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Improving image quality on pediatric and neonatal radiography using AI-based compensation for image degradation

So Ode1, Atsuko Fujikawa2, Atsushi Hiroishi2

  • 1Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan. so.ode@marianna-u.ac.jp.

Japanese Journal of Radiology
|April 7, 2025
PubMed

Insights

Artificial intelligence-based noise reduction significantly improved image quality in pediatric and neonatal radiography. This AI technique enhances diagnostic accuracy for chest and abdomen X-rays.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Pediatric Radiology

Background:

  • Image quality in pediatric and neonatal radiography is crucial for accurate diagnosis.
  • Image degradation can occur, necessitating effective noise reduction techniques.
  • Conventional noise reduction methods may have limitations.

Purpose of the Study:

  • To evaluate the effectiveness of an AI-based noise reduction technique.
  • To assess its impact on image quality in pediatric and neonatal chest and abdomen radiography.
  • To compare AI-based noise reduction with conventional methods using visual grading analysis.

Main Methods:

  • Forty-six pediatric and neonatal chest X-rays were analyzed.
  • Images were processed using an AI-based noise reduction technique (Intelligent NR).
  • Three radiologists assessed image quality using a 4-point scale; data analyzed with Wilcoxon signed rank test.

Main Results:

  • Images processed with AI-based noise reduction demonstrated significantly higher image quality compared to conventional methods.
  • A statistically significant difference (p < 0.05) was observed between the two processing groups.
  • The AI technique effectively compensated for image degradation.

Conclusions:

  • AI-based noise reduction significantly enhances image quality in pediatric and neonatal radiography.
  • This technology offers a promising tool for improving diagnostic confidence and accuracy.
  • The findings support the clinical utility of AI for medical imaging enhancement.
Abstract

Keywords:
Artificial intelligenceNoise reductionPediatric radiologyRadiography

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