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CLIF-Net: Intersection-guided Cross-view Fusion Network for Infection Detection from Cranial Ultrasound.

Mingzhao Yu1, Mallory R Peterson2, Kathy Burgoine3

  • 1Department of Electrical Engineering, Pennsylvania State University, University Park, PA 16802 USA.

Medrxiv : the Preprint Server for Health Sciences
|August 8, 2025
PubMed
Summary
This summary is machine-generated.

Detecting serious bacterial infection in infants using cranial ultrasound (cUS) is crucial. A new AI framework, CLIF-Net, effectively analyzes multi-view cUS images for improved infant infection detection.

Keywords:
Cross-view fusiondeep learninggeometric priorinfection detectionultrasound

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neonatal Health

Background:

  • Early detection of possible serious bacterial infection (pSBI) in infants is critical for timely treatment and improved outcomes.
  • Cranial ultrasound (cUS) is a valuable imaging modality for neonatal assessment, providing multi-view data (coronal and sagittal).
  • Existing methods for pSBI detection using cUS may not fully exploit the rich information available from multi-view imaging.

Purpose of the Study:

  • To develop and evaluate a novel deep learning framework, CLIF-Net, for enhanced detection of pSBI in infants using multi-view cUS images.
  • To leverage the geometric overlap between coronal and sagittal cUS views to create a robust 3D representation for improved diagnostic accuracy.
  • To improve upon state-of-the-art methods for identifying serious bacterial infections in newborns.

Main Methods:

  • Development of the intersection-guided Cross-view Local- and Image-level Fusion Network (CLIF-Net), a deep learning framework utilizing two distinct convolutional neural network branches.
  • Implementation of multi-level fusion blocks with cross-attention modules to extract and enhance semantic features from intersecting regions of coronal and sagittal cUS images.
  • Integration of enhanced features through an image-level fusion layer to output class probabilities for pSBI and non-pSBI.

Main Results:

  • CLIF-Net demonstrated substantially enhanced performance in detecting pSBI compared to existing techniques.
  • The framework effectively exploited multi-view cUS imagery, creating a robust 3D representation for pSBI detection.
  • Evaluation on a dataset of 302 cUS scans from Uganda showed superior results, surpassing current state-of-the-art infection detection methods.

Conclusions:

  • The CLIF-Net framework offers a novel and effective approach for pSBI detection in infants by utilizing multi-view cUS data.
  • Exploiting the geometric relationship and cross-view features significantly improves the accuracy of infection detection in newborns.
  • This method represents a significant advancement in leveraging AI for neonatal infection diagnosis, with potential for broader clinical application.