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Supervised machine learning quality control for magnetic resonance artifacts in neonatal data sets.

Yang Ding1,2, Sabrina Suffren1,2, Pierre Bellec2,3,4

  • 1Department of Pediatrics, Sainte-Justine University Hospital and University of Montreal, Montreal, Quebec, Canada.

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Summary

Automated quality control for neonatal brain MRI is crucial. This study developed a machine learning method using objective image features to detect major artifacts in 2D neonatal MRI scans, achieving 85% positive predictive value.

Keywords:
Canadian Neonatal Brain PlatformT2wbrain imagingmotion detectionneonatalopen sourcequality control

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Neonatal Neuroscience

Background:

  • Quality control (QC) of brain magnetic resonance images (MRI) is essential but labor-intensive.
  • Automated tools for identifying major artifacts, like subject motion, are lacking, especially for motion-prone neonates in clinical trials.
  • Ensuring data quality in neonatal MRI studies is critical for reliable research outcomes.

Purpose of the Study:

  • To test an open-source, supervised machine learning method for automated artifact detection in 2D neonatal MRI.
  • To evaluate the performance of various classifiers in identifying QC Fail images.
  • To establish a proof of concept for objective, no-reference image feature-based QC.

Main Methods:

  • 1,020 2D transverse T2-weighted neonatal MRI images were classified as QC Pass or QC Fail.
  • 70 image features (focus, texture, noise, natural scene statistics) were extracted.
  • Supervised machine learning classifiers, including RUSBoost, were trained and evaluated against subjective ratings.

Main Results:

  • The RUSBoost classifier achieved the best performance for QC Fail images with 85% positive predictive value and 75% sensitivity.
  • Classification performance was stable across repeated subjective ratings.
  • The study demonstrated the feasibility of predicting QC Fail images using objective features.

Conclusions:

  • An automated QC method using machine learning and objective image features is feasible for neonatal brain MRI.
  • The developed method can help safeguard data quality in large-scale neonatal studies.
  • Evaluating machine learning performance beyond accuracy is vital in imbalanced datasets like this one.