The accuracy of two algorithms of artificial intelligence based on
neural networks and the CaRDIA-X algorithm in the identification of
electronic implantable cardiac devices by chest x-rays.
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.