Artificial Intelligence for Aortic Pressure Waveform Analysis During Coronary Angiography: Machine Learning for Patient Safety

Research output: Contribution to journalArticle

Abstract

Objectives: This study developed a neural network to perform automated pressure waveform analysis and allow real-time accurate identification of damping. Background: Damping of aortic pressure during coronary angiography must be identified to avoid serious complications and make accurate coronary physiology measurements. There are currently no automated methods to do this, and so identification of damping requires constant monitoring, which is prone to human error. Methods: The neural network was trained and tested versus core laboratory expert opinions derived from 2 separate datasets. A total of 5,709 aortic pressure waveforms of individual heart beats were extracted and classified. The study developed a recurrent convolutional neural network to classify beats as either normal, showing damping, or artifactual. Accuracies were reported using the opinions of 2 independent core laboratories. Results: The neural network was 99.4% accurate (95% confidence interval: 98.8% to 99.6%) at classifying beats from the testing dataset when judged against the opinions of the internal core laboratory. It was 98.7% accurate (95% confidence interval: 98.0% to 99.2%) when judged against the opinions of an external core laboratory not involved in neural network training. The neural network was 100% sensitive, with no beats classified as damped misclassified, with a specificity of 99.8%. The positive predictive and negative predictive values were 98.1% and 99.5%. The 2 core laboratories agreed more closely with the neural network than with each other. Conclusions: Arterial waveform analysis using neural networks allows rapid and accurate identification of damping. This demonstrates how machine learning can assist with patient safety and the quality control of procedures.

Details

Authors
  • James P. Howard
  • Christopher M. Cook
  • Tim P. van de Hoef
  • Martijn Meuwissen
  • Guus A. de Waard
  • Martijn A. van Lavieren
  • Mauro Echavarria-Pinto
  • Ibrahim Danad
  • Jan J. Piek
  • Matthias Götberg
  • Rasha K. Al-Lamee
  • Sayan Sen
  • Sukhjinder S. Nijjer
  • Henry Seligman
  • Niels van Royen
  • Paul Knaapen
  • Javier Escaned
  • Darrel P. Francis
  • Ricardo Petraco
  • Justin E. Davies
External organisations
  • Academic Medical Center of University of Amsterdam (AMC)
  • Amphia Hospital
  • Amsterdam UMC - Vrije Universiteit Amsterdam
  • Hospital Clinico San Carlos de Madrid
  • Skåne University Hospital
  • Hammersmith Hospital
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Cardiac and Cardiovascular Systems

Keywords

  • artificial intelligence, coronary angiography, machine learning, neural networks
Original languageEnglish
Pages (from-to)2093-2101
Number of pages9
JournalJACC: Cardiovascular Interventions
Volume12
Issue number20
Publication statusPublished - 2019
Publication categoryResearch
Peer-reviewedYes
Externally publishedYes