| Title: |
Cardiac Rhythm Device Identification Using Neural Networks |
| Authors: |
Howard, JP; Fisher, L; Shun-Shin, MJ; Keene, D; Arnold, AD; Ahmad, Y; Cook, CM; Moon, JC; Manisty, CH; Whinnett, ZI; Cole, GD; Rueckert, D; Francis, DP |
| Source: |
JACC: Clinical Electrophysiology , 5 (5) pp. 576-586. (2019) |
| Publication Year: |
2019 |
| Collection: |
University College London: UCL Discovery |
| Subject Terms: |
Cardiac rhythm devices; machine learning; neural networks; pacemaker |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
text |
| Language: |
English |
| Relation: |
https://discovery.ucl.ac.uk/id/eprint/10074868/ |
| Availability: |
https://discovery.ucl.ac.uk/id/eprint/10074868/1/1-s2.0-S2405500X19301446-main.pdf; https://discovery.ucl.ac.uk/id/eprint/10074868/ |
| Rights: |
open |
| Accession Number: |
edsbas.350D82E0 |
| Database: |
BASE |