AI software improves X-ray identification of pacemakers

A recent publication by a team from Imperial College, London, UK describes the development, validation, and public availability of a new neural network-based system which attempts to identify from a chest radiograph the manufacturer and even the model group of a pacemaker or defibrillator. (Howard, JP et al. Cardiac rhythm device identification using neural networks. JACC Electro Physiology, 2019; doi: 10.1016/j.jacep.2019.02.003)

The software has been able to identify the make and model of different cardiac rhythm devices, such as pacemakers and defibrillators, within seconds. Dr. J Howard, lead author of the study said: “Pacemakers and defibrillators have improved the lives of millions of patients from around the world. However, in some rare cases these devices can fail and patients can deteriorate as a result. In these situations, clinicians must quickly identify the type of device a patient has so they can provide treatment such as changing the device’s settings or replacing the leads. Unfortunately, current methods are slow and out-dated and there is a real need to find new and improved ways of identifying devices during emergency settings. Our new software could be a solution as it can identify devices accurately and instantly. This could help clinicians make the best decisions for treating patients.

More than one million people around the world undergo implantation of a cardiac rhythm device each year. These devices are placed under the patients’ skin to either help the heart’s electrical system function properly or measure heart rhythm. In emergencies, clinicians need to determine the model of a device to investigate why it has failed. Unless they have access to the records where implantation took place, staff must use a flowchart algorithm to identify pacemakers by a process of elimination. The flowchart shows a series of shapes and circuit board components of different pacemakers designed to help identify the make and model of a patient’s pacemaker. Not only is this time-consuming, but these flow charts are now outdated and therefore inaccurate. This can result in delays to delivering care to patients, who are often in a critical state. In the new study, researchers trained a neural network software program to identify more than 1,600 different cardiac devices from patients.

To use the neural network, the clinician simply uploads the X-ray image containing the device into a computer and the software reads the image, to give the result of the make and model of the device within seconds.

The team tested the program on radiographic images of more than 1,500 patients acquired at Hammersmith Hospital, London between 1998 and 2018. They then compared the results with five cardiologists who used the current standard flowchart algorithm to identify the devices.

The team found that the software outperformed the current flow-chart based methods. The software was 99 per cent accurate in identifying the manufacturer of a device, compared with only 72 percent accuracy for the flow chart. doi: 10.1016/j.jacep.2019.02.003