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Auto-evaluation of skull radiograph accuracy using unsupervised anomaly detection.

Haruyuki Watanabe1, Yuina Ezawa1, Eri Matsuyama2

  • 1School of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.

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Summary

This study introduces an automated method using unsupervised learning (autoencoder and variational autoencoder) to assess skull radiograph quality. The AI model accurately identifies radiographs needing retakes, improving efficiency in medical imaging operations.

Keywords:
Anomaly detectionautoencoderskull radiographunsupervised learningvariational autoencoder

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiography

Background:

  • Accurate patient positioning in radiography is crucial for diagnostic image quality.
  • Suboptimal radiographs are often rejected, necessitating repeat imaging, which is currently a subjective process.
  • Automating the assessment of radiograph quality can enhance efficiency and consistency.

Purpose of the Study:

  • To develop and evaluate an automated system for assessing skull radiograph accuracy using unsupervised machine learning.
  • To replace qualitative, visual assessment of radiograph suitability with a quantitative, data-driven approach.
  • To utilize autoencoder (AE) and variational autoencoder (VAE) models for anomaly detection in radiographic images.

Main Methods:

  • Acquired 1,680 skull radiograph images from five phantoms, categorized as normal (appropriate positioning) or abnormal (inappropriate positioning).
  • Employed anomaly detection techniques based on AE and VAE models to discriminate between acceptable and unacceptable radiographs.
  • Verified the discriminatory capability of the unsupervised learning models on the acquired dataset.

Main Results:

  • The autoencoder (AE) model achieved an area under the curve (AUC) of 0.7060 in receiver operating characteristic analysis.
  • The variational autoencoder (VAE) model achieved an AUC of 0.6707.
  • The proposed unsupervised learning methods demonstrated superior discrimination ability compared to previous studies with 52% accuracy.

Conclusions:

  • The developed method exhibits high classification accuracy for determining the need for skull radiograph retakes.
  • Automating the assessment of radiograph quality can significantly improve operational efficiency in high-volume X-ray departments.
  • This approach offers a quantitative and objective alternative to manual radiograph evaluation.