Artificial Intelligence for Aortic Pressure Waveform Analysis During Coronary Angiography : Machine Learning for Patient Safety
(2019) In JACC: Cardiovascular Interventions 12(20). p.2093-2101- 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... (More)
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.
(Less)
- author
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- artificial intelligence, coronary angiography, machine learning, neural networks
- in
- JACC: Cardiovascular Interventions
- volume
- 12
- issue
- 20
- pages
- 9 pages
- publisher
- Elsevier
- external identifiers
-
- pmid:31563678
- scopus:85073068914
- ISSN
- 1936-8798
- DOI
- 10.1016/j.jcin.2019.06.036
- language
- English
- LU publication?
- no
- id
- 177d09f5-85a0-48c1-a9c3-f69c279de106
- date added to LUP
- 2019-10-21 12:27:38
- date last changed
- 2022-04-18 18:14:19
@article{177d09f5-85a0-48c1-a9c3-f69c279de106,
abstract = {{<p>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.</p>}},
author = {{Howard, James P. and Cook, Christopher M. and van de Hoef, Tim P. and Meuwissen, Martijn and de Waard, Guus A. and van Lavieren, Martijn A. and Echavarria-Pinto, Mauro and Danad, Ibrahim and Piek, Jan J. and Götberg, Matthias and Al-Lamee, Rasha K. and Sen, Sayan and Nijjer, Sukhjinder S. and Seligman, Henry and van Royen, Niels and Knaapen, Paul and Escaned, Javier and Francis, Darrel P. and Petraco, Ricardo and Davies, Justin E.}},
issn = {{1936-8798}},
keywords = {{artificial intelligence; coronary angiography; machine learning; neural networks}},
language = {{eng}},
number = {{20}},
pages = {{2093--2101}},
publisher = {{Elsevier}},
series = {{JACC: Cardiovascular Interventions}},
title = {{Artificial Intelligence for Aortic Pressure Waveform Analysis During Coronary Angiography : Machine Learning for Patient Safety}},
url = {{http://dx.doi.org/10.1016/j.jcin.2019.06.036}},
doi = {{10.1016/j.jcin.2019.06.036}},
volume = {{12}},
year = {{2019}},
}