Imperial College London
Alternate

ProfessorDarrelFrancis

Faculty of Medicine, National Heart & Lung Institute

Professor of Cardiology
 
 
 
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Contact

 

+44 (0)20 7594 3381d.francis Website

 
 
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Assistant

 

Miss Juliet Holmes +44 (0)20 7594 5735

 
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Location

 

Block B Hammersmith HospitalHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Howard:2019:10.1016/j.jacep.2019.02.003,
author = {Howard, J and Fisher, L and Shun-Shin, M and Keene, D and Arnold, A and Ahmad, Y and Cook, C and Moon, J and Manisty, C and Whinnett, Z and Cole, G and Rueckert, D and Francis, D},
doi = {10.1016/j.jacep.2019.02.003},
journal = {JACC: Clinical Electrophysiology},
pages = {576--586},
title = {Cardiac rhythm device identification using neural networks},
url = {http://dx.doi.org/10.1016/j.jacep.2019.02.003},
volume = {5},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BackgroundMedical 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 ResultsWe 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).ConclusionsA 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.
AU  - Howard,J
AU  - Fisher,L
AU  - Shun-Shin,M
AU  - Keene,D
AU  - Arnold,A
AU  - Ahmad,Y
AU  - Cook,C
AU  - Moon,J
AU  - Manisty,C
AU  - Whinnett,Z
AU  - Cole,G
AU  - Rueckert,D
AU  - Francis,D
DO  - 10.1016/j.jacep.2019.02.003
EP  - 586
PY  - 2019///
SN  - 2405-5018
SP  - 576
TI  - Cardiac rhythm device identification using neural networks
T2  - JACC: Clinical Electrophysiology
UR  - http://dx.doi.org/10.1016/j.jacep.2019.02.003
UR  - http://hdl.handle.net/10044/1/67610
VL  - 5
ER  -
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