Cardiac rhythm device identification using neural networks

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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
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 (
Sponsor/Funder: Wellcome Trust
Funder's Grant Number: PS3162_WHCP
Keywords: cardiac rhythm devices
machine learning
neural networks
Publication Status: Published
Online Publication Date: 2019-03-27
Appears in Collections:Faculty of Engineering
National Heart and Lung Institute
Faculty of Medicine
Epidemiology, Public Health and Primary Care

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