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Related Experiment Video

Updated: Sep 6, 2025

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Neural Network Detection of Pacemakers for MRI Safety.

Mark Daniel Vernon Thurston1,2, Daniel H Kim3, Huub K Wit4

  • 1Peninsula Medical School, University of Plymouth, Plymouth Science Park, Plymouth, PL6 8BT, UK. mark@mdvthu.com.

Journal of Digital Imaging
|June 29, 2022
PubMed
Summary
This summary is machine-generated.

A machine learning model accurately detects pacemakers on chest X-rays with 99.67% accuracy. This AI tool enhances patient safety by flagging cardiac devices before MRI scans, improving pre-procedure screening.

Keywords:
Artificial intelligenceCardiac devicesImage classificationMRIPatient safety

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • MRI safety protocols require identifying patients with cardiac devices like pacemakers.
  • Current methods for flagging pacemakers before MRI scans can be time-consuming and prone to error.

Purpose of the Study:

  • To evaluate the accuracy of a machine learning model in classifying the presence or absence of pacemakers on chest radiographs.
  • To explore the potential of AI in enhancing patient safety during MRI procedures.

Main Methods:

  • A dataset of 7973 chest radiographs (3996 with pacemakers, 3977 without) was compiled and manually reviewed by radiologists.
  • A pre-trained image classification neural network was retrained using this dataset, divided into training, validation, and test sets.

Main Results:

  • The machine learning model achieved a high accuracy of 99.67% on the test set for pacemaker detection.
  • Further analysis of misclassified examples was performed to identify areas for potential improvement.

Conclusions:

  • AI-powered image classification can effectively screen for cardiac devices, complementing existing safety questionnaires and improving pre-MRI safety.
  • This approach offers a low-computational-power solution to enhance patient safety in healthcare settings.