Altmetric

Cardiac rhythm device identification using neural networks

File Description SizeFormat 
1-s2.0-S2405500X19301446-main.pdfPublished version1.58 MBAdobe PDFView/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:Faculty of Engineering
Computing
National Heart and Lung Institute
Faculty of Medicine
Epidemiology, Public Health and Primary Care



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons