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JACC Clin Electrophysiol. 2019 May;5(5):576-586. doi: 10.1016/j.jacep.2019.02.003. Epub 2019 Mar 27.

Cardiac Rhythm Device Identification Using Neural Networks.

Author information

1
Department of Cardiology, National Heart and Lung Institute, Imperial College London, London, United Kingdom. Electronic address: jphoward@doctors.org.uk.
2
Department of Cardiology, National Heart and Lung Institute, Imperial College London, London, United Kingdom.
3
Department of Cardiology, University College London, London, United Kingdom.
4
Department of Computing, Imperial College London; London, United Kingdom.

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.

KEYWORDS:

cardiac rhythm devices; machine learning; neural networks; pacemaker

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