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

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

doi:10.1016/j.amjcard.2020.12.067

Cited by 13 publications

(15 citation statements)

References 13 publications

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“…Despite the high level of accuracy, we demonstrated here, previous studies have demonstrated a range of accuracies of 60.5-89% with PIDa [3][4][5][6][7]. Variability is likely the result of intra-operator and intra-phone variability, with one study demonstrating significantly reduced accuracy of 80% versus 86% when the app was used off-axis [5].…”

Section: Discussioncontrasting

confidence: 61%

“…Although algorithms such as CARDIA-X exist for a cardiovascular implantable electronic device (CIED) manufacturer identification from chest x-ray imaging, they are potentially timeconsuming and operator-dependent [2]. We previously described the PacemakerID machine learning algorithm (PIDa) mobile phone application, showing that it provides equivalent to superior accuracy compared to other algorithms [3]. PIDa is available for free, requires only a mobile phone, and identifies manufacturers of both permanent pacemakers (PPMs) and implantable cardioverter-defibrillators (ICDs).…”

Section: Introductionmentioning

confidence: 99%

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Accuracy of a Single Versus Multiple Trials of Novel Pacemaker ID Algorithm Mobile Phone App for Identification of Cardiac Devices

Conant¹,

Chudow²,

Shetty³

et al. 2022

JICOA

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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.

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Section: Discussioncontrasting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Accuracy of a Single Versus Multiple Trials of Novel Pacemaker ID Algorithm Mobile Phone App for Identification of Cardiac Devices

Conant¹,

Chudow²,

Shetty³

et al. 2022

JICOA

Self Cite

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“…We observed that the diagnostic accuracy of Pacemaker-ID application was 91.7% when validated in 300 CIED X-ray images and thus comparable with the initial (94%) and subsequent (89%) accuracy estimates provided by its creators. 3 , 4 Additionally, the diagnostic accuracy of this application remained 86.3% with serial use on 3 consecutive photo acquisitions confirming its reproducibility and dropped only modestly to 80.7% when photos were acquired in a “non-standard” off-axis manner intended to emulate suboptimal “real-world” use.…”

Section: Discussionmentioning

confidence: 66%

“… 3 Further validation by its creators of this potentially helpful point-of-care (POC) diagnostic tool has indicated an accuracy of 89% when directly compared to the very complex “CaRDIA-X” radiographic chart algorithm. 4 Additionally, although manufacturer-specific X-ray logos have been incorporated into CIED model designs for almost 4 decades (see Figure 2 for examples from the 4 major manufacturers), a formal analysis of their diagnostic utility has never been provided.…”

Section: Introductionmentioning

confidence: 99%

Radiographic Identification of Cardiac Implantable Electronic Device Manufacturer: Smartphone Pacemaker-ID Application Versus X-ray Logo

Boyle¹,

Love²,

Marine³

et al. 2022

J Innov Cardiac Rhythm Manage.

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“…In 2020, these apps and the CaRDIA-X® algorithm were compared with 93% and 86% agreement, respectively (13). This information was obtained from a poster publication at the American Congress of Cardiology 2020 (ACC 2020), does not have a sample size calculation, and was performed by the app developers at a single institution.…”

Section: Discussionmentioning

confidence: 99%

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.

Córdoba

Rodriguez

García

et al. 2022

Preprint

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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.

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

Cited by 13 publications

References 13 publications

Accuracy of a Single Versus Multiple Trials of Novel Pacemaker ID Algorithm Mobile Phone App for Identification of Cardiac Devices

Accuracy of a Single Versus Multiple Trials of Novel Pacemaker ID Algorithm Mobile Phone App for Identification of Cardiac Devices

Radiographic Identification of Cardiac Implantable Electronic Device Manufacturer: Smartphone Pacemaker-ID Application Versus X-ray Logo

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.

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