A Head-to Head Comparison of Machine Learning Algorithms for Identification of Implanted Cardiac Devices

Jay J. Chudow, Davis Jones, Michael Weinreich, Lynn Zaremski, Suegene Lee, Brian Weinreich, Andrew Krumerman, John Devens Fisher, Kevin J. Ferrick

Research output: Contribution to journalArticlepeer-review

Abstract

Application of artificial intelligence techniques in medicine has rapidly expanded in recent years. Two algorithms for identification of cardiac implantable electronic devices using chest radiography were recently developed: The PacemakerID algorithm, available as a mobile phone application (PIDa) and a web platform (PIDw) and The Pacemaker Identification with Neural Networks (PPMnn), available via web platform. In this study, we assessed the relative accuracy of these algorithms. The machine learning algorithms (PIDa, PIDw, PPMnn) were used to predict device manufacturer using chest X-rays for patients with implanted devices. Each prediction was considered correct if predicted certainty was >75%. For comparative purposes, accuracy of each prediction was compared to the result using the CARDIA-X algorithm. 500 X-rays were included from a convenience sample. Raw accuracy was PIDa 89%, PIDw 73%, PPMnn 71% and CARDIA-X 85%. In conclusion, machine learning algorithms for identification of cardiac devices are accurate at determining device manufacturer, have capacity for improved accuracy with additional training sets and can utilize simple user interfaces. These algorithms have clinical utility in limiting potential infectious exposures and facilitate rapid identification of devices as needed for device reprogramming.

Original languageEnglish (US)
Pages (from-to)77-82
Number of pages6
JournalAmerican Journal of Cardiology
Volume144
DOIs
StatePublished - Apr 1 2021

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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