Abstract
Objectives : In this study, we aim to describe the diagnostic
accuracy of two applications neural networks-based system and a visual
algorithm performed by different evaluators to identify the manufacturer
of electronic implantable cardiac devices by chest x-rays.
Background : cardiac rhythm devices frequently require
interrogation, and they have different software depending on the
manufacturer. Currently, there are a visual algorithm and two
applications based on artificial intelligence for the identification of
the manufacturer from chest radiographs.
Methods : Retrospective trial between January 2010 and December
2021 at a single institution. Chest radiographs were obtained from
patients with cardiac devices; they were cropped and resized to 224 by
224 pixels. Then, they were analyzed using the applications Pacemaker
ID® with a cell phone, Pacemaker ID®web and PPMnn® web, and the visual algorithm
CaRDIA-X® performed by evaluators at different levels
of training.
Results : 400 radiographic images with cardiac devices were
collected comprising 4 manufacturers and 40 different models. The
agreement for Pacemaker ID® with a cell phone was
90.6% (p <0.001), for Pacemaker ID®web was 81.2% (p < 0.001); and for
PPMnn® web was 82% (p < 0.001). The
agreement from the CaRDIA-X® algorithm performed by 4
evaluators ranged from 73.8% to 97.7% (p < 0.001).
Conclusions : The use of applications based on neural networks
offers a good agreement in the identification of the manufacturer and is
a tool for clinical use. In our paper, the visual algorithm has a better
agreement in identifying the manufacturer and it doesn’t require much
training.
Key Word: Pacemakers; implantable cardioverter-defibrillators;
cardiac resynchronization therapy; artificial intelligence, machine
learning.