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Cardiac rhythm device identification using neural networks
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![]() | Published version | 1.58 MB | Adobe PDF | View/Open |
Title: | Cardiac rhythm device identification using neural networks |
Authors: | Howard, J Fisher, L Shun-Shin, M Keene, D Arnold, A Ahmad, Y Cook, C Moon, J Manisty, C Whinnett, Z Cole, G Rueckert, D Francis, D |
Item Type: | Journal Article |
Abstract: | Background Medical staff often need to determine the model of a pacemaker or defibrillator (cardiac rhythm devices) quickly and accurately. Current approaches involve comparing a device’s X-ray appearance with a manual flow chart. We aimed to see whether a neural network could be trained to perform this task more accurately. Methods and Results We extracted X-ray images of 1676 devices, comprising 45 models from 5 manufacturers. We developed a convolutional neural network to classify the images, using a training set of 1451 images. The testing set was a further 225 images, consisting of 5 examples of each model. We compared the network’s ability to identify the manufacturer of a device with those of cardiologists using a published flow-chart. The neural network was 99.6% (95% CI 97.5 to 100) accurate in identifying the manufacturer of a device from an X-ray, and 96.4% (95% CI 93.1 to 98.5) accurate in identifying the model group. Amongst 5 cardiologists using the flow-chart, median manufacturer accuracy was 72.0% (range 62.2% to 88.9%), and model group identification was not possible. The network was significantly superior to all of the cardiologists in identifying the manufacturer (p < 0.0001 against the median human; p < 0.0001 against the best human). Conclusions A neural network can accurately identify the manufacturer and even model group of a cardiac rhythm device from an X-ray, 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. |
Issue Date: | 1-May-2019 |
Date of Acceptance: | 14-Feb-2019 |
URI: | http://hdl.handle.net/10044/1/67610 |
DOI: | https://dx.doi.org/10.1016/j.jacep.2019.02.003 |
ISSN: | 2405-5018 |
Publisher: | Elsevier |
Start Page: | 576 |
End Page: | 586 |
Journal / Book Title: | JACC: Clinical Electrophysiology |
Volume: | 5 |
Issue: | 5 |
Copyright Statement: | © 2019 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC-BY license (http://creativecommons.org/licenses/by/4.0/). |
Sponsor/Funder: | Wellcome Trust |
Funder's Grant Number: | PS3162_WHCP |
Keywords: | cardiac rhythm devices machine learning neural networks pacemaker |
Publication Status: | Published |
Online Publication Date: | 2019-03-27 |
Appears in Collections: | Computing National Heart and Lung Institute Faculty of Engineering |