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
Book Volume: 5
Pages Range: 576-586
Journal Issue: 5
DOI: 10.1016/j.jacep.2019.02.003
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.
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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