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Related Experiment Video

Updated: Jul 9, 2025

Protocol and Guidelines for Point-of-Care Lung Ultrasound in Diagnosing Neonatal Pulmonary Diseases Based on International Expert Consensus
06:15

Protocol and Guidelines for Point-of-Care Lung Ultrasound in Diagnosing Neonatal Pulmonary Diseases Based on International Expert Consensus

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Classification of lung pathologies in neonates using dual-tree complex wavelet transform.

Sagarjit Aujla1, Adel Mohamed2, Ryan Tan3

  • 1Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada. s1aujla@torontomu.ca.

Biomedical Engineering Online
|December 4, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an automated system for diagnosing neonatal lung pathologies using lung ultrasound (LUS) images. The developed framework significantly improves diagnostic accuracy, aiding clinicians in developing countries.

Keywords:
Image analysisNeonatal lung ultrasoundPattern classificationWavelet decomposition

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neonatal Care

Background:

  • Undiagnosed neonatal lung pathologies cause significant mortality in developing nations.
  • Lung Ultrasound (LUS) is a safe and portable diagnostic tool, but lacks trained interpreters in resource-limited settings.
  • Automated LUS interpretation can enable rapid diagnosis and improve outcomes.

Purpose of the Study:

  • To develop an automated framework for classifying common neonatal lung pathologies from LUS images.
  • To address the diagnostic challenges faced in developing countries due to a shortage of trained clinicians.

Main Methods:

  • Utilized 2D dual-tree complex wavelet transform (DTCWT) to extract spatial and texture patterns from LUS images.
  • Classified six common neonatal lung pathologies using Linear Discriminant Analysis (LDA).
  • Balanced the dataset of 1550 LUS images from 42 neonates to prevent class bias.

Main Results:

  • Achieved 74.39% per-image accuracy on imbalanced data, improving to 92.78% with a balanced dataset.
  • Attained 64.97% per-subject accuracy on imbalanced data, increasing to 81.53% with a balanced dataset.
  • Demonstrated the effectiveness of DTCWT and LDA in classifying neonatal lung pathologies.

Conclusions:

  • The proposed framework can automate neonatal lung pathology diagnosis using LUS.
  • Automated LUS interpretation can reduce neonatal mortality in areas with limited access to trained medical professionals.
  • This technology holds potential for improving neonatal healthcare in developing countries.