collected between between day 3±1 and day 21±3 , day 3±1 and term equivalent age, and results of the Parent Report of Children's Abilities-Revised (PARCA-R) questionnaire.	2 years
Correlating thalami and CVS 3D longitudinal volumes in very preterm infants with & without PWMLs	3D quantification of PWMLs and other 3D cUS tissue segmentations generated using new algorithms developed in WP3 as predictors of (i) MRI findings at term and (ii) neurodevelopmental outcomes at 2 years of age	2 years

Collaborators and Investigators

• Sponsor: Assistance Publique - Hôpitaux de Paris
• Collaborators: URC-CIC Paris Descartes Necker Cochin
• Investigators: Principal Investigator: Olivier BAUD, MD, PhD, Assistance Publique - Hopitaux de Paris

Study record dates

Study Major Dates

• Study Start (Estimated): April 1, 2026
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): April 1, 2031

Study Registration Dates

• First Submitted: January 14, 2026
• First Submitted That Met QC Criteria: January 14, 2026
• First Posted (Actual): January 22, 2026

Study Record Updates

• Last Update Posted (Actual): February 18, 2026
• Last Update Submitted That Met QC Criteria: February 16, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: Artificial intelligence; Neurodevelopment; Prediction; Very preterm infants; White matter lesions; 3D Cranial Ultrasound
• Other Study ID Numbers: APHP251518; IDRCB (Other Identifier: 2025-A01568-41)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No