TY - JOUR
AR - JICOA-2022-3-104
TI - Accuracy of a Single Versus Multiple Trials of Novel Pacemaker ID Algorithm Mobile Phone App for Identification of Cardiac Devices
AU - Alex, Conant
AU - Jay, J Chudow
AU - Syona, S Shetty
AU - Rahul, Grover
AU - John, D Fisher
AU - Andrew, Krumerman
AU - Kevin J, Ferrick
JO - Journal of Integrative Cardiology Open Access
PY - 2022
DA - Fri 23, Sep 2022
SN - 2674-2489
DO - http://dx.doi.org/10.31487/j.JICOA.2022.03.04
UR - https://www.sciencerepository.org/accuracy-of-a-single-versus_JICOA-2022-3-104 
KW - Machine learning, cardiac devices, PacemakerID
AB - Fast and accurate identification of cardiac devices can facilitate device programming and interrogation in various medical settings. We have previously demonstrated the accuracy of the PacemakerID machine learning algorithm for mobile phone cardiac device identification. However, the questions of the reproducibility of this algorithm and whether a single trial sufficiently maximizes accuracy have yet to be answered. Here, we examine 502 chest x-rays performed at a single institution on patients with implantable cardioverter-defibrillators and permanent pacemakers. The PacemakerID mobile phone application was used for five sequential trials on each image and the accuracy of one, three, and five trials were compared. A single trial resulted in a 79% accuracy and 82% positive predictive value with no significant difference (p=0.69) as compared to five trials at identifying device manufacturers. Across all devices, the results of a single trial were not significantly different from those of five trials. Our data demonstrate that a single trial is sufficient to maximize diagnostic accuracy with the PacemakerID mobile phone application, facilitating rapid identification for prompt programming and interrogation of cardiac devices.