Cardiac Rhythm Device Identification Using Neural Networks

Howard JP, Fisher L, Shun-Shin MJ, Keene D, Arnold AD, Ahmad Y, Cook CM, Moon JC, Manisty CH, Whinnett Z, Cole GD, Rueckert D, Francis DP (2019)

Publication Type: Journal article

Publication year: 2019

Journal

JACC Clinical Electrophysiology Elsevier

Book Volume: 5

Pages Range: 576-586

Journal Issue: 5

DOI: 10.1016/j.jacep.2019.02.003

Abstract

Objectives: This paper reports the development, validation, and public availability of a new neural network-based system which attempts to identify the manufacturer and even the model group of a pacemaker or defibrillator from a chest radiograph. Background: Medical staff often need to determine the model of a pacemaker or defibrillator (cardiac rhythm device) quickly and accurately. Current approaches involve comparing a device's radiographic appearance with a manual flow chart. Methods: In this study, radiographic images of 1,676 devices, comprising 45 models from 5 manufacturers were extracted. A convolutional neural network was developed to classify the images, using a training set of 1,451 images. The testing set contained an additional 225 images consisting of 5 examples of each model. The network's ability to identify the manufacturer of a device was compared with that of cardiologists, using a published flowchart. Results: The neural network was 99.6% (95% confidence interval [CI]: 97.5% to 100.0%) accurate in identifying the manufacturer of a device from a radiograph and 96.4% (95% CI: 93.1% to 98.5%) accurate in identifying the model group. Among 5 cardiologists who used the flowchart, median identification of manufacturer accuracy was 72.0% (range 62.2% to 88.9%), and model group identification was not possible. The network's ability to identify the manufacturer of the devices was significantly superior to that of all the cardiologists (p < 0.0001 compared with the median human identification; p < 0.0001 compared with the best human identification). Conclusions: A neural network can accurately identify the manufacturer and even model group of a cardiac rhythm device from a radiograph and exceeds human performance. This system may speed up the diagnosis and treatment of patients with cardiac rhythm devices, and it is publicly accessible online.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) University College London (UCL) GB United Kingdom (GB)

How to cite

APA:

Howard, J.P., Fisher, L., Shun-Shin, M.J., Keene, D., Arnold, A.D., Ahmad, Y.,... Francis, D.P. (2019). Cardiac Rhythm Device Identification Using Neural Networks. JACC Clinical Electrophysiology, 5(5), 576-586. https://doi.org/10.1016/j.jacep.2019.02.003

MLA:

Howard, James P., et al. "Cardiac Rhythm Device Identification Using Neural Networks." JACC Clinical Electrophysiology 5.5 (2019): 576-586.

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