Author + information
- Received October 1, 2018
- Revision received January 16, 2019
- Accepted February 14, 2019
- Published online May 20, 2019.
- James P. Howard, MB BChira,∗ (, )
- Louis Fisher, BSca,
- Matthew J. Shun-Shin, BMBCha,
- Daniel Keene, BSca,
- Ahran D. Arnold, MBBSa,
- Yousif Ahmad, MBBSa,
- Christopher M. Cook, MBBSa,
- James C. Moon, PhDb,
- Charlotte H. Manisty, PhDb,
- Zach I. Whinnett, PhDa,
- Graham D. Cole, PhDa,
- Daniel Rueckert, PhDc and
- Darrel P. Francis, PhDa
- aDepartment of Cardiology, National Heart and Lung Institute, Imperial College London, London, United Kingdom
- bDepartment of Cardiology, University College London, London, United Kingdom
- cDepartment of Computing, Imperial College London; London, United Kingdom
- ↵∗Address for correspondence:
Dr. James P. Howard, National Heart and Lung Institute, Imperial College London, B Block, Hammersmith Hospital, Du Cane Road, London W12 0HS, United Kingdom.
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.
Supported by Wellcome Trust grant 212183/Z/18/Z to Dr. Howard, British Heart Foundation grants FS/15/53/31615 to Dr. Keene and FS 04/079 to Dr. Francis, and Medical Research Council grant MR/M018369/1 to Dr. Cook. Dr. Cook is a consultant for Philips Health Care. Dr. Rueckert has received research grants from and is a consultant for Heartflow. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
All authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the JACC: Clinical Electrophysiology author instructions page.
- Received October 1, 2018.
- Revision received January 16, 2019.
- Accepted February 14, 2019.
- 2019 The Authors