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Related Experiment Video

Updated: May 6, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

CLIF-Net: Intersection-Guided Cross-View Fusion Network for Infection Detection From Cranial Ultrasound.

Mingzhao Yu, Mallory R Peterson, Kathy Burgoine

IEEE Transactions on Medical Imaging

|May 15, 2025

Summary

This summary is machine-generated.

A new AI framework, CLIF-Net, enhances detection of serious bacterial infection in newborns using multi-view cranial ultrasound images. This method improves diagnostic accuracy for early sepsis detection in infants.

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Area of Science:

Medical Imaging
Artificial Intelligence
Neonatal Care

Background:

Serious bacterial infection (pSBI) in infants poses a significant diagnostic challenge.
Cranial ultrasound (cUS) is a valuable tool for neonatal imaging.
Existing methods for pSBI detection using cUS have limitations.

Purpose of the Study:

To develop a novel deep learning framework for improved pSBI detection in newborns.
To leverage multi-view cUS images (coronal and sagittal) for enhanced diagnostic accuracy.
To create a robust 3D representation for pSBI detection.

Main Methods:

Developed the intersection-guided Crossview Local- and Image-level Fusion Network (CLIF-Net).
Employed dual convolutional neural network branches for coronal and sagittal image feature extraction.
Utilized multi-level fusion blocks with cross-attention modules to enhance intersecting region features.

Main Results:

CLIF-Net demonstrated substantially enhanced performance in pSBI detection.
The method surpassed prevailing state-of-the-art infection detection techniques.
Evaluated on a dataset of 302 cUS scans from Uganda.

Conclusions:

Exploiting multi-view cUS images with CLIF-Net provides a robust 3D representation for pSBI detection.
The developed framework offers a promising advancement in diagnosing neonatal sepsis.
This approach has the potential to improve early detection and management of serious bacterial infections in infants.